Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks.

Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is designated for multiple users, which aims to optimize the distribution of network resources. This network's main intent is to maximize system performance by improving energy efficiency. However, the users of MIMO need many resources for effective operation.

Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs.

Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs.

Resource management for multi-drone communications in next-generation NOMA-enabled wireless networks.

The communication network of Unmanned Aerial Drones (UAD) is expected to become a vital element in the development of next-generation wireless networks, offering flexible infrastructure that extends network coverage to remote or disaster-stricken locations while enhancing capacity during critical events and large-scale emergencies. As UAD technology evolves, its role in ensuring consistent, widespread connectivity becomes more essential, though it faces challenges such as high latency, low spectral efficiency, and fairness issues across multiple drones. This research presents an optimization framework designed for multi-UAD communication networks based on Non-Orthogonal Multiple Access (NOMA) to address these difficulties.

AI-enabled Barilai-Borwein-Blinder-Oaxaca-Bernoulli Deep Classifier for Enhanced Crop Yield Prediction.

This article explores the integration of advanced Artificial Intelligence (AI) enabled deep learning methods with accurate crop yield prediction. The objective of the work is to enhance the accuracy, sensitivity, and specificity of crop yield prediction. Also, false positive and false negative cases are minimized in crop yield prediction.

Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation.

Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion management enables drivers to bypass heavily trafficked areas and reducing pollutant emissions.