International Journal of Computational Intelligence and Applications

Metaheuristic algorithms are increasingly used for feature selection (FS) problems due to their robustness and searchability. This study presents a unique optimization technique called fuzzy bluefin trevally optimizer (BTO). BTO draws its inspiration from bluefin’s cooperative nature and sudden ambush behavior. The trevally will jump out of the water and attack its food in the air or even snag it off the water when it is sufficiently close to it. This study uses mathematical modeling to create an optimization algorithm to replicate dynamic patterns and behaviors. Since parameters play a critical role in optimization, we use the fuzzy concept for dynamic parameter adaptation in fuzzy BTO. By introducing the idea of signature and demonstrating that fuzzy BTO converges to the global optimal point, the mathematical basis for the proposed algorithm is provided. The convergence of fuzzy BTO is proved using the Markov chain property. On benchmark functions (BF), the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then the proposed algorithms are applied to a fuzzy FS problem. Then they are compared with traditional and recent metaheuristic algorithms on the UCI machine learning repository datasets. Finally, the statistical significance is examined using the Kruskal–Wallis test (KWT).

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the $\mathrm{\text{FCM}}+\mathrm{\text{LSTM}}$ algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the $\mathrm{\text{FCM}}+\mathrm{\text{LSTM}}$ algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13min and 18s for training on corn leaf disease images, while the prediction process only took 1.59s. The training and prediction time required for the $\mathrm{\text{FCM}}+\mathrm{\text{LSTM}}$ algorithm were 65min and 24s and 5min and 44s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to $\mathrm{\text{FCM}}+\mathrm{\text{LSTM}}$ on the dataset used in this study in terms of corn leaf disease detection.

A neural network architecture is constructed to solve Convex Multi-objective Programming Problem (CMPP). By using the weighted sum method, a single-objective optimization problem related to the CMPP is formulated, in which the scalar objective is summation of weighted original objective functions. The Pareto Optimal Solution (POS) is given by using different values of weights. A neural network framework is then designed for solving the obtained convex problem. Based on employing Lyapunov theory, the proposed model is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the convex programming problem with different values of weights. The computer simulations shows the feasibility and the efficiency of the suggested model.

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members’ influence and leader impact. In this paper, a model has been proposed to compute members’ influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders’ judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms.

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

The quick emergence in the quantity of data produced through the linked devices of Internet of Things (IoT) models opened the novel potential to improve service qualities for budding tools considering data sharing. However, privacy problems are main issues of data providers for sharing data. The outflow of confidential data causes severe problems beyond the loss in finance of providers. A blockchain-based secured data-sharing model is devised for dealing with various kinds of parties. Thus, data-sharing issue is modeled as a machine learning issue by adapting federated learning (FL). Here, data privacy is controlled by sharing data in spite of exposing genuine data. At last, the FL is combined in consensus task of permissioned blockchain for accomplishing federated training. Here, the data model learning is executed using a deep maxout network (DMN), which is trained using jellyfish search African vultures optimization (JSAVO). Moreover, the data-sharing records are generated to share data amid data providers and requestors. The proposed JSAVO-based DMN outperformed with better accuracy of 93.3%, FPR of 0.054, loss function of 0.067, mean square error (MSE) of 0.346, mean average precision of 94.6, RMSE of 0.589, computational time of 17.47s, and memory usage of 48.62MB.