Avinash Kaur

Introduction: Unmanned aerial vehicle (UAV) swarms are increasingly employed in civilian and military applications. Efficient path planning is critical for navigating dynamic environments, yet challenges such as collision avoidance, energy optimization, and multiagent coordination hinder deployment. Methods: A systematic literature review (SLR) was conducted to classify UAV swarm pathplanning algorithms into four categories: sampling-based, graph-based, biologically inspired, and mathematical model-based approaches. The review adhered to established SLR guidelines, employing systematic database searches and predefined inclusion and exclusion criteria. Each algorithm was evaluated in terms of computational efficiency, adaptability, and application suitability. Results: Biologically inspired algorithms demonstrated high adaptability and flexibility, whereas graph-based approaches were effective in structured environments. Sampling-based methods proved scalable in high-dimensional spaces, and mathematical models emphasized optimization. Persistent challenges include real-time adaptability, energy efficiency, and operations in interactive environments. Discussion: The comparative analysis highlights the trade-offs inherent in each approach and underscores the unresolved challenges that limit practical implementation. Addressing these gaps is essential to advance the reliability and applicability of UAV swarms in real-world scenarios. Conclusion: This review systematically evaluates UAV swarm path-planning algorithms, identifying their respective advantages, limitations, and application contexts. The findings provide a reference framework to inform future research directions aimed at enhancing UAV swarm autonomy and operational efficiency.

The Metaverse represents a transformative virtual environment with vast potential for innovation, societal engagement, and economic growth. However, its rapid expansion also brings pressing concerns regarding privacy and security. Key challenges include data breaches, identity theft, unauthorized surveillance, malicious activities, the absence of robust and standardized security frameworks, and other emerging threats in Metaverse Security. This review presents a systematic literature review (SLR) to explore and analyze these challenges comprehensively. We propose a taxonomy that categorizes Metaverse privacy and security challenges, synthesizing insights from current literature. Furthermore, we compare existing research efforts, identifying trends, gaps, and limitations in addressing these issues. Based on our findings, we provide actionable solutions, such as developing privacy-preserving technologies, secure authentication mechanisms, and interoperable security standards. Finally, the review outlines future research directions to mitigate emerging risks and foster a secure and privacy-centric Metaverse ecosystem. By offering a structured analysis and clear guidance, this work aims to support researchers and practitioners in tackling privacy and security challenges effectively, contributing to the sustainable development of the Metaverse.

The demand for electric vehicles (EVs) is influenced by the location of charging stations and the management of renewable energy production. Efficient control of renewable energy can optimize resource usage, cost reduction, and enhance operator’s profitability, whereas mismanagement adversely impacts economic and energy efficiency. This study proposed an optimal location management system for distributed charging stations with renewable energy. A reward mechanism has been designed to support the electrical vehicle charging station (EVCS) operators based on uncertain demand and location. The unexpected arrival of EV demand is catered with the proposed response model by considering the price, time, and charging. The Honey Badger Algorithm (HBA) is used for the optimal operation of the charging operator for EV response. The smart contract is deployed between the transmission and charging operators to decide incentives for handling congestion and voltage stability. The experiment results demonstrate that the proposed model can reduce the overproduction of variable renewable energy with proper location management of charging stations for EVs.

This paper presents a computational approach for quantifying soybean defects through seed classification using deep learning techniques. To differentiate between good and defective soybean seeds quickly and accurately, we introduce a lightweight soybean seed defect identification network (SSDINet). Initially, the labeled soybean seed dataset is developed and processed through the proposed seed contour detection (SCD) algorithm, which enhances the quality of soybean seed images and performs segmentation, followed by SSDINet. The classification network, SSDINet, consists of a convolutional neural network, depthwise convolution blocks, and squeeze-and-excitation blocks, making the network lightweight, faster, and more accurate than other state-of-the-art approaches. Experimental results demonstrate that SSDINet achieved the highest accuracy, of 98.64%, with 1.15 M parameters in 4.70 ms, surpassing existing state-of-the-art models. This research contributes to advancing deep learning techniques in agricultural applications and offers insights into the practical implementation of seed classification systems for quality control in the soybean industry.

Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance the classifier’s performance. Some features are less useful than others or do not correlate with the system’s evaluation, and their removal does not affect the system’s performance. In most cases, removing features with a monotonically decreasing impact on the system’s performance increases accuracy. Therefore, this research aims to propose a dimensionality reduction method using a feature selection technique to enhance accuracy. This paper proposes a novel feature-selection approach that combines filter and wrapper techniques to select optimal features using Mutual Information with the Sequential Forward Method and 10-fold cross-validation. Results show that the proposed algorithm can reduce features by more than 75% in datasets with large features and achieve a maximum accuracy of 97%. The algorithm outperforms or performs similarly to existing ones. The proposed algorithm could be a better option for classification problems with minimized features.