Explainable machine learning identifies small high-performance gene signatures from full transcriptomes in human liver preservation cohorts

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

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

Machine learning–assisted performance prediction of graphene–silicon twin-port band-notched wideband antenna for THz 6G communication systems

Goutam Datta, Nagesh Kallollu Narayanaswamy, Asha Verma, Pooja Singh, Sreedhar Jadapalli, Neha K. Saini, et al.

Abstract A graphene–silicon-based twin-port terahertz (THz) antenna is proposed and investigated in this work. The antenna employs an aperture-coupled asymmetric ring dielectric resonator configuration to achieve wideband operation in the THz regime. Circular metallic rings integ…Read more

Enhancing human-dog interaction through deep learning and explainable AI

Michał Kopczyński, Michał Czubenko

Comparative machine learning analysis of energy and exergy performance in evacuated tube solar air heater with pierced twisted tape

A. Veerakumar, T. Karthickmunisamy, P. Manoj Kumar, S. Deepa, Dawit Tafesse Gebreyohannes

Exploring feature limitations in antimicrobial resistance prediction: machine learning and deep learning in A. baumannii

Zahra Seraj, Zahra Ghorbanali, Fatemeh Zare-Mirakabad, Bahareh Attaran, Sajjad Gharaghani