Review Special Issues

Cassava cultivation; current and potential use of agroindustrial co–products

  • Cassava (Manihot esculenta Crantz) has garnered global attention due to its importance as a crucial raw material for ethanol and other derivative production. Nonetheless, its agroindustry generates a substantial amount of residues. We examined the potential utilization of co–products from both agricultural and industrial sectors concerning starch extraction processes. A total of 319 million tons of fresh cassava roots are globally produced, yielding up to 55% of agricultural co–products during harvesting. For every ton of starch extracted, 2.5 tons of bagasse, along with 100 to 300 kg of peel per ton of fresh processed cassava, and 17.4 m3 of residual liquid tributaries are generated. Consequently, both solid agricultural biomass and solid/liquid residues could be directed towards cogenerating bioenergy such as bioethanol, biobutanol, biodiesel, bio–oil, charcoal, and other bioproducts. In conclusion, the conversion of cassava agroindustrial co–products into food and non–food products with high added value could be promoted, thus fostering a circular economy to enhance profitability, sustainability, and crop promotion.

    Citation: Pablo Andrés–Meza, Noé Aguilar–Rivera, Isaac Meneses–Márquez, José Luis Del Rosario–Arellano, Gloria Ivette Bolio–López, Otto Raúl Leyva–Ovalle. Cassava cultivation; current and potential use of agroindustrial co–products[J]. AIMS Environmental Science, 2024, 11(2): 248-278. doi: 10.3934/environsci.2024012

    Related Papers:

    [1] Fukui Li, Hui Xu, Feng Qiu . Correction: Modified artificial rabbits optimization combined with bottlenose dolphin optimizer in feature selection of network intrusion detection. Electronic Research Archive, 2024, 32(7): 4515-4516. doi: 10.3934/era.2024204
    [2] Feng Qiu, Hui Xu, Fukui Li . Applying modified golden jackal optimization to intrusion detection for Software-Defined Networking. Electronic Research Archive, 2024, 32(1): 418-444. doi: 10.3934/era.2024021
    [3] Rajakumar Ramalingam, Saleena B, Shakila Basheer, Prakash Balasubramanian, Mamoon Rashid, Gitanjali Jayaraman . EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks. Electronic Research Archive, 2023, 31(6): 3123-3144. doi: 10.3934/era.2023158
    [4] Hui Xu, Longtan Bai, Wei Huang . An optimization-inspired intrusion detection model for software-defined networking. Electronic Research Archive, 2025, 33(1): 231-254. doi: 10.3934/era.2025012
    [5] Mohd. Rehan Ghazi, N. S. Raghava . Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms. Electronic Research Archive, 2024, 32(2): 1268-1307. doi: 10.3934/era.2024060
    [6] Ilyоs Abdullaev, Natalia Prodanova, Mohammed Altaf Ahmed, E. Laxmi Lydia, Bhanu Shrestha, Gyanendra Prasad Joshi, Woong Cho . Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries. Electronic Research Archive, 2023, 31(8): 4443-4458. doi: 10.3934/era.2023227
    [7] Zhenzhong Xu, Xu Chen, Linchao Yang, Jiangtao Xu, Shenghan Zhou . Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings. Electronic Research Archive, 2024, 32(6): 4074-4095. doi: 10.3934/era.2024183
    [8] Eray Önler . Feature fusion based artificial neural network model for disease detection of bean leaves. Electronic Research Archive, 2023, 31(5): 2409-2427. doi: 10.3934/era.2023122
    [9] Sahar Badri . HO-CER: Hybrid-optimization-based convolutional ensemble random forest for data security in healthcare applications using blockchain technology. Electronic Research Archive, 2023, 31(9): 5466-5484. doi: 10.3934/era.2023278
    [10] Jianjun Huang, Xuhong Huang, Ronghao Kang, Zhihong Chen, Junhan Peng . Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks. Electronic Research Archive, 2024, 32(9): 5249-5267. doi: 10.3934/era.2024242
  • Cassava (Manihot esculenta Crantz) has garnered global attention due to its importance as a crucial raw material for ethanol and other derivative production. Nonetheless, its agroindustry generates a substantial amount of residues. We examined the potential utilization of co–products from both agricultural and industrial sectors concerning starch extraction processes. A total of 319 million tons of fresh cassava roots are globally produced, yielding up to 55% of agricultural co–products during harvesting. For every ton of starch extracted, 2.5 tons of bagasse, along with 100 to 300 kg of peel per ton of fresh processed cassava, and 17.4 m3 of residual liquid tributaries are generated. Consequently, both solid agricultural biomass and solid/liquid residues could be directed towards cogenerating bioenergy such as bioethanol, biobutanol, biodiesel, bio–oil, charcoal, and other bioproducts. In conclusion, the conversion of cassava agroindustrial co–products into food and non–food products with high added value could be promoted, thus fostering a circular economy to enhance profitability, sustainability, and crop promotion.



    Undoubtedly, the current era of networking is characterized by novelty. The rapid expansion of networks is giving rise to an unprecedented volume of data, contributing to heightened complexity in terms of data dimensions and features. Within this extensive dataset, when there is a need to analyze and detect specific data, the presence of numerous non-essential redundant features emerges. This proliferation of redundant features intensifies the challenges in network intrusion detection, akin to a pathological condition. As a consequence, a pivotal strategy for enhancing the efficacy and performance of network intrusion detection involves the elimination of duplicate characteristics. Network intrusion detection detects attack patterns by analyzing network traffic, with machine learning algorithms such as artificial neural networks, naive Bayes, and decision trees being predominantly utilized in current practices. Traditional machine learning methods are particularly valuable for solving small-scale data and simple tasks, offering better interpretability. Novel deep learning methods exhibit superior performance in handling large-scale data and complex tasks, albeit requiring more computational resources. Given the immense volume of current network traffic, attempting to identify the most suitable features through a systematic search is generally impractical due to the limitations of direct computation in practice. Although evaluating all possible subsets is costly in practice, the emergence of intelligent optimization algorithms provides a solution. Intelligent optimization algorithms are classified into four categories: evolution-based algorithms, swarm intelligence-based algorithms, physics-based algorithms, and human behavior-related algorithms. These algorithms can approach the optimal solution of a problem, are simple to implement, and exhibit high flexibility. The algorithms can be modified depending on the requirements of the problem to efficiently search the space and avoid falling into local optimum. Hence, many feature selection methods utilize intelligent optimization algorithms to mitigate increasing computational complexity, handle invalid or duplicate features, and aid in analyzing data behavior, thereby reducing computational and storage costs [1,2,3,4].

    In this context, numerous studies have been conducted, yielding a plethora of viable solutions. These include traditional feature selection methods (such as relevance feature selection and information gain) and population intelligence optimization algorithms (e.g., Harris hawk optimization (HHO) [5], particle swarm optimization (PSO) [6], gray wolf optimization (GWO) [7], and whale optimization algorithm (WOA) [8]), all aimed at addressing the feature problem in network intrusion detection.

    Despite the establishment of a substantial number of intelligent optimization algorithms for handling feature selection in network intrusion detection, there remains an optimization space in the selection of these algorithms. While there are numerous outstanding options available, their outcomes are not perfect [9].

    Artificial rabbits optimization (ARO) [10], introduced by L. Wang et al. in 2022, is a novel intelligent optimization algorithm. Its robust optimality-seeking ability makes it particularly well-suited for addressing the feature selection challenges in network intrusion detection. When handling large-dimensional data, ARO is prone to settling into the local optimum, leading to unsatisfactory results. The bottlenose dolphin aptimizer (BDO) [11], introduced by A. Srivastava et al. in 2022, stands out for its strong pre-probing ability and remarkable convergence speed.

    Given the exceptional performance of ARO, scholars have extended its applicability to practical scenarios. To extend network life by reducing energy consumption rates, R. Ramalingam et al. integrated ARO with WSNs, designing the energy efficient cluster formation based on the ARO [12]. Y. Wang et al. synergized the aquila optimizer (AO) with ARO, utilizing the hybrid algorithm to address five industrial engineering design problems and photovoltaic model parameter identification challenges [13]. Additionally, the ARO, introduced by D. Dangi et al., plays a crucial role in enhancing the performance of robust random vector functional link networks (RRVFLN) by efficiently mitigating hidden layer bias and optimizing the input weights of the RRVFLN model [14].

    Furthermore, the research in network traffic intrusion detection aims to enhance detection capabilities, striving for both strength and speed to yield superior results in practical applications [15]. Notably, H. Alazzam et al. introduced a feature selection method designed for an IDS. The suggested method efficiently reduces the number of features required to construct a robust IDS, preserving a high level of accuracy. Moreover, the proposed cosine similarity method exhibits superior convergence speed compared to the standard sigmoid method [16]. Q. M. Alzubi et al. introduced a novel IDS utilizing an enhanced hybrid algorithm that combines binary GWO and PSO. The system efficiently employs a support vector machine for dataset classification and experimentally evaluates the significant enhancement in intrusion detection accuracy using the NSL-KDD dataset [17]. A. Alzaqebah et al. employed a modified GWO, incorporating filter and wrapper approaches during the initialization phase. The parameters of the extreme learning machine are subsequently fine-tuned using the enhanced GWO. The final proposed model can minimize data dimensions and eliminate irrelevant and noisy data, effectively enhancing the performance of the IDS [18]. M. Injadat et al. proposed a multi-level optimization NIDS framework based on machine learning. The framework utilizes oversampling techniques to determine the minimum suitable training sample size, investigates the impact of various feature selection techniques, and employs hyperparameter optimization to enhance performance. Final experiments demonstrate that the framework effectively reduces computational complexity while maintaining detection performance [19]. J. Lee et al. proposed a deep sparse autoencoder (DASE) for extracting and compressing important features, which was then combined with random forest (RF) to form the DASE-RF model. Experimental comparisons demonstrate that the model significantly enhances both detection speed and performance [20]. D. Mauro et al. focus on feature selection in machine learning for network intrusion detection. The article introduces and investigates various feature selection algorithms and datasets, validated using a correlation-based feature selector as the objective function. A comprehensive analysis demonstrates that reducing redundant features is practically lossless for feature selection, leading to accelerated training processes and enhanced detection speed [21].

    Y. Li et al. introduced a hybrid intrusion detection method that incorporates adaptive synthesis and a decision tree based on the ID3. The approach involves employing multiple criteria and comparing various models. Experimental results suggest that this method effectively increases the intrusion detection rate [22]. T. Wang et al. introduced a multi-label feature selection method utilizing the Hilbert-Schmidt independence criterion (HSIC) and the sparrow search algorithm. This method aims to identify optimal features by capturing dependencies between features and all labels, employing HSIC as a feature selection criterion. The proposed method demonstrates some effectiveness [23]. A. Dahou et al. utilized the reptile search algorithm (RSA) to enhance the IDS in the context of Internet of Things (IoT) environment data. In this approach, the CNN model is employed to filter the optimal subset of features, effectively boosting the performance of the detection system [24]. M. Imran et al. proposed a novel approach for anomaly detection that involves optimizing an artificial neural network with a cuckoo search algorithm. The NSL-KDD dataset was employed for real data simulation, and the experiments were assessed through a multi-algorithm comparison to achieve optimal results [25].

    Next, we present the prior work done by our group. Initially, our team proposed an enhanced butterfly optimization algorithm combined with black widow optimization. The experimental dataset was selected from the UNSW-NB15 dataset, and the results demonstrated that the proposed approach significantly enhances performance while successfully minimizing feature dimensions in the context of feature selection for network intrusion detection [26]. Then, our group integrated the classification optimization results of weighted K-nearest neighbor (KNN) with the outcomes of the feature selection algorithm. We proposed a combination strategy of feature selection and weighted KNN based on the integrated optimization algorithm. Experiments demonstrated that this proposed strategy significantly enhances the efficiency and accuracy of network intrusion detection [27]. Finally, our group introduced a jumping spider optimization approach, combining the HHO with the tiny hole imaging algorithm (HHJSOA). The experimental section verified the classification accuracy and performance of the HHJSOA using both the UNSW-NB15 dataset and the KDD99 dataset. The experimental findings revealed that it can significantly enhance the classification effect and address performance issues in feature selection applications [28]. Furthermore, our team proposed a modified version of the golden jackal optimization (mGJO), which combines two strategies and applies them to intrusion detection in software-defined networks (SDN). Our experiments utilized the novel InSDN dataset, resulting in improved performance across various classification metrics and feature selection [29].

    Building upon the studies and considerations mentioned above, this paper introduces LBARO, a hybrid algorithm that combines BDO and ARO. Additionally, four strategies are incorporated to collaboratively enhance the original algorithm. Subsequently, the LBARO is employed in the feature selection of network intrusion detection, facilitating the construction of a robust network intrusion detection model. The experiments involve a range of network intrusion detection datasets, along with recent superior algorithms and traditional classical algorithms, for comparative testing and evaluation. The aim is to verify the effectiveness and excellence of the LBARO. The main contributions of this paper are as follows.

    1) A novel feature selection model for network intrusion detection is proposed. Four main modules exist in this model. This model is used to solve the feature redundancy problem of the intrusion detection dataset, reduce the feature dimension, and enhance the intrusion detection efficiency and accuracy.

    2) In this paper, four strategies are used to synergistically modify ARO. The mud ring feeding strategy helps to enhance the exploration rate. The adaptive switching strategy effectively balances the combined algorithm. The levy flight strategy can provide larger strides to escape from the local optimum. The dynamic lens-imaging learning strategy enhances population richness. The benchmarking function is used to test the performance of LBARO by comparing it with other algorithms.

    3) In this paper, the feature selection model incorporating LBARO is proposed, using a binary version of LBARO to search for the optimal subset of features. The experiments are conducted using four UCI datasets (the NSL-KDD dataset, the UNSWNB-15 dataset, and the InSDN dataset) to test the superiority of the proposed model in this paper by comparing the models combined with other algorithms.

    The ARO is primarily proposed by referencing two survival laws observed in the natural world: meandering foraging and random hiding of rabbits. Specifically, meandering foraging serves as an exploration strategy preventing rabbits from being detected by natural predators, allowing them to graze near their nests. Random hiding is another strategy in which rabbits move to other burrows to hide further away.

    In detour foraging (exploration) within the ARO, it is assumed that each rabbit in the population has its own area with some grass and burrows. During foraging activities, rabbits tend to randomly move far away from other individuals in search of food and ignore nearby food. This behavior is known as meandering foraging, and its mathematical model is expressed as

    Xi(t+1)=Xj(t)+K×(Xi(t)Xj(t))+round×(0.5×0.05+r1)×n1i,j=1,...,N and ijK=l×c  (1)
    l=(ee(t1Tmax)2)×sin(2πr2) (2)
    c(k)={1 if k==G(l) 0 elselk=1,...,D and l=1,...,r3×D (3)
    g=randperm(D) (4)
    n1N(0,1) (5)

    where Xt+1i represents the potential location of the ith rabbit in iteration t + 1; the rabbits' positions in the current iteration t are shown by the symbols Xti and Xtj, respectively; N represents the population's size; Tmax is the maximum number of iterations, while t is the current iteration; the size of the dimensions is indicated by d; the randomly selected integer between 1 and D is indicated by g; three random values in the interval [0, 1] are r1, r2, and r3; n1 has a typical normal distribution; and L is the distance covered by a step in a meandering foraging performance.

    The energy factor F gradually decreases to maintain a satisfactory equilibrium between exploration and exploitation. The mathematical model for this is expressed as

    F(t)=4×(1tTmax)×ln1r6 (6)

    where r6 is an arbitrary number in the range of 0–1 and the value of the energy factor F fluctuates between 0 and 2. The search mechanism based on the energy factor F is depicted in Figure 1.

    Figure 1.  Search mechanism based on the energy factor F.

    Facing chases and attacks from predators is the norm for rabbits. To survive, they dig various holes around their nests as shelters. In each iteration, rabbits always generate burrows along the dimension of the search space and then choose one of them randomly to hide, reducing the probability of being captured. The mathematical model is simulated as follows:

    Xi(t+1)=Xj(t)+K×(r4×bi,r(t)Xi(t) ) (7)
    bi,r(t)=Xi(t)+H×gr(k)×Xi(t) (8)
    gr(k)={1 if k==r5×D 0 otherwise (9)
    H=Tmaxt+1Tmax×n2 (10)
    n2N(0,1) (11)

    where the parameter K can be calculated using Eqs (2)–(4), bi,rt denotes the burrow of the ith rabbit randomly selected among the D burrows utilized for hiding in the current iteration t, r4 and r5 are two arbitrary values in the range of 0–1, and n2 has a normal distribution.

    The hunting technique of the bottlenose dolphin, which mimics the mud ring feeding strategy, serves as the inspiration for the BDO. Dolphins utilize a special hunting tactic called mud ring feeding to both feed and trap fish. Dolphins that live in groups collaborate to find prey early in the hunt. Driver dolphins will guide the population in team hunts to surround the shoal of fish. During the encirclement, the dolphins move their tails along the sand so that they form a plume. The purpose of the plume, which resembles a fishing net, is that the fish become disorientated. At the same time, fish trapped in the plume attempt to jump out of the plume. Due to the jumping behavior of the fish, other members of the dolphin population will surround the position of the plume and capture any fish that reach the plume position. To increase the efficiency of the attack, the dolphins reduce the encirclement. Eventually, as the dolphins approach the location of the captured fish, more fish will jump out to be hunted by the dolphins. During this hunt, other dolphins in the group also generate plumes simultaneously for hunting, enhancing search efficiency.

    In this study, the defects of the ARO are modified from the perspective of synergy, which can make the ARO effective in enhancing the convergence speed, escaping the local optimum and stability. The strategies utilized to modify the performance of the ARO include the mud ring feeding strategy, adaptive switching mechanism strategy, levy flight strategy, and dynamic lens-imaging learning strategy, which utilize the complementary properties of these four strategies to synergistically optimize the original algorithm in all aspects to maximize the gains achieved, as shown in Figure 2.

    Figure 2.  Modified approach diagram of the ARO.

    Initially, a faster search is conducted for the global exploration phase by incorporating the mud ring feeding strategy of the BDO. Then, to better balance the LBARO, the adaptive switching mechanism is introduced to better equilibrate and guide individual search directions. Next, the levy flight strategy is introduced in the local exploitation phase by utilizing the resulting perturbations for variational updates on the original algorithmic positions. Lastly, for better escaping from the local optimum, the dynamic lens-imaging learning strategy is introduced to enhance the exploitation capability.

    In addition to sluggish convergence and low population diversity in the early exploration phase of the ARO, the detour foraging mechanism lacks the ability to produce enough volatility so that it allows the search agent to completely explore the whole field of search. The mud ring feeding strategy of the BDO is then added to the exploitation phase aimed at enhancing the original algorithm constraints and obtaining superior overall optimization performance. The dolphins collaborate to locate their prey during the exploration phase. The driving dolphin begins to circle the prey area as soon as it has been identified. The movement of the driver dolphin towards the prey location. It is assumed that the current position of the driver dolphin represents the location of the prey. During the search process, this position is searched for a better solution. Therefore, the mud ring feeding strategy of the BDO has excellent exploration ability and fast contraction speed, which can effectively make up for the shortcomings of the ARO [30]. The mathematical model for this is expressed as

    Xt+1DD=XtDD+XtDD×rand×eθ×cos(2π×θ(t)) (12)
    θ(t)=1(1θmin)×tTmax (13)
    Xt+1FD=XtFD+af×rand×(XtDDXtFD) (14)

    where Xt+1DD denotes the updated position of the driver dolphin, XtDD denotes the position of the driver dolphin, rand is a random number between [-1, 1], which helps to spread out the search capability, and θ is a constant that aids in encircling the place during the search by haphazardly decreasing. Xt+1FD denotes the updated position of the follower dolphin, XtFD denotes the current position of the follower dolphin, denotes the position of the driver dolphin, and af is an acceleration factor that accelerates the movement of the follower dolphin towards the driver dolphin.

    The meandering foraging strategy of the ARO can still provide some guarantee for the survival of the rabbits, although it suffers from the problems of poor volatility and slow convergence to a certain extent. The mud ring feeding strategy introduced is not a direct replacement for the detour foraging strategy. To further optimize the balance between the two, an extra parameter that directs the search direction must be added to the combined algorithm. Therefore, this paper introduced an adaptive switching mechanism of one kind [31]. The mathematical model for this is expressed as

    E=2E0E1 (15)
    E1=1tTmax (16)

    where E0 is a random value that ranges between -1 and 1, E1 is a control parameter that decreases linearly, t is the current iteration number, and Tmax is the maximum iteration number. The global exploration phase of the algorithm occurs when |E| ≥ 1, while the local exploitation phase occurs when |E| < 1. E1 drops consistently to improve the balance between the phases of exploration and exploitation.

    When rabbits face predators, they will use the holes dug around the nest as hiding places out of the need for survival. At this point, the random number r4 utilized to generate the perturbation can, to some extent, provide a small range of changes in the location of the update mutation so that the rabbit's choice of hiding place has a certain degree of randomness. However, as the ARO iterates, the fluctuation of random numbers shows relatively weak performance and may not provide sufficient leaps when facing the local optimum. Consequently, the ARO might lead to the capture of the rabbit, resulting in getting trapped in the local optimum. Thus, at this point, the levy flight strategy can generate random numbers with larger spans, providing more variables for replacing the random number r4 [32]. Since the stochastic hiding phase is the exploitation phase, levy flight enhances the spatial search capability and the ability of the ARO to escape from the local optimum. This effectively searches for the global optimal solution in the iterations of the ARO [33]. The mathematical model for this is expressed as

    Xi(t+1)=Xj(t)+K×(α×levy(β)×bi,r(t)Xi(t) ),i=1,...,N (17)
    levy(β)=u×v|v(1+γ)| (18)
    u(0,σu2),v(0,σv2) (19)
    σu=(Γ(1+γ)×sin(π×γ2)Γ(1+γ)×γ×2γ12)1γ,σv=1 (20)

    where α is fixed at 0.15; u and v follow Gaussian distributions with mean 0 and variances σu2 and σv2, respectively; The conventional gamma function is represented by Γ; and the correlation parameter, which is set to 1.5, is represented by γ.

    In this paper, the levy flight strategy is adopted to disturb the position update to enhance the exploitation ability of the ARO locally and to enhance the rabbit's chance of survival. Nevertheless, if one only uses the levy flight strategy, the goal of preventing the ARO from reaching the local optimum is defeated by a probabilistic solution. Therefore, the dynamic lens-imaging learning strategy is introduced after each algorithm iteration. It improves the local optimal ability of the ARO and prevents sliding into iterative stagnation. The survival potential of the rabbits has been effectively boosted. The dynamic lens-imaging learning strategy has been recently proposed [34,35]. It is derived from the opposition-based learning method. This strategy derives the law of convex lens imaging from the law of optics. It is based on the principle of refracting a solid from one side to the other through a convex lens to generate an inverted image.

    X=ub+lb2+ub+lb2×φXφ (21)

    where ub and lb are the upper and lower bounds, respectively, and X and X are the individual and its opposing individual, called the scale factor, respectively. The scaling factor φ improves the local exploitation of the original algorithm. The scaling factor is typically regarded as a constant in the original lens imaging learning strategy, which reduces the convergence performance of the original algorithm. Therefore, a new nonlinear dynamically decreasing scale factor based on nonlinear dynamics is introduced, which allows for larger values to be obtained in the early iterations of the modified algorithm. Thus, the modified algorithm is able to search in a wider range of different dimensional regions and enhance the diversity of the population. Smaller values are obtained towards the end of the modified algorithm iterations, enabling a refined search in the proximity of optimal individuals to further enhance the resolution of the local optimum.

    φ=ζmin(ζmaxζmin)×(tTmax)2 (22)

    As the population is more likely to fall into the local optimum during the exploitation phase, the dynamic lens-imaging learning strategy was adopted for the iterated population of the LBARO. In each iteration, the positions of the population are randomly altered based on both the total number of individuals in the current population and the fitness of the best solution, which is computed and maintained. This is done to further enhance population variety and prevent local optimum.

    The LBARO is constructed based on the ARO and consists of four main components. First, by combining the mud ring feeding strategy with ARO, which takes advantage of the rapid convergence rate of strategy in updating the position, the global exploration capability is improved. The introduction of an adaptive switching mechanism facilitates the adjustment between the exploration and exploitation phases. To prevent subsequently falling into the local optimum, the levy flight strategy is also implemented during the local exploitation phase of the ARO. Lastly, the dynamic lens-imaging learning strategy is presented to provide better positional variability while also improving population variety and stochastically optimizing the population. This helps the population avoid stagnating in the local optimum. The execution phases of the LBARO are displayed below. The flowchart of LBARO is depicted in Figure 3, and its pseudo-code description is provided in Algorithm 1.

    Figure 3.  Flow chart of the LBARO.

    Algorithm 1. Pseudo-code of the LBARO
    1. Initialize the population size N, the maximum iterations Tmax, the dimension D, Initialize the position of each search agent Xi
    2. Calculate the fitness Fiti and Xbest is the best solution found so far
    3. While tTmax
    4.  For each Xi
    5.  Calculate the factor E using Eq (16) //Adaptive switching mechanism strategy
    6.  Calculate the energy factor F using Eq (7)
    7.   If |E| ≥ 1 then
    8.    Updates the position of search agent using Eqs (13)–(15) //Mud ring feeding strategy
    9.  Else
    10.    If |F| ≥ 1 then
    11.     Updated the position of search agent using Eqs (8)–(12)
    12.    Else
    13.     Updated the position of search agent using Eqs (18)–(21) //Levy flight strategy
    14.    End If
    15.  End If
    16. Updated the position of search agent using Eq (22) //Dynamic lens-imaging learning strategy
    17. End For
    18: End While
    19. Return Xbest

    The time complexity can effectively measure the running efficiency of the algorithm. Time complexity is undoubtedly one of the very important performance metrics. An in-depth discussion of time complexity can provide a better understanding of the performance characteristics of algorithms and provide guidance for practical applications. The excellence of an algorithm depends not only on the quality of individual metrics but also on whether the complexity of the algorithm has increased. In the feature selection of network intrusion detection, an excess of redundant features can decrease detection efficiency, while the algorithm's operational speed also impacts the overall system performance. Therefore, one of the requirements for enhancing the algorithm is to minimize increases in complexity while building upon the original algorithm. According to the pseudo-code in Algorithm 1, the overall time complexity is determined by the population size (N), the maximum number of iterations (T), and the dimensionality (D). The time complexity of ARO can be expressed as O(1 + N + T × N +× T × N × D +× T × N × D), which is O(N + NT + NDT). During the initialization phase of LBARO, the rabbit locations are randomly generated, requiring a time complexity of O(N). Throughout the iterative phase of LBARO, the time complexity of evaluating the rabbit's frontal fitness and updating its position is O(N × T + N × D × T). Hence, the time complexity of LBARO remains O(N + NT + NDT), indicating no increase compared to ARO.

    For network intrusion detection, the corresponding feature selection model was constructed with the LBARO, as illustrated in Figure 4. Feature selection of network intrusion detection model based on the LBARO can be divided into four core modules according to their functional roles: the data acquisition module, the data pre-processing module, the feature selection module, and the model evaluation module [36,37,38,39].

    Figure 4.  Feature selection model based on LBARO.

    1) Data acquisition module

    The rapid development of the Internet era results in a substantial and cumbersome redundancy of network data, necessitating its analysis. Therefore, it is necessary to collect the network reality traffic data through relevant tools. To generate a dataset for further analysis of the data, the network data collection component primarily gathers the network data packets that the host obtains from the network. In the study, four datasets (UCI, NSL-KDD, UNSW-NB 15, and InSDN) are utilized as simulations of realistic network data.

    2) Data pre-processing module

    The data collected in the actual network is generally dirty data, and there are usually problems such as missing numbers, data noise, data inconsistency, data redundancy, unbalanced data sets, outliers, and data duplication. Therefore, before using the data, effective data cleaning must be carried out.

    The first phase involves cleaning the data, which includes identifying and eliminating anomalous data, handling missing or incorrect data, and getting rid of duplicate data. The data is consistently classified as numerical in the second stage. This is done to prevent the occurrence of later experimental input value format inconsistency by transforming the character type or other types of data using label coding. The third step of the normalization process is carried out, utilizing the normalization function to process the data to tackle the problem of the substantial disparities in the dimensions of the attributes of the dataset species utilized in this work. The procedure maps all the data values into the [0, 1] interval, which can achieve the aim of converting the un-normalized data into normalized data to increase the accuracy of feature selection.

    3) Feature selection module

    After the dataset is crawled from the web and undergoes data cleaning, simple data filtering has been performed to some extent. There will still be an overwhelming number of redundant features that are invisible to the unaided eye, though, because network data is typically very vast. These characteristics greatly increase the complexity of detecting network intrusions, decreasing the rate of detection and taking an unnecessary amount of time. Thus, the existence of feature selection provides further processing of the dataset before intrusion detection. This effectively reduces redundant features, reduces the amount of data, and improves the detection correctness.

    Next, the preprocessed dataset undergoes an iterative optimization search conducted by an intelligent optimization algorithm. The population of rabbits forages and avoids obstacles in search of a better place with each generation. When the iteration concludes, the algorithm obtains where the current ideal location exists, which is the index of the optimal subset. At this point, the module obtains the optimal feature subset selection to achieve the aim of de-redundant feature subsets and data dimensionality reduction.

    4) Model evaluation module

    Evaluating the classifiers means estimating the average degree of correctness of the classifiers' decisions at the time of prediction. Common classifiers are SVM classifier, KNN classifier, K-means classifier, and plain Bayesian classifier. Therefore, it is necessary to select or design the classification effect evaluation metrics according to the characteristics of the scene. In the paper, a suitable KNN classifier is utilized for evaluation.

    Following the feature selection by the algorithm, the dataset is obtained concerning dimensionality reduction. At this point, the KNN classifier is invoked and the dataset of the optimal feature subset optimized by LBARO is provided as an input parameter to the classifier. After the prediction by the classifier, the data relevant to the classification is collected. Finally, to evaluate the overall model performance, metrics such as accuracy, recall, precision, and F1-score are employed, all of which are commonly utilized to assess classification effectiveness.

    The experimental tests were carried out in a single setting to guarantee the objectivity and fairness of experiments. The Intel Core i5-12490F CPU@3.00 GHz processor type, the Windows 11 operating system, and the MATLAB 2022b programming language are all utilized in the experimental setup.

    For this experiment, eight common benchmark test functions were selected, comprising four single-peak functions (f1–f4) and four multi-peak functions (f5–f8), chosen with moderate concentrations [40]. The experiment involves a degree of randomness. The test functions in the experiment were run independently multiple times. The benchmark test functions are depicted in Table 1.

    Table 1.  Benchmark function expressions.
    Function expressions Dimension Range fmin
    f1(x)=ni=1x2i 30 [-100,100] 0
    f2(x)=ni=1|xi|+ni=1|xi| 30 [-10, 10] 0
    f3(x)=ni=1(ij1xj)2 30 [-100,100] 0
    f4(x)=maxi{|xi|,1in} 30 [-32, 32] 0
    f5(x)=ni=1xisin(|xi|) 30 [-500,500] -418.9829 × D
    f6(x)=20exp(0.21nni=1x2i)exp(1nni=1cos(2πxi))+20+e 30 [-32, 32] 0
    f7(x)=πn{10sin(πyi)+n1i=1(yi1)2[1+10sin2(πyi+1)]+(yn1)2}+ni=1u(xi,10,100,4)yi=1+xi+14u(xi,a,k,m)={k(xi1)m,xi>a0,a<xi<ak(xia)m,xi<a} 30 [-50, 50] 0
    f8(x)=7i=1[(Xai)(Xai)T+ci]1 4 [0, 10] -10.5363

     | Show Table
    DownLoad: CSV

    Table 2 provides the parameters of each algorithm. The average optimal value, average worst value, average value, and standard deviation of the four algorithms, AO, GWO, ARO, and LBARO, are calculated independently and run thirty times on single-peak and multi-peak test functions.

    Table 2.  Parameterization.
    Algorithms Parameter values
    AO α = 0.1, δ = 0.1
    PSO c1 = 2, c2 = 2
    ARO
    LBARO d1 = 100, d2 = 10, af = 3.5

     | Show Table
    DownLoad: CSV

    The fitness graph in Figure 5 illustrates how the LBARO converges more rapidly and accurately than alternative algorithms. Further evidence of LBARO's superior stability and results is presented in Table 3. This demonstrates that the LBARO can balance exploration and development while achieving a faster and more accurate convergence rate throughout the global exploration stage. It can enhance the population richness in the local exploitation phase and effectively avoid falling into the local optimum. As a result, the LBARO has better ability and more robustness under the iterative optimization of the same algorithm.

    Figure 5.  Convergence curves of fitness.
    Table 3.  Benchmark function results.
    Function Algorithms Min Max Ave Std
    F1 AO 1.79E-158 1.32E-103 4.44E-105 2.41E-104
    PSO 9.79E-01 4.36E+00 2.58E+00 8.57E-01
    ARO 6.75E-70 1.93E-55 7.85E-57 3.62E-56
    LBARO 0.00E+00 0.00E+00 0.00E+00 0.00E+00
    F2 AO 4.47E-84 2.49E-67 8.30E-69 4.55E-68
    PSO 2.21E+00 8.09E+00 4.41E+00 1.49E+00
    ARO 6.14E-39 5.16E-29 1.73E-30 9.41E-30
    LBARO 0.00E+00 1.76E-188 5.87E-190 0.00E+00
    F3 AO 1.53E-155 1.74E-102 6.53E-104 3.18E-103
    PSO 9.91E+01 2.83E+02 1.86E+02 5.00E+01
    ARO 2.63E-55 1.62E-42 1.25E-43 4.05E-43
    LBARO 0.00E+00 0.00E+00 0.00E+00 0.00E+00
    F4 AO 1.09E-80 9.30E-53 3.10E-54 1.70E-53
    PSO 1.52E+00 2.36E+00 2.00E+00 2.00E-01
    ARO 2.16E-29 8.77E-23 6.05E-24 1.98E-23
    LBARO 6.15E-143 4.03E-110 1.34E-111 7.36E-111
    F5 AO -4.03E+03 -2.76E+03 -3.35E+03 2.84E+02
    GWO -8.66E+03 -3.30E+03 -6.12E+03 1.26E+03
    ARO -1.02E+04 -8.53E+03 -9.28E+03 4.36E+02
    LBARO -1.24E+04 -9.02E+03 -1.05E+04 1.09E+03
    F6 AO 4.44E-16 4.44E-16 4.44E-16 0.00E+00
    GWO 1.66E+00 3.44E+00 2.66E+00 4.72E-01
    ARO 4.44E-16 4.44E-16 4.44E-16 0.00E+00
    LBARO 4.44E-16 4.44E-16 4.44E-16 0.00E+00
    F7 AO 7.35E-09 1.80E-05 3.12E-06 5.13E-06
    GWO 7.97E-03 4.19E-01 6.22E-02 7.89E-02
    ARO 1.18E-05 2.13E-04 5.63E-05 4.11E-05
    LBARO 1.57E-32 1.57E-32 1.57E-32 5.57E-48
    F8 AO -1.04E+01 -1.03E+01 -1.04E+01 2.45E-02
    PSO -1.04E+01 -2.75E+00 -8.47E+00 2.96E+00
    ARO -1.04E+01 -2.77E+00 -9.39E+00 2.33E+00
    LBARO -1.04E+01 -1.04E+01 -1.04E+01 0.00E+00

     | Show Table
    DownLoad: CSV

    Combining feature selection with intelligent optimization algorithms is a superior approach because of the advancement and growth of these algorithms in recent years, which has increased their effectiveness. The performance of the feature subset is significantly affected by the number of selected features and the classification error rate. The evaluation function in question is displayed as follows:

    Fitness=α×Er+β×|Se|×Fe, (23)

    where Er is the classification error rate of the specified classifier, Fitness is the ideal value of a workable solution as represented by a single member of the population, Se is the quantity of chosen feature subsets, Fe represents the total features, α and β denote the two weights, with α set to 0.99 and β to 0.01.

    This work presents the introduction of the sigmoid function to LBARO, enabling its conversion to binary LBARO for discrete situations. The binary version of the LBARO is utilized in this study for feature selection. The population members act as the seeking agents, and the locations of agents are obtained through the iterations of the algorithm. The population is transformed into a binary encoded population with individuals described as Xid, where d is the feature dimension, via the conversion function. The characteristics of this workable solution are chosen when Xid = 1. Otherwise, it is not chosen [41]. The following formula displays its transformation function:

    Sigm(Xid)=11+eXid,Xid={1,rand()Sigm(Xid)0,rand()<Sigm(Xid)}. (24)

    In the experimental context, the experimental algorithms are AO, GWO, ARO, and LBARO, and different datasets will be generated to examine the overall performance of the respective models. The experiment selects a variety of datasets, offering varied test conditions and data as much as feasible. It can verify the ability of redundant features and inspect the perfection of the overall model performance. The experimental settings were set: the K-fold cross-validation multiplier was 10, the population size N = 30, and the number of iterations Tmax = 50.

    Table 4.  Parameterization.
    Parameters Numbers
    K-fold cross-validation multiplier 10
    Population size 30
    Maximum iterations 50

     | Show Table
    DownLoad: CSV

    The UCI dataset is employed to evaluate the efficacy of the LBARO in dimensionality reduction [42]. Four UCI datasets are selected as test objects, and their complete information is provided in Table 5. Table 6 indicates the experimental outcomes of UCI datasets.

    Table 5.  UCI dataset.
    Number Dataset name Sample size Number of features
    1 Ionosphere 351 34
    2 Vehicle 846 18
    3 Heatstatlog 270 13
    4 Sonar 208 61

     | Show Table
    DownLoad: CSV
    Table 6.  Test results of the UCI.
    Algorithms Metrics Ionosphere Vehicle Heatstatlog Sonar
    AO Accuracy 0.933 0.759 0.864 0.887
    Recall 0.970 0.828 0.758 0.871
    F1-score 0.948 0.873 0.820 0.885
    Precision 0.928 0.923 0.893 0.900
    PSO Accuracy 0.867 0.779 0.877 0.871
    Recall 0.955 0.857 0.758 0.903
    F1-score 0.900 0.909 0.833 0.875
    Precision 0.851 0.968 0.926 0.848
    ARO Accuracy 0.905 0.762 0.864 0.920
    Recall 0.970 0.812 0.758 0.935
    F1-score 0.928 0.881 0.820 0.921
    Precision 0.889 0.963 0.893 0.906
    LBARO Accuracy 0.943 0.778 0.889 0.935
    Recall 0.985 0.867 0.758 0.968
    F1-score 0.956 0.912 0.847 0.938
    Precision 0.929 0.963 0.962 0.909

     | Show Table
    DownLoad: CSV

    The LBARO achieved the highest scores across all four classification metrics on the Ionosphere, Heatstatlog, and Sonar datasets in the experiment. Its classification performance is significantly superior to the other algorithms.

    The accuracy and precision of the vehicle dataset are marginally worse than those of the PSO, but generally, the effect is the best and the other metrics remain fantastic. Though the classification performance is still inferior to the three comparison methods, the LBARO achieves an outstanding overall ranking in terms of score.

    The NSL-KDD dataset is the modified edition of the KDD99 dataset [43,44,45,46]. It emerged as the solution to several intrinsic issues, for instance the duplicate record issue. The NSL-KDD dataset is divided into two subsets: a training set and a test set.

    The NSL-KDD dataset, which consists of 41 features with one column of labelled characteristics, is utilized for classification testing. The dataset includes four attack types: denial of service (DoS), probing, user to root (U2R), and remote to local (R2L). The labels for the typical type of data are assigned to 0, while the labels for the four types of aberrant attacks are set to 1. Following the deletion of the features in columns 10–22 of the dataset, all the data are normalized. The features of non-essential network connection records are also removed. Additionally, 10% and 5% of the training and testing sets, respectively, are randomly chosen for testing. ROC curve is presented in Figures 6 and 7, while corresponding test data is provided in Tables 7 and 8.

    Figure 6.  ROC curve of 5% NSL-KDD.
    Figure 7.  ROC curve of 10% NSL-KDD.
    Table 7.  Classification results of 5% NSL-KDD.
    Metrics AO PSO ARO LBARO
    Accuracy 0.813 0.802 0.797 0.831
    Recall 0.957 0.938 0.973 0.951
    F1-score 0.815 0.804 0.805 0.829
    Precision 0.710 0.703 0.687 0.734
    Number of features 10 14 12 6

     | Show Table
    DownLoad: CSV
    Table 8.  Classification results of 10% NSL-KDD.
    Metrics AO PSO ARO LBARO
    Accuracy 0.803 0.843 0.806 0.846
    Recall 0.969 0.958 0.970 0.969
    F1-score 0.809 0.840 0.812 0.845
    Precision 0.694 0.748 0.700 0.749
    Number of features 10 15 15 11

     | Show Table
    DownLoad: CSV

    The results gathered from the experiments on the 5% dataset are indicated in Table 7. The LBARO outperformed the other three algorithms in all three metrics, except for recall. Additionally, accuracy has increased by 1.8–3.4% when compared to the other algorithms. These findings suggest that the modified algorithm has a clear accuracy in classification and does not exhibit any glaring classification errors. With a 1.4–2.5% increase in the F1-score, it can be said that the updated method more effectively balances recall and precision and can benefit from both effects simultaneously. LBARO is precisely accurate in classifying the data samples as positive classes, and the occurrence of incorrect predictions is significantly minimized. The precision is enhanced by 2.4–4.7%. Additionally, by reducing the number of chosen feature values to six, a significant number of redundant features are eliminated, which lessens the workload associated with intrusion detection and boosts its efficiency. This suggests that for the effect of feature selection on a 5% dataset, the LBARO performs best overall.

    The results gathered from the experiments on the 10% dataset are indicated in Table 8. The data in the table illustrates that while the recall of the AO and LBARO is the same, the accuracy and F1-score of the LBARO are significantly higher than those of the AO. The modified algorithm performs better overall and has a more comprehensive effect than the original PSO and ARO, which were the least effective and ranked low for the number of features selected. This suggests that the feature selection of the LBARO on the 10% dataset yields the best overall performance. The results indicate that with feature selection on 10% of the dataset, the LBARO output reflects its best overall performance.

    The ROC curve is intuitively effective in reflecting the excellence of the classifier's performance. The extent to which the ROC curve is leaning towards the upper-left corner determines the excellence of the classifier. The comparison indicates that, for different numbers of subsets taken from the NSL-KDD dataset, the results are all that the curve curvature of LBARO is more towards the upper left corner. Its classification accuracy is the most superior.

    The UNSW-NB15 dataset is available for network intrusion detection. It is a public dataset. It is provided by the Network Security Laboratory at the University of New South Wales in Sydney [47,48]. The dataset simulates network traffic in a real network environment and contains a variety of common network attacks and normal traffic. The UNSW-NB 15 dataset contains 175,341 network connection records, which include summary information, network connection characteristics, and traffic statistics. The network connections in the dataset are labelled as normal traffic or with different types of attacks such as DoS, scanning, intrusion, etc. In addition, it contains a detailed description of the attacks and a categorization of the attack types.

    UNSW-NB 15 dataset preparation. Firstly, deleting superfluous features, and removing ID features in the dataset, is only the data serial number. Secondly, the data is numericized, comprising the features proto, service, status, and attack_cat, and their numerical values are processed. In the proto attribute, since its values are too varied and yet certain data are too little, the three most essential values of network traffic TCP, UDP, and ICMP are mapped to 1, 2, and 3, respectively, and the rest of the values are mapped to 4. The rest of the non-numerical properties are changed according to the natural number ordering. The data is then normalized.

    Table 9 shows the data results of the experiments on the UNSW-NB 15 dataset 10,000 dataset. Based on the data, LBARO scores higher than the other three algorithms in all three metrics except the precision rate, and the comparison shows that the improvement exists at most 0.6%, 2.19%, and 0.85% effect enhancement in the accuracy, recall, and F1-score respectively. Additionally, the final method reduces feature values to 12, thereby filtering out superfluous redundant features and decreasing intrusion detection effort while also increasing intrusion detection efficiency. According to the statistics, the LBARO performs the best overall when it comes to how feature selection affects the UNSW-NB 15 dataset.

    Table 9.  Classification results of UNSW-NB 15.
    Metrics AO PSO ARO LBARO
    Accuracy 0.9210 0.9217 0.9260 0.9270
    Recall 0.9303 0.9130 0.9302 0.9349
    F1-score 0.8966 0.8956 0.9024 0.9041
    Precision 0.8652 0.8788 0.8763 0.8753
    Number of features 12 21 13 12

     | Show Table
    DownLoad: CSV

    The iterative fitness curve results of the algorithms are presented in Figure 8. The number of folds in the fitness curve can indicate the LBARO's effectiveness in avoiding local maxima, while the curve's steepness and height can reflect its ability to find iterative maxima in a given environment. The fitness curve in the figure illustrates that LBARO can achieve a higher fitness value and a reduced error with the same population size and number of repetitions. The frequency of zigzags in the curve indicates that LBARO consistently navigates out of the local optimum and approaches the optimal solution more efficiently during the procedure.

    Figure 8.  Fitness curves of UNSW-NB 15.

    The LBARO has the best classification accuracy, as demonstrated by the comparison of the ROC curve in Figure 9, which indicates superior results. With a value range of [0, 1], the AUC value can also, to some extent, represent the classifier's performance. As can be seen from the bar chart in Figure 10, the AUC values of the modified algorithm are also all higher than the other algorithms and are closer to 1. These results signify the authenticity of its detection method, rendering it notably valuable.

    Figure 9.  ROC curves of UNSW-NB 15.
    Figure 10.  AUC results of UNSW-NB 15.

    The SDN concept was introduced by Prof Mckeown in 2009. It has been gaining more and more acceptance and has also been utilized and implemented in numerous data centers [49,50]. It can be challenging for manufacturers to address the numerous vulnerabilities and dangers posed by developing technology. Consequently, the deployment of IDS is an important component of the network architecture. The aim is to monitor the network for the presence of malicious activities. No existing publicly available dataset can be directly utilized for anomaly detection systems applied in SDN networks. InSDN by Nhien-An Le-Khac first generated a comprehensive SDN dataset to validate IDS's performance. The new dataset includes benign and various attack categories that can occur in different elements of the SDN platform. 343,939 instances total are included in the dataset for both normal and attack traffic, with 68,424 instances coming from normal data and 27,515 instances from attack traffic.

    Classification test on the InSDN dataset. There are three files in the InSDN dataset, in which Normal_data.csv is the normal data, and the remaining two files, metasploitable-2.csv, and OVS.csv are the anomalous attack information. The label "normal" is assigned the value 0, while the remaining anomalous data is labelled with 1. After normalizing the data, 10,000 random data points are taken from the data set for testing.

    Table 10 presents the results of the experiments on the 10,000-item InSDN dataset. From the presented data, it is evident that LBARO performs well overall. Although it ranks second in the number of selected features, there is no significant difference compared to AO, and both achieve better results. Furthermore, the score data for the other four performance indicators surpasses that of other algorithms, all showing improvements in their metrics. The comprehensive evaluation reveals that LBARO enhances detection accuracy by effectively reducing redundant features and improving detection rates. Therefore, it can be concluded that LBARO’s feature selection effect on the InSDN dataset is relatively superior and leads to certain performance improvements.

    Table 10.  Classification results of InSDN.
    Metrics AO PSO ARO LBARO
    Accuracy 0.9857 0.9860 0.9863 0.9900
    Recall 0.9837 0.9837 0.9845 0.9861
    F1-score 0.9825 0.9829 0.9833 0.9877
    Precision 0.9813 0.9821 0.9821 0.9894
    Number of features 5 15 14 6

     | Show Table
    DownLoad: CSV

    The iterative fitness curve results of the algorithm are presented in Figure 11. At this point, for the zigzag frequency of the fitness curves, the test results in the InSDN dataset are similar to those in the UNSWNB-15 dataset. It indicates that the excellence of LBARO can be effectively demonstrated in different datasets as well. Moreover, upon zooming in on Figure 12, the discernible ROC curve exhibits a more pronounced upper-left corner, indicating a superior classification effect based on the evaluation criteria. As depicted in the bar chart in Figure 13, while all four algorithms exhibit improved effectiveness, the AUC value of the modified algorithm remains the highest, underscoring the authenticity of its detection method.

    Figure 11.  Fitness curve of InSDN.
    Figure 12.  ROC curve of InSDN.
    Figure 13.  AUC results of InSDN.

    This work synergistically modifies the ARO by utilizing four approaches. By absorbing the mud ring feeding strategy of the BDO, the advantages of its global exploration ability and fast convergence speed are taken advantage of. The shortcomings of the ARO, with poor global exploration ability and slow convergence speed, are compensated. In the meantime, an adaptive switching mechanism is presented to guide the equilibrium between the original algorithm and the mud ring feeding strategy. Also, to take advantage of its capacity to produce giant strides and cause the algorithm to deviate as much as possible from the local optimum, the levy flight strategy is implemented during the local exploitation phase. Then, the dynamic lens-imaging learning strategy is introduced to further enhance the perturbation ability. This strategy aims to improve the ARO's overall performance by increasing the population richness of the populations. At this point, the LBARO is adopted to construct a feature selection model for network intrusion detection, which effectively overcomes the issue of the existence of unduly duplicated features in the network intrusion detection dataset. The experimental design examines the model integrated with various algorithms, and the conclusions are as follows:

    1) The exploration ability of the ARO can be effectively enhanced by the LBARO, ensuring that the iteration process can converge rapidly. Additionally, the exploration and exploitation of the LBARO are more balanced, and the last larger volatility and population richness enhancement can make it easier for the LBARO to escape the local optimum.

    2) Four benchmark functions, four single-peaks, and four multi-peaks, are utilized to evaluate the performance of the LBARO. The findings indicate that, compared with other algorithms, the LBARO obtains the minimum values and lowest standard deviation and converges more rapidly. It exhibits superior stability and an improved ability to approach the ideal.

    3) The study presents a novel LBARO-based feature selection model for network intrusion detection. It excels in both dimensionality reduction and detection rate enhancement, securing the top-ranking position in overall tests conducted on four distinct types of network datasets simulating real network traffic data.

    In the era of big data, data analysis is imperative. Differences in analyzed data can directly affect the generation of economic value and the yield of social benefits. However, the data contains unnecessary characteristic features. This undoubtedly causes a significant impact on the accuracy of data prediction and analysis, among other issues. To improve the quality of data, a significant volume of duplicated network traffic must be subjected to data dimensionality reduction. The research demonstrates that the processing of network traffic data can be solved using feature selection in the network intrusion detection model.

    The network traffic data that has undergone dimensionality reduction processing can significantly improve the accuracy and prediction time of the ensuing prediction and offer a certain implementation baseline for actual data processing. Currently, this research only considers the experimental comparison of binary classification on four network datasets. To improve the performance of the proposed model and strengthen its stability, additional network datasets will be employed in further research to provide a more complete data classification scenario.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This work has been supported by the National Natural Science Foundation of China under Grant 61602162 and Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.

    The authors declare no conflict of interest.



    [1] Jiang D, Wang Q, Ding F, et al. (2019) Potential marginal land resources of cassava worldwide: A data–driven analysis. Renew. and Sustain. Energy Rev 104: 167–173. https://doi.org/10.1016/j.rser.2019.01.024
    [2] FAO (Food and Agriculture Organization of the United Nations) (2018) Food Outlook–Biannual Report on Global Food Markets–November 2018. Available from: https://reliefweb.int/report/world/food-outlook-biannual-report-global-food-markets-november-2018
    [3] Esuma W, Nanyonjo AR, Miiro R, et al. (2019) Men and women's perception of yellow–root cassava among rural farmers in eastern Uganda. Agri Food Secur 8: 1–9. https://doi.org/10.1186/s40066-019-0253-1 doi: 10.1186/s40066-019-0253-1
    [4] Okoruwa VO, Abass AB, Akin–olagunju OA, et al. (2020) Does institution type affect access to finance for cassava actors in Nigeria? J of Agric and Food Res 2(October 2019): 100023. https://doi.org/10.1016/j.jafr.2020.100023
    [5] Kihara J, Bolo P, Kinyua M, et al. (2020) Micronutrient deficiencies in African soils and the human nutritional nexus: opportunities with staple crops. Environ Geochem and Health 42: 3015–3033. https://doi.org/10.1007/s10653-019-00499-w doi: 10.1007/s10653-019-00499-w
    [6] FAOStat (2023) Data, production, crops. Rome (Ita): Food and Agriculture Organisation. Available from: http://www.fao.org/faostat/en/#data/QC. 15
    [7] Fernando NML, Amaraweera APSM, Gunawardane, OHP, et al. (2022) Sustainable biorefinery approach for cassava: A Review. Eng: J of the Inst of Eng Sri Lanka 2: 71–88. http://doi.org/10.4038/engineer.v55i2.7510 doi: 10.4038/engineer.v55i2.7510
    [8] Ayetigbo O, Latif S, Abass A, et al. (2018). Comparing characteristics of root, flour and starch of biofortified yellow–flesh and white–flesh cassava variants, and sustainability considerations: A review. Sustainability (Switzerland). 10: 1–32. https://doi.org/10.3390/su10093089
    [9] Olukanni DO, Olatunji TO (2018) Cassava waste management and biogas generation potential in selected local government areas in Ogun State, Nigeria. Recycl 3: 1–12. https://doi.org/10.3390/recycling3040058 doi: 10.3390/recycling3040058
    [10] Lerdlattaporn R, Phalakornkula C, Trakulvichean S, et al. (2021) Implementing circular economy concept by converting cassava pulp and wastewater to biogas for sustainable production in starch industry. Sustain Environ Res 31: 1–12. https://doi.org/10.1186/s42834-021-00093-9 doi: 10.1186/s42834-021-00093-9
    [11] Bradu P, Biswas A, Nair C, et al. (2023) Recent advances in green technology and Industrial Revolution 4.0 for a sustainable future. Environ Sci Pollution Res 30: 124488–124519. https://doi.org/10.1007/s11356-022-20024-4
    [12] OCDE/FAO (2019) OCDE–FAO Perspectivas Agrícolas 2019–2028, OECD Publishing, París/Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma. https://doi.org/10.1787/7b2e8ba3-es
    [13] Martínez DG, Feiden A, Bariccatti R, et al. (2018) Ethanol production from waste of cassava processing. Appl Sci 8: 1–8. https://doi.org/10.3390/app8112158 doi: 10.3390/app8112158
    [14] Chatellard L, Marone A, Carrère H, et al. (2017) Trends and challenges in biohydrogen production from agricultural waste. In: Singh A, Rathore D. Biohydrogen production: sustainability of current technology and future perspective. Springer, New Delhi. p. 69–95. https://doi.org/10.1007/978-81-322-3577-4_4
    [15] Sivamani S, Chandrasekaran AP, Balajii M, et al. (2018) Evaluation of the potential of cassava–based residues for biofuels production. Rev Environ Sci Biotech 17: 553–570. https://doi.org/10.1007/s11157-018-9475-0 doi: 10.1007/s11157-018-9475-0
    [16] Gogoi N, Sarma B, Mondal SC, et al. (2019) Use of biochar in sustainable agriculture. In: Farooq, M., and Pisante, M. editors. Innovations in Sustainable Agriculture. Springer, Cham, p. 501–528. https://doi.org/10.1007/978-3-030-23169-9_16
    [17] Aruwajoye GS, Faloye FD, Kana EG (2020a) Process optimisation of enzymatic saccharification of soaking assisted and thermal pretreated cassava peels waste for bioethanol production. Waste Biomass Valor 11: 2409–2420 https://doi.org/10.1007/s12649-018-00562-0
    [18] Pason P, Tachaapaikoon C, Panichnumsin P, et al. (2020) One–step biohydrogen production from cassava pulp using novel enrichment of anaerobic thermophilic bacteria community. Biocatal Agric Biotechnol 27: 1–6. https://doi.org/10.1016/j.bcab.2020.101658 doi: 10.1016/j.bcab.2020.101658
    [19] Aduba CC, Ndukwe JK, Onyejiaka CK, et al. (2023) Integrated valorization of cassava wastes for production of bioelectricity, biogas and biofertilizer. Waste Biomass Valori 14: 4003–4019. https://doi.org/10.1007/s12649-023-02126-3 doi: 10.1007/s12649-023-02126-3
    [20] Xiong X, Iris KM, Tsang DC, et al. (2019) Value-added chemicals from food supply chain wastes: State-of-the-art review and future prospects. Chem Eng J 375: 121983. https://doi.org/10.1016/j.cej.2019.121983 doi: 10.1016/j.cej.2019.121983
    [21] Rodrigues CG, Silva VDM, Loyola ACDF, et al. (2022) Characterization and identification of bioactive compounds in agro-food waste flours. Quím N 45: 403–409. http://dx.doi.org/10.21577/0100-4042.20170853 doi: 10.21577/0100-4042.20170853
    [22] Ratnadewi AAI, Santoso AB, Sulistyaningsih E (2016) Application of cassava peel and waste as raw materials for xylooligosaccharide production using endoxylanase from Bacillus subtilis of soil termite abdomen. Procedia Chem 18: 31–38. https://doi.org/10.1016/j.proche.2016.01.007 doi: 10.1016/j.proche.2016.01.007
    [23] AMAO JA, Omojasola PF, Barooah M (2019) Isolation and characterization of some exopolysaccharide producing bacteria from cassava peel heaps. Sci Afr 4: 1–11. https://doi.org/10.1016/j.sciaf.2019.e00093 doi: 10.1016/j.sciaf.2019.e00093
    [24] John T, Salihu A, Onyike E (2020) Assessment of cassava peels as renewable substrate for production of poly–γ–glutamic acid by Bacillus subtilis. Environ Sustain 3: 179–186. https://doi.org/10.1007/s42398-020-00102-4 doi: 10.1007/s42398-020-00102-4
    [25] Vishnu D, Dhandapani B, Mahadevan S (2020) Recent advances in organic acid production from microbial sources by utilizing agricultural by–products as substrates for industrial applications. In: Jerold M, Arockiasamy S, Sivasubramanian V. The Handbook of Environmental Chemistry. Springer, Berlin, Heidelberg. p. 67–87. https://doi.org/10.1007/698_2020_577
    [26] Acosta AHA, Barraza YCA, Albis AAR (2017) Adsorción de cromo (VI) utilizando cáscara de yuca (Manihot esculenta) como biosorbente: Estudio cinético. Ing y Desarro 35: 58–79. http://dx.doi.org/10.14482/inde.35.1.8943 doi: 10.14482/inde.35.1.8943
    [27] Albis AA, López AJ, Romero MC (2017) Remoción de azul de metileno de soluciones acuosas utilizando cáscara de yuca (Manihot esculenta) modificada con ácido fosfórico. Prospectiva 15: 60–73. https://doi.org/10.15665/rp.v15i2.777 doi: 10.15665/rp.v15i2.777
    [28] Santos LN, Porto CE, Bulla MK, et al. (2021) Peach palm and cassava wastes as biosorbents of tartrazine yellow dye and their application in industrial effluent. Scientia Plena 17: 1–19. https://doi.org/10.14808/sci.plena.2021.054201 doi: 10.14808/sci.plena.2021.054201
    [29] Salgaonkar BB, Mani K, Bragança JM (2019) Sustainable bioconversion of cassava waste to poly(3–hydroxybutyrate–co–3–hydroxyvalerate) by Halogeometricum borinquense Strain E3. J Polym Environ 27: 299–308. https://doi.org/10.1007/s10924-018-1346-9 doi: 10.1007/s10924-018-1346-9
    [30] Keller M, Ambrosio E, de Oliveira VM et al. (2020). Polyurethane foams synthesis with cassava waste for biodiesel removal from water bodies. Bioresour Technol Rep 10: 1–5. https://doi.org/10.1016/j.biteb.2020.100396 doi: 10.1016/j.biteb.2020.100396
    [31] Sharma HK, Kaushal P (2016) Introduction to tropical roots and tubers. In: Sharma HK, Njintang NY, Singhal RS, Kaushal P. Tropical roots and tubers: production, processing and technology. John Wiley & Sons. p. 1–33. https://doi.org/10.1002/9781118992739.ch1
    [32] Olsen KM (2004) SNPs, SSRs and inferences on cassava's origin. Plant Mol Biol 56: 517–526. https://doi.org/10.1007/s11103-004-5043-9 doi: 10.1007/s11103-004-5043-9
    [33] Léotard G, Duputié A, Kjellberg F, et al. (2009) Phylogeography and the origin of cassava: new insights from the northern rim of the Amazonian basin. Mol Phylogenetics and Evol 53: 329–334. https://doi.org/10.1016/j.ympev.2009.05.003 doi: 10.1016/j.ympev.2009.05.003
    [34] Tovar E, Bocanegra JL, Villafañe C, et al. (2016). Diversity and genetic structure of cassava landraces and their wild relatives (Manihot spp.) in Colombia revealed by simple sequence repeats. Plant Genet Resour: Charact and Util 14: 200–210. https://doi.org/10.1017/S1479262115000246
    [35] Ogbonna AC, Braatz de AL., Mueller LA, et al. (2021) Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication. Theoretical App Genet 134: 1343–1362. https://doi.org/10.1007/s00122-021-03775-5
    [36] Watling J, Shock MP, Mongelo GZ, et al. (2018) Direct archaeological evidence for Southwestern Amazonia as an early plant domestication and food production centre. PLoS ONE 13: 28. https://doi.org/https://doi.org/10.1371/journal.pone.0199868 doi: 10.1371/journal.pone.0199868
    [37] Carvalho LJCB, Schaal BA (2001) Assessing genetic diversity in the cassava (Manihot esculenta Crantz) germplasm collection in Brazil using PCR–based markers. Euphytica 120: 133–142. https://doi.org/10.1023/A:1017548930235 doi: 10.1023/A:1017548930235
    [38] Isendahl C (2011) The Domestication and early spread of manioc (Manihot esculenta Crantz): A Brief Synthesis. Latin American Antiquity 22: 452–468. https://doi.org/10.7183/1045-6635.22.4.452 doi: 10.7183/1045-6635.22.4.452
    [39] Simon MF, Reis TS, Mendoza FJM, et al. (2020) Conservation assessment of cassava wild relatives in central Brazil. Biodiver Conserv 29: 1589–1612. https://doi.org/10.1007/s10531-018-1626-7 doi: 10.1007/s10531-018-1626-7
    [40] Nassar N (2000) Cytogenetics and evolution of cassava (Manihot esculenta Crantz). Genet and Mol Biol 23: 1003–1014. https://doi.org/10.1590/S1415-47572000000400046 doi: 10.1590/S1415-47572000000400046
    [41] Pope KO, Pohl MED, Jones JC, et al. (2001) Origin and environmental setting of ancient agriculture in the lowlands of Mesoamerica. Sci 292: 1370–1373. https://doi.org/10.1126/science.292.5520.1370 doi: 10.1126/science.292.5520.1370
    [42] Acosta OG (2005) Asentamiento y sistemas agrícolas en los márgenes del Tonalá bases para el estudio de la paleosubsistencia olmeca en La Venta, Tabasco (ca. 1500-500 aC). Dialogo Antropog 3: 57–72.
    [43] Cagnato C, Ponce JM (2018) Ancient Maya manioc (Manihot esculenta Crantz) consumption: Starch grain evidence from late to terminal classic (8th-9th century CE) occupation at La Corona, northwestern Petén, Guatemala. J of Archaeol Sci: Rep 16(December 2017): 276–286. https://doi.org/10.1016/j.jasrep.2017.09.035
    [44] Carter SE, Fresco LO, Jones PG, et al. (1993) Introduction and diffusion of cassava in Africa. Ibadan: ⅡTA, Research Guide 49. Training Program, International Institute of Tropical Agriculture (ⅡTA). Ibadan, Nigeria. 34 p. https://www.researchgate.net/publication/40207427_Introduction_and_diffusion_of_cassava_in_Africa
    [45] Jones WO (1959) Manioc in Africa. Michigan (EE. UU.): Stanford Univ. Press, Oxford Univ. Press. 315 p.
    [46] Cock JH (2019) Cassava: new potential for a neglected crop. New York (EE. UU.): CRC Press, 208 p.
    [47] Onwueme IC (2002) Cassava in Asia and the Pacific. In: Hillocks RJ, Thresh JM, Bellotti AC. Cassava: Biology, production and utilization. Cab 55–65.
    [48] Lim TK (2016) Manihot esculenta. In: Lim TK. Edible medicinal and non–medicinal plants. Springer, Dordrecht. p. 308–353. https://doi.org/10.1007/978-94-017-7276-1_17
    [49] Aguilar-Rivera N (2024) Life cycle assessment of valorization of root and tuber crop wastes for bio-commodities and biofuels: Cassava as a case study. In: Ray RC editor. Roots, tubers, and bulb crop wastes: Management by biorefinery approaches. Springer, Singapore 333–350 https://doi.org/10.1007/978-981-99-8266-0_15
    [50] Anyanwu CN, Ibeto CN, Ezeoha SL, et al. (2015) Sustainability of cassava (Manihot esculenta Crantz) as industrial feedstock, energy and food crop in Nigeria. Renew Energy 81: 745–752. https://doi.org/10.1016/j.renene.2015.03.075 doi: 10.1016/j.renene.2015.03.075
    [51] Ikuemonisan ES, Mafimisebi TE, Ajibefun I, et al. (2020) Cassava production in Nigeria: trends, instability and decomposition analysis (1970–2018). Heliyon 6: e05089. https://doi.org/10.1016/j.heliyon.2020.e05089 doi: 10.1016/j.heliyon.2020.e05089
    [52] Tokunaga H, Tamon B, Ishitani M, Ito K, et al. (2018). Sustainable management of invasive cassava pests in Vietnam, Cambodia, and Thailand. In: Kokubun M, Asanuma S. Crop Production under Stressful Conditions: Application of Cutting–edge Science and Technology in Developing Countries. p. 131–157. https://doi.org/10.1007/978-981-10-7308-3
    [53] OEC (The Observatory of Economic Complexity) (2020) Cassava. https://oec.world/en
    [54] GSOVN (General Statistics Office of Vietnam) (2019) Agriculture, Forestry and Fishing. Vietnam. Available from: https://www.gso.gov.vn/en/agriculture-forestry-and-fishery/
    [55] ACIAR (Australian Centre for International Agricultural Research) (2023) A tale of two diseases: Cassava and COVID–19. https://www.aciar.gov.au/media-search/blogs/a-tale-two-diseases-cassava-and-covid-19
    [56] Ceballos H, Hershey CH (2017) Cassava (Manihot esculenta Crantz). In: Campos H, Caligari PDS. Genetic Improv of Tropical Crops 129–180. https://doi.org/10.1007/978-3-319-59819-2
    [57] de Souza FD, Rodrigues dos STP, Mazetti FA, et al. (2019) Harvest time optimization leads to the production of native cassava starches with different properties. Int J of Biol Macromol 132: 710–721. https://doi.org/10.1016/j.ijbiomac.2019.03.245 doi: 10.1016/j.ijbiomac.2019.03.245
    [58] Vilpoux O, de Oliveira GD, Pascoli CM (2017) Cassava cultivation in Latin America. Burleigh Dodds Science Publishing. 1–26. https://doi.org/10.19103/as.2016.0014.07
    [59] Andrade CI, Andrade LRS, Bharagava RN, et al. (2021) Valorization of cassava residues for biogas production in Brazil based on the circular economy: An updated and comprehensive review. Clean Eng Technol 4: 100196. https://doi.org/10.1016/j.clet.2021.100196 doi: 10.1016/j.clet.2021.100196
    [60] Thiele G, Friedmann M, Polar V, et al. (2022) Overview. In: Thiele G, Friedmann M, Campos H, Polar V, Bentley JW. editors. Root, Tuber and Banana Food System Innovations. Springer Cham 3–28. https://doi.org/10.1007/978-3-030-92022-7
    [61] Dah-Sol K, Fumiko I (2023) Nutritional composition of cassava (Manihot esculenta) and its application to elder-friendly food based on enzyme treatment. Int J of Food Prop 26: 1311–1323. https://doi.org/10.1080/10942912.2023.2213410 doi: 10.1080/10942912.2023.2213410
    [62] Panghal A, Munezero C, Sharma P, et al. (2019) Cassava toxicity, detoxification and its food applications: a review. Toxin Reviews. https://doi.org/10.1080/15569543.2018.1560334
    [63] Ospina MA, Pizarro M, Tran T, et al. (2021) Cyanogenic, carotenoids and protein composition in leaves and roots across seven diverse population found in the world cassava germplasm collection at CIAT, Colombia. Int J Food Sci Technol 56: 1343–1353. https://doi.org/10.1111/ijfs.14888 doi: 10.1111/ijfs.14888
    [64] FAO WHO (2019) Codex Committee on Contaminats in Foods. In: Organisation, FAA, United, WHOOT. & NATIONS U. editors. Discussion paper on the establishment of MLS for HCN in Cassava and Cassava-based Products and Occurrence of Mycotoxins in these Products. 1–4. Rome, Italy, FAO.
    [65] Boakye PB, Parkes EY, Harrison OA, et al. (2020) Proximate composition, cyanide content, and carotenoid retention after boiling of provitamin A–rich cassava grown in Ghana. Foods 9: 1800. https://doi.org/10.3390/foods9121800 doi: 10.3390/foods9121800
    [66] Herminingrum S (2019) The genealogy of traditional Javanese cassava–based foods. J Ethn Foods 6: 1–16. https://doi.org/10.1186/s42779-019-0015-5 doi: 10.1186/s42779-019-0015-5
    [67] da Silva Santos BR, Silva EFR, Minho LAC, et al. (2020) Evaluation of the nutritional composition in effect of processing cassava leaves (Manihot esculenta) using multivariate analysis techniques. Microchem J 152: 104271. https://doi.org/10.1016/j.microc.2019.104271 doi: 10.1016/j.microc.2019.104271
    [68] Chiwona-Karltun L, Brimer L, Jackson J. (2022) Improving safety of cassava products. In: Thiele G, Friedmann M, Campos H, Polar V, Bentley JW. editors. Root, Tuber and Banana Food System Innovations. Springer Cham, p. 241–258. https://doi.org/10.1007/978-3-030-92022-7
    [69] Tambalo FMZ, Capuno RBA, Estrellana CD, et al. (2023) Effect of processing on the antinutrient and protein contents of cassava leaves from selected varieties. Philippine J of Sci 152: 561–570.
    [70] Wadhwa M, Singh H, Kumar B, et al. (2021) In vitro evaluation of short duration cassava varieties as llivestock feed. Indian J of Anim Sci 91: 965-970. https://doi.org/10.56093/ijans.v91i11.118142 doi: 10.56093/ijans.v91i11.118142
    [71] Leguizamón AJ, Rompato KM, Hoyos RE, et al. (2021) Nutritional evaluation of three varieties of cassava leaves (Manihot esculenta Crantz) grown in Formosa, Argentina. J Food Compos Anal 101: 103986. https://doi.org/10.1016/j.jfca.2021.103986 doi: 10.1016/j.jfca.2021.103986
    [72] Mohidin SRNSP, Moshawih S, Hermansyah A, et al. (2023) Cassava (Manihot esculenta Crantz): A systematic review for the pharmacological activities, traditional uses, nutritional values, and phytochemistry. J EvidBased Integr Med 28: 2515690X231206227.
    [73] Ryan PJ, Riechman SE, Fluckey JD, et al. (2021) Interorgan metabolism of amino acids in human health and disease. In: Wu G. editor. Amino Acids in Nutrition and Health, Advances in Experimental Medicine and Biology. Springer, Cham 1332: 129–149. https://doi.org/10.1007/978-3-030-74180-8_8
    [74] Byju G, Suja G (2020) Mineral nutrition of cassava. Adv Agron 159: 169–235. https://doi.org/10.1016/bs.agron.2019.08.005 doi: 10.1016/bs.agron.2019.08.005
    [75] Laxminarayana K, Mishra S, Soumya, S (2016) Good agricultural practices in tropical root and tuber crops. In: Sharma HK, Kaushal P. Tropical roots and tubers: production, processing and technology. p. 183–224. https://doi.org/10.1002/9781118060858.ch3
    [76] Morgante CV, Nunes SLP, Chaves ARDM, et al. (2020) Genetic and physiological analysis of early drought response in Manihot esculenta and its wild relative. Acta physiologiae plant 42: 1–11. https://doi.org/10.1007/s11738-019-3005-8 doi: 10.1007/s11738-019-3005-8
    [77] Boundy-Mills K, Karuna N, Garay LA, et al. (2019) Conversion of cassava leaf to bioavailable, high-protein yeast cell biomass. J Sci Food Agric 99: 3034–3044. DOI10.1002/jsfa.9517
    [78] USDA (U.S. Department of Agriculture) (2019) Food data central, Cassava, raw. https://fdc.nal.usda.gov/fdc-app.html#/food-details/169985/nutrients
    [79] Mardina P, Irawan C, Putra MD, et al. (2021) Bioethanol production from cassava peel treated with sulfonated carbon catalyzed hydrolysis. Jurnal Kimia Sains dan Aplikasi 24: 1–8. https://doi.0rg/io.i47io/jksa.24.1.1-9
    [80] Chaiareekitwat S, Latif S, Mahayothee B, et al. (2022) Protein composition, chlorophyll, carotenoids, and cyanide content of cassava leaves (Manihot esculenta Crantz) as influenced by cultivar, plant age, and leaf position. Food Chem 372: 131–173. https://doi.org/10.1016/j.foodchem.2021.131173 doi: 10.1016/j.foodchem.2021.131173
    [81] Gundersen E, Christiansen AHC, Jørgensen K, et al. (2022) Production of leaf protein concentrates from cassava: Protein distribution and anti-nutritional factors in biorefining fractions. J Clean Prod 379: 134730. https://doi.org/10.1016/j.jclepro.2022.134730 doi: 10.1016/j.jclepro.2022.134730
    [82] Fanelli NS, Torres-Mendoza LJ, Abelilla JJ, et al. (2023) Chemical composition of cassava-based feed ingredients from South-East Asia. Anim Biosci 36: 908–919. https://doi.org/10.5713/ab.22.0360 doi: 10.5713/ab.22.0360
    [83] Munyahali W, Pypers P, Swennen R, et al. (2017) Responses of cassava growth and yield to leaf harvesting frequency and NPK fertilizer in South Kiv, Democratic Republic of Congo. F Crops Res 214: 194–201. https://doi.org/10.1016/j.fcr.2017.09.018 doi: 10.1016/j.fcr.2017.09.018
    [84] Otun S, Escrich A, Achilonu I, et al. (2023) The future of cassava in the era of biotechnology in Southern Africa. Crit Re Biotechnol 43: 594–612. https://doi.org/10.1080/07388551.2022.2048791 doi: 10.1080/07388551.2022.2048791
    [85] Saraiva LL, da Silva LCA, da Silva SV (2019) Effect of harvesting times on agronomic characteristics of industrial cassava genotypes. Revista Brasileira de Ciências Agrárias. 14: 1–6. https://doi.org/10.5039/agraria.v14i2a5647
    [86] Sukara E, Hartati S, Ragamustari SK (2020). State of the art of Indonesian agriculture and the introduction of innovation for added value of cassava. Plant Biotechnol Rep 14: 207–212. https://doi.org/10.1007/s11816-020-00605-w doi: 10.1007/s11816-020-00605-w
    [87] Veiga JPS, Valle TL, Feltran JC, et al. (2016). Characterization and productivity of cassava waste and its use as an energy source. Renew Energy 93: 691–699. https://doi.org/10.1016/j.renene.2016.02.078 doi: 10.1016/j.renene.2016.02.078
    [88] Ozoegwu CG, Eze C, Onwosi CO, et al. (2017) Biomass and bioenergy potential of cassava waste in Nigeria: Estimations based partly on rural–level garri processing case studies. Renew and Sustain Energy Rev 72: 625–638. https://doi.org/10.1016/j.rser.2017.01.031 doi: 10.1016/j.rser.2017.01.031
    [89] Howeler R, Lutaladio N, Thomas G (2013) Save and grow cassava: a guide to sustainable production intensification. Available from: http://www.fao.org/3/i3278e/i3278e.pdf
    [90] Lismeri L, Anggraini M, Sudarno A. et al (2019) Characterization and analysis of cassava stems as potential biomass for bio–oil production via electromagnetic–assisted catalytic liquefaction. Adv Eng Res 202: 292–298. http://repository.lppm.unila.ac.id/16247/1/ICBS%20pdf.pdf
    [91] Akogun OA, Waheed MA, Ismaila SO, et al. (2020) Co-briquetting characteristics of cassava peel with sawdust at different torrefaction pretreatment conditions. Energy Sources Part A Recovery Utilization Environ Eff 1–19. https://doi.org/10.1080/15567036.2020.1752333
    [92] Mbinda W, Mukami A (2022) Breeding for postharvest physiological deterioration in cassava: problems and strategies. CABI Agric Biosci 3: 30. https://doi.org/10.1186/s43170-022-00097-4 doi: 10.1186/s43170-022-00097-4
    [93] Amelework AB, Bairu MW, Maema O, et al. (2021) Adoption and promotion of resilient crops for climate risk mitigation and import substitution: A case analysis of cassava for South African agriculture. Front Sustain Food Syst 5: 617783. https://doi.org/10.3389/fsufs.2021.617783 doi: 10.3389/fsufs.2021.617783
    [94] Kringel DH, El Halal SLM, Zavareze EDR, et al. (2020) Methods for the extraction of roots, tubers, pulses, pseudocereals, and other unconventional starches sources: a review. Starch-Stärke 72: 1900234. https://doi.org/10.1002/star.201900234 doi: 10.1002/star.201900234
    [95] Guangyu D, Xueting W, Bochao Z, et al. (2021) The transformation and outcome of traditional cassava starch processing in Guangxi, China. Environ Techn 42: 3278–3287. DOI: 10.1080/09593330.2020.1725647 doi: 10.1080/09593330.2020.1725647
    [96] Salla DA, Furlaneto FP, Cabello C, et al. (2010) Energetic analysis of the ethanol production systems of cassava (Manihot esculenta Crantz). Revista Brasileira de Engenharia Agrícola e Ambiental 14: 444–448. https://doi.org/10.1590/S1415-43662010000400015 doi: 10.1590/S1415-43662010000400015
    [97] Santos SA, Lopes SY, Araújo KR, et al. (2017) Waste bio–refineries for the cassava starch industry: New trends and review of alternatives. Renew Sustain Energy Rev 73: 1265–1275. https://doi.org/10.1016/j.rser.2017.02.007 doi: 10.1016/j.rser.2017.02.007
    [98] Tan X, Gu B, Li X, et al. (2017). Effect of growth period on the multi–scale structure and physicochemical properties of cassava starch. Int J Biol Macromol 101: 9–15. https://doi.org/10.1016/j.ijbiomac.2017.03.031 doi: 10.1016/j.ijbiomac.2017.03.031
    [99] Buddhakulsomsiri J, Parthanadee P, Pannakkong W (2018) Prediction models of starch content in fresh cassava roots for a tapioca starch manufacturer in Thailand. Comput Electron Agric 154: 296–303. https://doi.org/10.1016/j.compag.2018.09.016 doi: 10.1016/j.compag.2018.09.016
    [100] Tappiban P, Sraphet S, Srisawad N, et al. (2020) Effects of cassava variety and growth location on starch fine structure and physicochemical properties. Food Hydrocoll 108: 106074. https://doi.org/10.1016/j.foodhyd.2020.106074 doi: 10.1016/j.foodhyd.2020.106074
    [101] García–Mogollón C, Salcedo–Mendoza J, Alvis–Bermúdez A (2018) Optimum conditions for the leaching step in the extraction of cassava starch. Biotecnología en el Sect Agropecu y Agroind 16: 62–67. http://www.scielo.org.co/scielo.php?script = sci_arttext & pid = S1692-35612018000100062
    [102] Zhang X, Guo D, Blennow A, et al. (2021) Mineral nutrients and crop starch quality. Trends Food Sci Technol 114: 148–157. https://doi.org/10.1016/j.tifs.2021.05.016 doi: 10.1016/j.tifs.2021.05.016
    [103] Agama–Acevedo E, Flores–Silva PC, Bello–Perez LA (2019) Cereal starch production for food applications. In: Silva CMTP, Schmiele M. editors. Starches for Food Application, Chemical, Technological and Health Properties 71–102. https://doi.org/10.1016/B978-0-12-809440-2.00003-4
    [104] Devi A, Bajar S, Sihag P, et al. (2023) A panoramic view of technological landscape for bioethanol production from various generations of feedstocks. Bioeng 14: 81–112. https://doi.org/10.1080/21655979.2022.2095702 doi: 10.1080/21655979.2022.2095702
    [105] IFBB (Institute for Bioplastics and Biocomposites) (2019) Biopolymers facts and statistics, Production capacities, processing routes, feedstock, land and water use. https://www.ifbb-hannover.de/en/facts-and-statistics.html
    [106] Del Rosario–Arellano JL, Bolio–López GI, Valadez–González A, et al. (2021) Exploration of cassava clones for the development of biocomposite films. AIMS Mater Sci 9: 85–104. doi: 10.3934/matersci.2022006 doi: 10.3934/matersci.2022006
    [107] Sunmonu M, Sanusi M, Lawal H (2021) Effect of different processing conditions on quality of cassava. Croatian J Food Sci Technol 13: 69–77. https://doi.org/10.17508/CJFST.2021.13.1.09 doi: 10.17508/CJFST.2021.13.1.09
    [108] Adewale P, Yancheshmeh MS, Lam E (2022) Starch modification for non-food, industrial applications: Market intelligence and critical review. Carbohydr Polym 291: 119590. https://doi.org/10.1016/j.carbpol.2022.119590 doi: 10.1016/j.carbpol.2022.119590
    [109] Del Rosario–Arellano JL, Meneses–Márquez I, Leyva–Ovalle OR, et al. (2020) Morphoagronomic and industrial performance of cassava (Manihot esculenta Crantz) germplasm for the production of starch and solid byproducts. AIMS Agric Food 5: 617–634. doi: 10.3934/agrfood.2020.4.617 doi: 10.3934/agrfood.2020.4.617
    [110] Trakulvichean S, Chaiprasert P, Otmakhova J, et al. (2017) Integrated economic and environmental assessment of biogas and bioethanol production from cassava cellulosic waste. Waste Biomass Valor 10: 691–700. https://doi.org/10.1007/s12649-017-0076-x doi: 10.1007/s12649-017-0076-x
    [111] Giau VV, Van TT. Le LT, et al. (2023) Application of linear programming for cassava starch production optimization in Vietnam within a circular economy framework toward zero emission. Environ Eng Res 28: 220214. https://doi.org/10.4491/eer.2022.214
    [112] de Carvalho JC, Borghetti IA, Cartas LC, et al. (2018) Biorefinery integration of microalgae production into cassava processing industry: Potential and perspectives. Bioresour Technol 247(September 2017): 1165–1172. https://doi.org/10.1016/j.biortech.2017.09.213 doi: 10.1016/j.biortech.2017.09.213
    [113] Okunade DA, Adekalu KO (2013) Physico–chemical analysis of contaminated water resources due to cassava wastewater effluent disposal. Eur Int J Sci Technol 2: 75–84. https://www.researchgate.net/publication/263328306_Physico-chemical_analysis_of_contaminated_water_resources_due_to_cassava_wastewater_effluent_disposal
    [114] Zhang M, Xie L, Yin Z, et al. (2016) Biorefinery approach for cassava–based industrial wastes: current status and opportunities. Bioresour Technol 215: 50–62. https://doi.org/10.1016/j.biortech.2016.04.026 doi: 10.1016/j.biortech.2016.04.026
    [115] Raza QUA, Bashir MA, Rehim A, et al. (2021) Sugarcane industrial byproducts as challenges to environmental safety and their remedies: A review. Water 13: 3495. https://doi.org/10.3390/w13243495 doi: 10.3390/w13243495
    [116] Jennings DL (2019) Starch crops. In: CRC Handbook of plant Science in Agriculture 137–144. CRC press.
    [117] Benesi IR, Labuschagne MT, Dixon AG, et al. (2004) Stability of native starch quality parameters, starch extraction and root dry matter of cassava genotypes in different environments. J Sci Food Agric 84: 1381–1388. https://doi.org/10.1002/jsfa.1734
    [118] Liang S, Gliniewicz K, Mendes-Soares H, et al. (2015) Comparative analysis of microbial community of novel lactic acid fermentation inoculated with different undefined mixed cultures. Bioresour Technol 179: 268–274. https://doi.org/10.1016/j.biortech.2014.12.032. doi: 10.1016/j.biortech.2014.12.032
    [119] Mangla AK, Chawla V, Singh G (2017) Review paper on high temperature corrosion and its control in coal fired boilers. Int J Latest Trends Eng Technol (Special Issue–AFTMME): 088–092. https://www.ijltet.org/pdfviewer.php?id = 925 & j_id = 4238.
    [120] Vargas YA, Peréz LI (2018) Aprovechamiento de residuos agroindustriales en el mejoramiento de la calidad del ambiente. Revista Facultad de Ciencias Básicas 1: 59–72. https://doi.org/10.18359/rfcb.3108 doi: 10.18359/rfcb.3108
    [121] Cao X, Tong J, Ding M, et al. (2019) Physicochemical properties of starch in relation to rheological properties of wheat dough (Triticum aestivum L.). Food chem 297: 125000. https://doi.org/10.1016/j.foodchem.2019.125000
    [122] Park J, O0h SK, Chun, HJ, et al. (2020) Structural and physicochemical properties of native starches and non-digestible starch residues from Korean rice cultivars with different amylose contents. Food Hydrocoll 102: 105544. https://doi.org/10.1016/j.foodhyd.2019.105544 doi: 10.1016/j.foodhyd.2019.105544
    [123] Awoyale AA, Lokhat D, Eloka-Eboka AC (2021) Experimental characterization of selected Nigerian lignocellulosic biomasses in bioethanol production. Int J Ambient Energy 42: 1343–1351. https://doi.org/10.1080/01430750.2019.1594375 doi: 10.1080/01430750.2019.1594375
    [124] Biel W, Jaroszewska A, Stankowski S, et al. (2021) Comparison of yield, chemical composition and farinograph properties of common and ancient wheat grains. Eur Food Res Technol 247: 1525–1538. https://doi.org/10.1007/s00217-021-03729-7 doi: 10.1007/s00217-021-03729-7
    [125] Pineda-Gómez P, González NM, Contreras-Jiménez B, et al. (2021) Physicochemical characterisation of starches from six potato cultivars native to the Colombian andean region. Potato Res 64: 21–39. https://doi.org/10.1007/s11540-020-09462-0 doi: 10.1007/s11540-020-09462-0
    [126] Singh SP, Jawaid M, Chandrasekar M, et al. (2021) Sugarcane wastes into commercial products: Processing methods, production optimization and challenges. J Clean Prod 328: 129453. https://doi.org/10.1016/j.jclepro.2021.129453 doi: 10.1016/j.jclepro.2021.129453
    [127] Ghaffar Y, Ashraf W, Akhtar N, et al. (2022) Estimation of statistical parameters in candidate wheat genotypes for yield-related traits. J King Saud University-Sci 34: 102364. https://doi.org/10.1016/j.jksus.2022.102364 doi: 10.1016/j.jksus.2022.102364
    [128] Martínez RD, Cirilo AG, Cerrudo AA, et al. (2022) Environment affects starch composition and kernel hardness in temperate maize. J Sci Food Agric 102: 5488–5494. DOI10.1002/jsfa.1190
    [129] Yossa R, Ahmad FN. Kumari J, et al. (2022) Apparent digestibility coefficients of banana peel, cassava peel, cocoa husk, copra waste, and sugarcane bagasse in the GIFT strain of Nile tilapia (Oreochromis niloticus). J Appl Aquaculture 34: 734–754. https://doi.org/10.1080/10454438.2021.1890304
    [130] Liu H, Lin X, Li X, et al. (2023) Haplotype variations of sucrose phosphate synthase B gene among sugarcane accessions with different sucrose content. BMC Genom 24: 1–12. https://doi.org/10.1186/s12864-023-09139-1 doi: 10.1186/s12864-023-09139-1
    [131] Thuppahige VTW, Moghaddam L, Welsh ZG (2023) Investigation of critical properties of Cassava (Manihot esculenta) peel and bagasse as starch-rich fibrous agro-industrial wastes for biodegradable food packaging. Food Chem 422: 136200. https://doi.org/10.1016/j.foodchem.2023.136200 doi: 10.1016/j.foodchem.2023.136200
    [132] Kovač M, Ravnjak B, Šubarić D, et al. (2024) Isolation and characterization of starch from different potato cultivars grown in Croatia. Applied Sci 14: 909. https://doi.org/10.3390/app14020909 doi: 10.3390/app14020909
    [133] Barros FFC, Ponezi AN, Pastore GM (2008) Production of biosurfactant by Bacillus subtilis LB5a on a pilot scale using cassava wastewater as substrate. J Ind Microbiol Biotech 35: 1071–1078. https://doi.org/10.1007/s10295-008-0385-y doi: 10.1007/s10295-008-0385-y
    [134] Fleck L, Tavares MH, Eyng E, et al. (2017) Optimization of anaerobic treatment of cassava processing wastewater. Engenharia Agríc 37: 574–590. https://doi.org/10.1590/1809-4430-Eng.Agric.v37n3p574-590/2017 doi: 10.1590/1809-4430-Eng.Agric.v37n3p574-590/2017
    [135] Santos RJE, da Silva SAM, Martini M, et al. (2019) Rhodotorula glutinis cultivation on cassava wastewater for carotenoids and fatty acids generation. Biocatal Agric Biotechnol. 22: 101419. https://doi.org/10.1016/j.bcab.2019.101419
    [136] Muniz, MJ., Santos TT., Ronchesel RM, et al. (2022) Chlorella sorokiniana as bioremediator of wastewater: Nutrient removal, biomass production, and potential profit. Bioresour Technol Rep 17: 100933. https://doi.org/10.1016/j.biteb.2021.100933
    [137] Padi RK, Chimphango A (2020) Commercial viability of integrated waste treatment in cassava starch industries for targeted resource recoveries. J Clean Prod 265: 1–33. https://doi.org/10.1016/j.jclepro.2020.121619 doi: 10.1016/j.jclepro.2020.121619
    [138] Li M, Zhou H, Zi X, et al. (2024) Feeding value assessment of five varieties whole-plant cassava in tropical China. Fermentation 10: 45. https://doi.org/10.3390/fermentation10010045 doi: 10.3390/fermentation10010045
    [139] Parmar A, Sturm B, Hensel O (2017) Crops that feed the world: Production and improvement of cassava for food, feed, and industrial uses. Food Secur 9: 907–927. https://doi.org/10.1007/s12571-017-0717-8 doi: 10.1007/s12571-017-0717-8
    [140] Kombate K, Dansi D, Dossou–Aminon I, et al. (2017) Diversity of cassava (Manihot esculenta Crantz) cultivars in the traditional agriculture of Togo. Int J Curr Res Biosci Plant Biol 4: 98–113. https://doi.org/10.20546/ijcrbp.2017.406.012 doi: 10.20546/ijcrbp.2017.406.012
    [141] Quadros FGS, Gomide IS (2021) Aspectos socioeconômicos e ambientais da produção de farinha de mandioca na comunidade quilombola Amazônica do Cuxiú, Bonito/PA. Nat Conserv 14: 55–61. http://doi.org/10.6008/CBPC2318-2881.2021.001.0006 doi: 10.6008/CBPC2318-2881.2021.001.0006
    [142] Achi CG, Coker AO, Sridhar MKC (2018) Cassava processing wastes: options and potentials for resource recovery in Nigeria. In: Ghosh S. Utilization and management of bioresources. Springer, Singapore. p. 77–89 https://doi.org/10.1007/978-981-10-5349-8_8
    [143] Silva PA, Pires AJ, Pina DDS, et al. (2022) Cassava wastewater can be safely used in the diet of feedlot lambs. Anim Prod Sci 62: 601–609. https://doi.org/10.1071/AN20214 doi: 10.1071/AN20214
    [144] Hassan ZM, Manyelo TG, Selaledi L, et al. (2020) The effects of tannins in monogastric animals with special reference to alternative feed ingredients. Mol 25: 4680. https://doi.org/10.3390/molecules25204680. doi: 10.3390/molecules25204680
    [145] Apata DF, Babalola TO (2012) The use of cassava, sweet potato and cocoyam, and their by–products by non–ruminants. Int J Food Sci Nutr Eng 2: 54–62. https://www.researchgate.net/publication/233532871_The_Use_of_Cassava_Sweet_Potato_and_Cocoyam_and_Their_By-Products_by_Non_-_Ruminants
    [146] Okike I, Wigboldus S, Samireddipalle A, et al. (2022) Turning waste to wealth: harnessing the potential of cassava peels for nutritious animal feed. In: Thiele G, Friedmann M, Campos H, Polar V, Bentley JW. editors. Root, Tuber and Banana Food System Innovations. Springer Cham, 173–206. https://doi.org/10.1007/978-3-030-92022-7
    [147] Marin ME, Zajul M, Goldman M, et al. (2020) Effects of solid–state fermentation and the potential use of cassava by–products as fermented food. Waste Biomass Valor 11: 1289–1299 https://doi.org/10.1007/s12649-018-0479-3 doi: 10.1007/s12649-018-0479-3
    [148] de Souza AP, da Silva PGP, de Souza AS, et al. (2020) Changes in biochemical composition of cassava and beet residues during solid state bioprocess with Pleurotus ostreatus. Biocatal Agric Biotechnol 26: 1–8. https://doi.org/10.1016/j.bcab.2020.101641 doi: 10.1016/j.bcab.2020.101641
    [149] Morm S, Lunpha A, Pilajun R, et al. (2023) Gas kinetics, rumen characteristics, and in vitro degradability of varied levels of dried and fresh cassava leaf top fermented with cassava pulp. Tropical Anim Sci J 46: 105–111. https://doi.org/10.5398/tasj.2023.46.1.105 doi: 10.5398/tasj.2023.46.1.105
    [150] Contino–Esquijerosa Y, Herrera–González R, Ojeda–García F, et al. (2017) Evaluación del comportamiento productivo en cerdos en crecimiento alimentados con una dieta no convencional. Pastos y Forrajes 40: 152–157. http://scielo.sld.cu/scielo.php?pid = S0864-03942017000200009 & script = sci_arttext & tlng = pt
    [151] Williams GA, Akinola OS, Adeleye TM, et al. (2023) Processed cassava peel–leaf blends: effect on performance, carcass yield, organ weights and ileal microflora of growing pigs. Anim Prod Sci 63: 751–760. https://doi.org/10.1071/AN22101 doi: 10.1071/AN22101
    [152] Adiaha MS (2017) Potential of cassava peel as a biotechnical nutrient carrier for organic fertilizer production to increase crop production and soil fertility. World Sci News 72: 103–107. https://www.infona.pl/resource/bwmeta1.element.psjd-7cbacb46-5426-45a3-9103-e81f1933267b
    [153] Syamala C, Kuzhivilayil SJ, Nair MM, et al. (2017) Management of cassava starch factory solid waste (thippi) through composting to a nutrient-rich organic manure. Commun Soil Sci Plant Anal 48: 595–607. http://dx.doi.org/10.1080/00103624.2016.1243700 doi: 10.1080/00103624.2016.1243700
    [154] Makinde EA, Salau AW (2017) Fortified cassava peel compost amendment for Amaranthus: influence on plant growth, nutrients uptake and on soil nutrient changes. J Plant Nutr 40: 645–655. https://doi.org/10.1080/01904167.2016.1245328 doi: 10.1080/01904167.2016.1245328
    [155] Nguefack J, Onguene D, Lekagne JD, et al. (2022) Effect of aqueous extract of clove basil (Ocimum gratissimum L.) and soil amendment with cassava peels compost on nutrients, pesticide residues, yield and antioxidant properties of sweet pepper (Capsicum annuum L.). Sci Hortic 295: 110872. https://doi.org/10.1016/j.scienta.2021.110872
    [156] Oo AN, Iwai CB, Saenjan P (2015) Soil properties and maize growth in saline and nonsaline soils using cassava–industrial waste compost and vermicompost with or without earthworms. L Degrad Dev 26: 300–310. https://doi.org/10.1002/ldr.2208 doi: 10.1002/ldr.2208
    [157] Bezerra MGDS, da Silva GG, Difante GDS, et al. (2019) Chemical attributes of soil under cassava wastewater application in Marandugrass cultivation. Revista Brasileira de Engenharia Agrícola e Ambiental 23: 579–585. https://doi.org/10.1590/1807-1929/agriambi.v23n8p579-585 doi: 10.1590/1807-1929/agriambi.v23n8p579-585
    [158] Ghimire A, Frunzo L, Pirozzi F, et al. (2015) A review on dark fermentative biohydrogen production from organic biomass: process parameters and use of by-products. Applied Energy 144: 73–95. DOI: 10.1016/j.apenergy.2015.01.045 doi: 10.1016/j.apenergy.2015.01.045
    [159] Zanatta ER, Reinehr TO, Awadallak JA, et al. (2016) Kinetic studies of thermal decomposition of sugarcane bagasse and cassava bagasse. J Therm Anal Calorim 125: 437–445. https://doi.org/10.1007/s10973-016-5378-x doi: 10.1007/s10973-016-5378-x
    [160] Cruz G, Rodríguez ADLP, da Silva DF, et al. (2020) Physical–chemical characterization and thermal behavior of cassava harvest waste for application in thermochemical processes. J Therm Anal Calorim 143: 1–12. https://doi.org/10.1007/s10973-020-09330-6 doi: 10.1007/s10973-020-09330-6
    [161] Aruwajoye GS, Sewsynker–Sukai Y, Kana EG (2020b) Valorisation of cassava peels through simultaneous saccharification and ethanol production: Effect of prehydrolysis time, kinetic assessment and preliminary scale up. Fuel 278: 118351. https://doi.org/10.1016/j.fuel.2020.118351
    [162] García-Velásquez CA, Daza L, Cardona CA (2020) Economic and energy valorization of cassava stalks as feedstock for ethanol and electricity production. BioEnergy Res 13: 810–823. https://doi.org/10.1007/s12155-020-10098-8 doi: 10.1007/s12155-020-10098-8
    [163] Kumar B, Bhardwaj N, Agrawal K, et al. (2020) Bioethanol production: generation–based comparative status measurements. In: Srivastava, N., Srivastava, M., Mishra, P., Gupta, V. editors. Biofuel production technologies: critical analysis for sustainability. Clean Energy Prod Technol Springer, Singapore. p. 155–201. https://doi.org/10.1007/978-981-13-8637-4_7
    [164] Jusakulvijit P, Bezama A, Thrän D (2021) Availability and assessment of potential agricultural residues for the regional development of second-generation bioethanol in Thailand. Waste Biomass Valor 12: 6091–6118. https://doi.org/10.1007/s12649-021-01424-y doi: 10.1007/s12649-021-01424-y
    [165] Adeleke KM, Itabiyi OE, Ilori OO (2018) Temperature effect on the product yield from pyrolysis of cassava peels. Int J Sci Eng Res 9: 953. https://www.ijser.org/researchpaper/Temperature-Effect-on-the-Products-Yield-from-Pyrolysis-of-Cassava-Peels.pdf
    [166] Budzianowski WM (2017) High–value low–volume bioproducts coupled to bioenergies with potential to enhance business development of sustainable biorefineries. Renew Sustain Energy Rev 70(December 2016): 793–804. https://doi.org/10.1016/j.rser.2016.11.260 doi: 10.1016/j.rser.2016.11.260
    [167] Rodrigues ADLP, Sousa AVS, Braz CEM, et al. (2018) Physical–chemical and thermal characterization of cassava harvest residues for application in combustion and pyrolysis processes. In: X Congreso Nacional de Engenharia Mecánica, 20 al 24 de mayo 2018. https://scholar.google.com.mx/scholar?hl = es & as_sdt = 0%2C5 & as_vis = 1 & q = Physical-chemical+and+thermal+characterization+of+cassava+harvest+residues+ & btnG =
    [168] Ray RC (2024) Roots, Tubers, and Bulb Crop Wastes: Management by Biorefinery Approaches Springer Singapore 1: 1–374. https://doi.org/10.1007/978-981-99-8266-0
    [169] Escaramboni B, Núñez EGF, Carvalho AFA, et al. (2018) Ethanol biosynthesis by fast hydrolysis of cassava bagasse using fungal amylases produced in optimized conditions. Ind Crops Prod 112: 368–377. https://doi.org/10.1016/j.indcrop.2017.12.004 doi: 10.1016/j.indcrop.2017.12.004
    [170] Ayutthaya PPN, Charoenrat T, Krusong W, et al. (2019) Repeated cultures of Saccharomyces cerevisiae SC90 to tolerate inhibitors generated during cassava processing waste hydrolysis for bioethanol production. 3 Biotech 9: 1–13. https://doi.org/10.1007/s13205-019-1607-x
    [171] Polachini TC, Fachin L, Betiol L, et al. (2016) Water adsorption isotherms and thermodynamic properties of cassava bagasse. Thermochim Acta 632: 79–85. https://doi.org/10.1016/j.tca.2016.03.032 doi: 10.1016/j.tca.2016.03.032
    [172] Hasselmann VI, Lisboa MGL, Pereira FS, et al. (2018) Cassava pulp enzymatic hydrolysate as a promising feedstock for ethanol production. Braz Arch Biol Technol 61: 1–10. https://doi.org/10.1590/1678-4324-2018161214 doi: 10.1590/1678-4324-2018161214
    [173] Huang J, Du Y, Bao T, et al. (2019) Production of n–butanol from cassava bagasse hydrolysate by engineered Clostridium tyrobutyricum overexpressing adhE2: kinetics and cost analysis. Bioresour Technol 292: 1–7. 121969. https://doi.org/10.1016/j.biortech.2019.121969 doi: 10.1016/j.biortech.2019.121969
    [174] Madadi M, Wang Y, Xu C, et al. (2021) Using Amaranthus green proteins as universal biosurfactant and biosorbent for effective enzymatic degradation of diverse lignocellulose residues and efficient multiple trace metals remediation of farming lands. J Hazard Mater 406: 124727. https://doi.org/10.1016/j.jhazmat.2020.124727 doi: 10.1016/j.jhazmat.2020.124727
    [175] Lyu H, Zhang J, Zhou J, et al. (2019) The byproduct-organic acids strengthened pretreatment of cassava straw: Optimization and kinetic study. Bioresource techn 290: 121756. https://doi.org/10.1016/j.biortech.2019.121756 doi: 10.1016/j.biortech.2019.121756
    [176] Olaniyan AM, Olawale TT, Alabi KP, et al. (2017) Design, construction and testing of a biogas reactor for production of biogas using cassava peel and cow dung as biomass. Arid Zone J Eng Technol Environ 13: 478–488. https://www.researchgate.net/publication/320842514_Design_Construction_and_Testing_of_a_Biogas_Reactor_for_Production_of_Biogas_using_Cassava_Peel_and_Cow_Dung_as_Biomass
    [177] Varongchayakul S, Songkasiri W, Chaiprasert P (2021) Optimization of cassava pulp pretreatment by liquid hot water for biomethane production. BioEner Res 14: 1312–1327. https://doi.org/10.1007/s12155-020-10238-0 doi: 10.1007/s12155-020-10238-0
    [178] Kabir G, Hameed BH (2017) Recent progress on catalytic pyrolysis of lignocellulosic biomass to high–grade bio–oil and bio–chemicals. Renew and Sustain Energy Rev 70(December 2016): 945–967. https://doi.org/10.1016/j.rser.2016.12.001 doi: 10.1016/j.rser.2016.12.001
    [179] Rueangsan K, Suwapaet N, Pattiya A (2018) Bio–oil production by fast pyrolysis of cassava residues in a free–fall reactor using liquid media–assisted condensation. Energy Sour Part A: Recovery Utilization Environ Eff 40: 615–622. https://doi.org/10.1080/15567036.2018.1440874 doi: 10.1080/15567036.2018.1440874
    [180] Wu J, Yang J, Huang G, et al. (2020) Hydrothermal carbonization synthesis of cassava slag biochar with excellent adsorption performance for Rhodamine B. J Clean Prod 251: 119717. https://doi.org/10.1016/j.jclepro.2019.119717 doi: 10.1016/j.jclepro.2019.119717
    [181] OECD/FAO (2022) OECD-FAO Agricultural Outlook 2022-2031, OECD Publishing, Paris, https://doi.org/10.1787/f1b0b29c-en
    [182] Ohimain EI, Silas–Olu DI, Zipamoh JT (2013) Biowastes generation by small scale cassava processing centres in Wilberforce Island, Bayelsa State, Nigeria. Gr J of Environ Manag and Public Saf 2: 51–59. https://www.researchgate.net/publication/236247754_Biowastes_Generation_by_Small_Scale_Cassava_Processing_Centres_in_Wilberforce_Island_Bayelsa_State_Nigeria
    [183] Niyomvong N, Boondaeng A (2019) Ethanol production from cassava stem using Saccharomyces cerevisiae TISTR 5339 through simultaneous saccharification and fermentation. Agric Nat Resour 53: 667–673. https://li01.tci-thaijo.org/index.php/anres/article/view/232624
    [184] Ajala AS, Adeoye AO, Olaniyan SA, et al. (2020) A study on effect of fermentation conditions on citric acid production from cassava peels. Sci Afr 8: 1–6. https://doi.org/10.1016/j.sciaf.2020.e00396 doi: 10.1016/j.sciaf.2020.e00396
    [185] Rogoski W, Pereira GN, Cesca K, et al. (2023) An overview on pretreatments for the production of cassava peels-based xyloligosaccharides: State of art and challenges. Waste Biomass Valorization 14: 2115–2131. https://doi.org/10.1007/s12649-023-02044-4 doi: 10.1007/s12649-023-02044-4
    [186] He CW, Wei JH, Zeng LY, et al. (2020) Triterpenoids and flavonoids from cassava leaves. Chem Nat Compd 56: 331–333. https://doi.org/10.1007/s10600-020-03022-1 doi: 10.1007/s10600-020-03022-1
    [187] Abotbina W, Sapuan SM, Sultan MTH, et al. (2022) Extraction, characterization, and comparison of properties of cassava bagasse and black seed fibers. J Nat Fibers 19: 14525–14538. https://doi.org/10.1080/15440478.2022.2068103 doi: 10.1080/15440478.2022.2068103
    [188] Aisien FA, Amenaghawon AN, Bienose KC (2015) Particle boards produced from cassava stalks: Evaluation of physical and mechanical properties. S Afr J Sci 111: 1–4. http://dx.doi.org/10.17159/sajs.2015/20140042 doi: 10.17159/sajs.2015/20140042
    [189] Qiu Y, Wang F, Ma X, et al. (2023) Carbon quantum dots derived from cassava stems via acid/alkali-assisted hydrothermal carbonization: formation, mechanism and application in drug release. Ind Crops Prod 204: 117243. https://doi.org/10.1016/j.indcrop.2023.11724 doi: 10.1016/j.indcrop.2023.11724
    [190] Aisien FA, Amenaghawon AN, Onyekezine FD (2013) Roofing sheets produced from cassava stalks and corn cobs: evaluation of physical and mechanical properties. Int J Sci Res Knowl 1: 521–527. http://dx.doi.org/10.12983/ijsrk-2013-p521-527 doi: 10.12983/ijsrk-2013-p521-527
    [191] Bokanisereme UF, Okechukwu PN (2013) Anti–inflammatory, analgesic and anti–pyretic activity of cassava leaves extract. Asian J Pharm Clin Res 6: 89–92. https://www.academia.edu/es/50486819/Anti_Inflammatory_Analgesic_and_Anti_Pyretic_Activity_of_Cassava_Leaves_Extract
    [192] Abiaziem CV, Ojelade IA (2019) Cassava peel wax: its extraction and characterization. J Chem Bio Phys Sci 9: 316–322. http://eprints.federalpolyilaro.edu.ng/345/
    [193] Zhang C, Ali KRA, Wei H, et al. (2022) Rapid and mass production of biopesticide Trichoderma Brev T069 from cassava peels using newly established solid-state fermentation bioreactor system. J Environ manag 313: 114981. https://doi.org/10.1016/j.jenvman.2022.114981 doi: 10.1016/j.jenvman.2022.114981
    [194] Attahdaniel EB, Enwerem PO, Lawrence PG, et al. (2020) Green synthesis and characterization of sodium cyanide from cassava (Manihot esculenta Crantz). FUW Trends Sci Technol J 5: 247–251.
    [195] Roza L, Fauzia V, Rahman MYA, (2020) ZnO nanorods decorated with carbon nanodots and its metal doping as efficient photocatalyst for degradation of methyl blue solution. Optical Mater 109: 110360. https://doi.org/10.1016/j.optmat.2020.110360 doi: 10.1016/j.optmat.2020.110360
    [196] Adebisi JA, Agunsoye JO, Bello SA, et al. (2019) Extraction of silica from sugarcane bagasse, cassava periderm and maize stalk: Proximate analysis and physico-chemical properties of wastes. Waste Biomass Valor 10: 617–629. https://doi.org/10.1007/s12649-017-0089-5
    [197] Sopapan P, Laopaiboon R, Laopaiboon J, et al. (2020) Study of bagasse and cassava rhizome effects on the physical, mechanical and structural properties of soda–lime borate glasses. SN Appl Sci 2: 1–10. https://doi.org/10.1007/s42452-020-2721-4 doi: 10.1007/s42452-020-2721-4
    [198] Diabor E (2017) Isolation and characterization of cassava fibre for tissue engineering scaffold application[doctoral dissertation]. Legon (Ghana): University of Ghana, College of Basic and Applied Sciences, Department of Biomedical engineering. 139 p. https://inis.iaea.org/search/search.aspx?orig_q = RN: 52026537
  • This article has been cited by:

    1. Idris H. Smaili, Ghareeb Moustafa, Dhaifallah R. Almalawi, Ahmed Ginidi, Abdullah M. Shaheen, Hany S. E. Mansour, Ahmed Fathy, Enhanced Artificial Rabbits Algorithm Integrating Equilibrium Pool to Support PV Power Estimation via Module Parameter Identification, 2024, 2024, 0363-907X, 10.1155/2024/8913560
    2. Fukui Li, Hui Xu, Feng Qiu, Correction: Modified artificial rabbits optimization combined with bottlenose dolphin optimizer in feature selection of network intrusion detection, 2024, 32, 2688-1594, 4515, 10.3934/era.2024204
    3. Ferzat Anka, Nazim Agaoglu, Sajjad Nematzadeh, Mahsa Torkamanian-afshar, Farhad Soleimanian Gharehchopogh, Advances in Artificial Rabbits Optimization: A Comprehensive Review, 2024, 1134-3060, 10.1007/s11831-024-10202-7
    4. Hui Xu, Longtan Bai, Wei Huang, An optimization-inspired intrusion detection model for software-defined networking, 2025, 33, 2688-1594, 231, 10.3934/era.20250012
    5. Hui Xu, Longtan Bai, Wei Huang, An optimization-inspired intrusion detection model for software-defined networking, 2025, 33, 2688-1594, 231, 10.3934/era.2025012
    6. Emre Tokgoz, 2025, Artificial Bee Colony Optimization Techniques’ Utilization for Intrusion Detection Systems’ Analysis, 979-8-3315-1888-2, 1, 10.1109/ICAIC63015.2025.10848880
    7. Kaike Tu, Jiatang Cheng, Enhanced dung beetle optimization algorithm and its application in 3D UAV path planning, 2025, 33, 2688-1594, 2618, 10.3934/era.2025117
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3734) PDF downloads(247) Cited by(1)

Figures and Tables

Figures(5)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog