In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.

In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.

The central remote servers are essential for storing and processing data for cloud computing evaluation. However, traditional systems need to improve their ability to provide technical data security solutions. Many data security challenges and complexities await technical solutions in today's fast-growing technology. These complexities will not be resolved by combining all secure encryption techniques. Quantum computing efficiently evolves composite algorithms, allowing for natural advances in cyber security, forensics, artificial intelligence, and machine learning-based complex systems. It also demonstrates solutions to many challenging problems in cloud computing security. This study proposes a user-storage-transit-server authentication process model based on secure keys data distribution and mathematical post-quantum cryptography methodology. The post-quantum cryptography mathematical algorithm is used in this study to involve the quantum computing-based distribution of security keys. It provides security scenarios and technical options for securing data in transit, storage, user, and server modes. Post-quantum cryptography has defined and included the mathematical algorithm in generating the distributed security key and the data in transit, on-storage, and on-editing. It has involved reversible computations on many different numbers by super positioning the qubits to provide quantum services and other product-based cloud-online access used to process the end-user's artificial intelligence-based hardware service components. This study will help researchers and industry experts prepare specific scenarios for synchronizing data with medicine, finance, engineering, and banking cloud servers. The proposed methodology is implemented with single-tenant, multi-tenant, and cloud-tenant-level servers and a database server. This model is designed for four enterprises with 245 users, and it employs integration parity rules that are implemented using salting techniques. The experimental scenario considers the plain text size ranging from 24 to 8248 for analyzing secure key data distribution, key generation, encryption, and decryption time variations. The key generation and encryption time variations are 2.3233 ms to 8.7277 ms at quantum-level 1 and 0.0355 ms to 1.8491 ms at quantum-level 2. The key generation and decryption time variations are 2.1533 ms to 19.4799 ms at quantum-level 1 and 0.0525 ms to 3.3513 ms at quantum-level 2.

Unmanned Aerial Vehicles (UAVs) are enabled to be fast and flexible in managing traffic compared to the conventional methods. However, in emergencies, this system takes more time to identify and clear the traffic because of fixed time control. To overcome this problem, an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network (CNN) along with re-routing algorithm to provide the safest alternate route from current position to destination, in a heavy traffic environment. The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions. The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.