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

CT imaging-based radiomics and deep learning models for predicting chemotherapy response in advanced pancreatic cancer

Zhu Y, H J Zhou, Peng An, Yingfan Mao, Ziwei Nie, Yi-Xiang Wang, et al.

To investigate the value of radiomics and deep learning features derived from pre-treatment CT imaging in predicting the efficacy of chemotherapy in patients with advanced pancreatic cancer. The retrospective study included 207 patients with advanced pancreatic cancer from two me…Read more

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

Development and external validation of machine learning models for predicting mortality in patients with lung cancer: a multicenter retrospective cohort study

Lihui Liu, Pan Zuo, Chunlan Yu, Xinhai Shen, Baoping Luo, Xicheng Zhang, et al.

Lung cancer remains the leading cause of cancer mortality worldwide. Accurate prognostic prediction can support clinical decision-making and resource allocation, yet many existing models use limited predictors and lack independent validation. We developed and externally validated…Read more

A comparative study of machine and deep learning models for time-series-based bearing fault diagnosis of induction motors

Kamal Hamani, Martin Kuchar, Martin Sobek, Vojtech Sotola, Petr Palacky

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