Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.

Sensors are now used by farmers and agronomists to help them improve their operations. They use sensor data transmitted via IoT to remotely monitor their crops. Farmers today manage crops in a controlled environment to increase yields in the name of modern farming. Crop productivity, on the other hand, is influenced by the severity of the weather and disease variations. The primary objective of this paper is to present a novel Multisensor Machine-Learning Approach (MMLA) for classifying multisensor data. The fusion strategy supports high-quality data analysis in agricultural contexts for cultivation recommendations. Based on the proposed recommendation system, eight crops were classified: cotton, gram, groundnut, maize, moong, paddy, sugarcane, and wheat. Crop species were classified using three machine learning algorithms: J48 Decision Tree, Hoeffding Tree, and Random Forest. To evaluate the performance of the proposed multi-text classifier, only the top eight classes were investigated. The classifier’s performance is measured in terms of precision, recall, F-measure, MCC, ROC Area, and PRC Area class, and the results are compared with the state-of-the-art classifiers. The Random forest algorithm has the lowest error measure of RMSE at 13%, RAE at 38.67%, and RRSE at 44.21%, demonstrating effectiveness in classifying the agriculture text. Thus, the use of a multisensor data fusion approach based on crop recommendation provides greater precision in prediction, resulting in a significant increase in crop yield while also creating awareness in the condition-based environmental monitoring system.

The computers nowadays are being replaced by the smartphones for the most of the internet users around the world, and Android is getting the most of the smartphone systems’ market. This rise of the usage of smartphones generally, and the Android system specifically, leads to a strong need to effectively secure Android, as the malware developers are targeting it with sophisticated and obfuscated malware applications. Consequently, a lot of studies were performed to propose a robust method to detect and classify android malicious software (malware). Some of them were effective, some were not; with accuracy below 90%, and some of them are being outdated; using datasets that became old containing applications for old versions of Android that are rarely used today. In this paper, a new method is proposed by using static analysis and gathering as most useful features of android applications as possible, along with two new proposed features, and then passing them to a functional API deep learning model we made. This method was implemented on a new and classified android application dataset, using 14079 malware and benign samples in total, with malware samples classified into four malware classes. Two major experiments with this dataset were implemented, one for malware detection with the dataset samples categorized into two classes as just malware and benign, the second one was made for malware detection and classification, using all the five classes of the dataset. As a result, our model overcomes the related works when using just two classes with F1-score of 99.5%. Also, high malware detection and classification performance was obtained by using the five classes, with F1-score of 97%.

Machine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel processing algorithms like Parallel Particle Swarm Optimization (PPSO) are parallelized to speed up the operation and reduce the processing time to train the neural network. Due to the arrival of a large number of incoming tasks in the cloud environment, load balancing is an important issue. To solve this problem, the datacenter controller or an agent makes an intelligent decision to handle a large number of tasks within a minimum time period or at high speed. In this work, we proposed an effective scheduling algorithm named Deep Reinforcement Learning with Parallel Particle Swarm Optimization (DRLPPSO) to solve the load balancing problem and its various parameters with greater accuracy and high speed. Our experimental results show that our proposed scheduling algorithm increases the reward by 15.7%, 12%, and 13.1% when the task set is 2000 and improves the reward by 17.5%, 12.6%, and 15.3% when the task set is 4000, as compared to the Modified Particle Swarm Optimization (MPSO), Asynchronous Advantage Actor-Critic (A3C), and Deep Q-Network (DQN) techniques.

Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors.

The Traveling Salesman Problem (TSP) is a combinatorial optimization problem that is useful in a number of applications. Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. Several variants of PSO have been proposed for solving discrete optimization problems like TSP. Among them, the basic algorithm is the Swap Sequence based PSO (SSPSO), however, it does not perform well in providing high quality solutions. To improve the performance of the swap sequence based PSO, this paper introduces an Enhanced Swap Sequence based PSO (Enhanced SSPSO) algorithm by integrating the strategies of the Expanded PSO (XPSO) in the swap sequence based PSO. This is because although XPSO is only suitable for solving continuous optimization problems, it has a high performance among the variants of PSO. In our work, the TSP problem is used to model a package delivery system in the Kuala Lumpur area. The problem set consists of 50 locations in Kuala Lumpur. Our aim is to find the shortest route in the delivery system by using the enhanced swap sequence based PSO. We evaluate the algorithm in terms of effectiveness and efficiency while solving TSP. To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. The proposed algorithm is found to provide better solutions with shorter paths when applied to TSP as compared to swap sequence based PSO. However, the swap sequence based PSO is found to converge faster than the proposed algorithm when applied to TSP.