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The detection of disease development in plants becomes very crucial because of its adverse effect on the quality and productivity of agriculture. The automatic disease detection in plants using image processing and machine learning is beneficial due to its fast computing and practicability for continuous monitoring of a large farm. This paper presents an automatic disease detection system using image segmentation, feature extraction, optimization, and classification algorithms. This paper proposes a memetic salp swarm optimization algorithm (MSSOA), which is transformed into binary MSSOA to search for the optimal number of features that give the best classification accuracy. The performance of the proposed algorithm for feature selection is compared with five metaheuristic feature selection (BSSA, BPSO, BMFO, BCOA, IBHHO) algorithms against the UCI benchmark datasets. The obtained results indicate the proposed algorithm outperforms the other algorithms in obtaining good classification accuracy and reducing the feature size. The proposed algorithm is implemented for automatic disease detection of maize, rice, and grape plant and achieved a classification accuracy of 90.6%, 67.9%, and 91.6% and best classification accuracy of 93.6%, 79.1%, and 95%, respectively.

Wireless sensor networks (WSNs) and Internet of Things (IoT) are essential for numerous applications. WSN nodes often operate on limited battery capacity, so energy efficiency is a significant problem for clustering and routing. In addition to these limitations, one of the primary issues of WSNs is achieving reliability and security of transmitted data in vulnerable environments to prevent malicious node attacks. This work aims to develop a secure and energy-efficient routing protocol for fault data prediction to enhance WSNs network lifespan and data reliability. The proposed technique has three major phases: cluster construction, optimal route selection, and intrusion detection. The adaptive shark smell optimization (ASSO) technique was initially used with three input parameters for CH selection. These parameters are the residual energy, the distance to the BS, and the node density. After clustering, salp swarm optimization (SSO) is used to select the optimum path for data transmission between clusters, resulting in an energy-efficient WSN. Finally, to ensure the security of cluster-based WSNs, an effective intrusion detection system based on a modified Elman recurrent neural network (MERNN) is implemented to detect the presence of intrusions in the network. The experimental results show that it outperforms the competing methods in various performance metrics. The performance results of quality of service (QoS) parameters are expressed as dispersion value (0.8072), packet delivery rate (98%), average delay (160 ms), network lifetime (3200 rounds), and the accuracy of this method is 99.2%. Compared to the SVM, ELM, HMM, and MK-ELM protocols, the proposed protocol increases network lifetime by 77%, 60%, 45.4%, and 14.2%, respectively.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP[sup.2]-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP[sup.2]-RSA attains a 96.123% security level.

Real-world optimization problems, such as global optimization, cleaner production system, and complex design challenges are inherently complex, involving many variables and constraints. These factors make it challenging for optimizers to determine optimal solutions efficiently. Salp Swarm Algorithm (SSA) adapts easily to complex optimization problems due to its simplicity, multi-search strategy, and few control parameters. However, its search strategy lacks precision in guiding the population toward optimal regions of the solution space, which limits its effectiveness in optimizing cleaner production systems and complex design problems. This study proposes an evolutionary SSA (ESSA) to address complex optimization problems. ESSA proposes distinct innovative search strategies, including two evolutionary search strategies that enhance diversity and adaptive search, as well as an enhanced SSA search strategy that, while less exploratory, ensures steady convergence. ESSA introduces an advanced memory mechanism that stores the best and inferior solutions identified during optimization, enhancing diversity and preventing premature convergence. Moreover, it incorporates a stochastic universal selection method to regulate the archive by selecting individuals according to their fitness values. The performance of ESSA was evaluated using benchmark functions CEC 2017 and CEC 2020, compared to seven leading algorithms. Results show that ESSA outperforms SSA and others in solution quality and convergence speed. Statistical analyses confirm that ESSA ranks first and achieves the best optimization effectiveness, with values of 84.48%, 96.55%, and 89.66% for dimensions 30, 50, and 100, respectively, surpassing other optimizers. Additionally, ESSA’s practical applicability is demonstrated through its success in optimizing a cleaner production system and solving complex design problems, highlighting its effectiveness in tackling challenging optimization tasks.

Meta-heuristic algorithms are among the technologies that have good performance in multilevel threshold image segmentation by obtaining optimal thresholds. However, most studies in the literature consider either a single objective function or images of a single type or low threshold levels, due to the drawbacks of poor ability to balance global and local search, premature convergence in high dimension, or low convergence efficiency of existing work in handling multi-task image segmentation. This paper aims to address these drawbacks and to develop search mechanisms and an enhanced optimizer for multilevel threshold image segmentation considering simultaneously different objective functions, both grayscale and color images, and both low and high threshold levels. More precisely, to improve the capability of balancing between global exploration and local exploitation, firstly a novel search mechanism ASSM inspired by the salp swarm optimization algorithm (SSA) is proposed, which is shown to have universality in improving a class of swarm intelligence optimization algorithms called DP-algorithms. Then, by proposing hierarchical vertical-horizontal search (HVHS) strategy and combining it with improved circle chaotic mapping initialization, lens opposition-based learning, and Lévy flight strategy, a multi-strategy collaborative ENCOA framework is constructed to prevent premature convergence in high-dimensional solution space. To evaluate the performance of the ENCOA, comparison experiments are implemented on CEC2017 benchmark suite and four engineering problems. Finally, the ENCOA is applied to multilevel threshold image segmentation on 6 grayscale images and 4 color images, by taking both Kapur’s entropy and Otsu between-class variance as the objective functions, and under threshold levels ranging from 4 to 32. It is shown that the ENCOA outperforms other recent-related algorithms in terms of both convergence accuracy and segmentation quality, especially when dealing with high threshold segmentation.

Owing to the superior transient and steady-state performance of the fractional-order proportional-integral-derivative (FOPID) controller over its conventional counterpart, this paper exploited its application in an automatic voltage regulator (AVR) system. Since the FOPID controller contains two more control parameters (µ and λ ) as compared to the conventional PID controller, its tuning process was comparatively more complex. Thus, the intelligence of one of the most recently developed metaheuristic algorithms, called the salp swarm optimization algorithm (SSA), was utilized to select the optimized parameters of the FOPID controller in order to achieve the optimal dynamic response and enhanced stability of the studied AVR system. To validate the effectiveness of the proposed method, its performance was compared with that of the recently used tuning methods for the same system configuration and operating conditions. Furthermore, a stability analysis was carried out using pole-zero and bode stability criteria. Finally, in order to check the robustness of the developed system against the system parameter variations, a robustness analysis of the developed system was undertaken. The results show that the proposed SSA-based FOPID tuning method for the AVR system outperformed its conventional counterparts in terms of dynamic response and stability measures.

Energy-potent routing protocols are vital for extending lifetime and energy stability in Wireless Sensor Networks (WSNs) as they comprise of numerous tiny sensor nodes with limited battery-powered energy. Clustering is a significant strategy that is commonly used for balancing energy consumption among energy-restricted sensor nodes with minimized overhead and traffic during data transmission. In particular, using a hybrid Swarm Intelligence (SI) metaheuristic algorithm for clustering and Cluster Head (CH) selection are considered to be significant for improving network longevity. In this paper, an Improved Salp Swarm and Bare Bones mayfly Optimization Algorithm (ISSBBMFOA)-based CH selection, along with sink node mobility scheme is proposed for improving network longevity. This algorithm specifically uses Improved Salp Swarm Optimization Algorithm (ISSOA) for identifying potential CH nodes in the network. This selection of CHs completely depends on the evaluation of fitness factors such as load balancing, mean inter and intra-cluster distances, distance from the sink and nodes’ Residual Energy (RE). It adopts Bare Bones Mayfly Optimization Algorithm (BBMFOA) for determining movement trajectory and location of sink corresponding to each cluster, following clustering of network regions involving optimal clusters. It facilitates moving sink to stop at optimal locations and aggregate data from sensor member nodes of associated clusters. The simulation results of the proposed ISSBBMFOA scheme confirm 23.21% improved throughput, 24.84% better sustained alive nodes and 22.62% enhanced network lifetime in contrast to other CH selection schemes considered for investigation.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.

The islanded mode of the microgrid (MG) operation faces more power quality challenges as compared to grid-tied mode. Unlike the grid-tied MG operation, where the voltage magnitude and frequency of the power system are regulated by the utility grid, islanded mode does not share any connection with the utility grid. Hence, a proper control architecture of islanded MG is essential to control the voltage and frequency, including the power quality and optimal transient response during different operating conditions. Therefore, this study proposes an intelligent and robust controller for islanded MG, which can accomplish the above-mentioned tasks with the optimal transient response and power quality. The proposed controller utilizes the droop control in addition to the back to back proportional plus integral (PI) regulator-based voltage and current controllers in order to accomplish the mentioned control objectives efficiently. Furthermore, the intelligence of the one of the most modern soft computational optimization algorithms called salp swarm optimization algorithm (SSA) is utilized to select the best combination of the PI gains (kp and ki) and dc side capacitance (C), which in turn ensures optimal transient response during the distributed generator (DG) insertion and load change conditions. Finally, to evaluate the effectiveness of the proposed control approach, its outcomes are compared with that of the previous approaches used in recent literature on basis of transient response measures, quality of solution and power quality. The results prove the superiority of the proposed control scheme over that of the particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA) based MG controllers for the same operating conditions and system configuration.

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.