AlphaGo-driven generative machine learning framework for inverse topology synthesis and optimization of planar antennas

Hybrid fusion of E-nose and computer vision using optimized deep learning and machine learning for robust plant leaf recognition

Pouya Bohlol, Mohammad Hasan Sabet Dizavandi, Syed Saeid Mohtasebi, Mahmoud Omid

Abstract The fusion multi-sensory system with optimized deep learning and machine learning algorithms appeared to synergize difficult paradigms in precision agriculture and boost recognition of various plant species. In this study, an electronic nose (E-nose) system with eight MO…Read more

A machine learning–based optimal charging strategy for PV-assisted electric vehicle systems incorporating second-life batteries under degradation constraints

Ahmed Ezzat, Ayman S. Abdel-Khalik, Ragi A. Hamdy, Mostafa S. Hamad

Abstract The rapid expansion of electric vehicles (EVs) and residential photovoltaic (PV) systems has created new challenges in battery charging management, particularly due to the variability of renewable energy, grid limitations, and battery aging effects. In this work, we pres…Read more

A unified machine learning framework for intelligent resource allocation toward 6G wireless communications

Nishu Gupta, Rupali Bhartiya, Seema Babusing Rathod, Asma Alshuhail, Shakila Basheer, Mohammad Hassan Alhefdi

An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification

Pratik Chakraborty, P. B. Shanthi

Abstract DNA functional group classification across species plays a crucial role in understanding genetic diversity, evolutionary relationships and biological function. The increasing availability of genomic data has led to the use of machine learning and deep learning methods fo…Read more

ProCDNet: prostate cancer detection network using quantum machine learning with enhanced addax optimization

M. R. Prathap, K. S. Vairavel, C. Kumar, Abdullah Alwabli

Optimization-enhanced machine failure classification using critical sensor features and hybrid learning models with advanced optimization techniques

Jinqin Tang, Junling Chai, Jinkun Dai, Jicheng Feng, Ruiqing Tian, Jijing Mei, et al.