Feature representation ‎via‎ graph-regularized ‎entropy-‎weighted nonnegative matrix factorization by Hazhir Sohrabi, Shahrokh Esmaeili, Parham Moradi

“…Feature extraction plays a crucial role in dimensionality reduction in machine learning applications. Nonnegative Matrix Factorization (NMF) has emerged as a powerful technique for dimensionality reduction; however, its equal treatment of all features may limit accuracy. …”
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Research on Fault Diagnosis of UAV Rotor Motor Bearings Based on WPT-CEEMD-CNN-LSTM by Xianyi Shang, Wei Li, Fang Yuan, Haifeng Zhi, Zhilong Gao, Min Guo, Bo Xin

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Amplification of Higher-Order Salivary Gland Volume Effects from External Beam Radiotherapy in Normal Tissue Complication Probability Modeling of Radiopharmaceutical Therapy by Chunming Gu, Robert F. Hobbs, Ana P. Kiess, Junghoon Lee, Todd McNutt, Harry Quon, Zhuoyao Xin, Tahir I. Yusufaly

“…The goal of this work is to combine machine learning of EBRT dose–outcome data with stylized small-scale RPT dosimetry to discover more reliable normal tissue complication probability (NTCP) models of xerostomia across both modalities. …”
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Climate change promotes shifts of summer maize yield and water productivity in the Weihe River Basin: A regionalization study based on a distributed crop model by Wenxin Xie, Hui Ran, Anni Deng, Kunhao Jiang, Han Ru, Ning Yao, Jianqiang He, Tehseen Javed, Xiaotao Hu

“…Machine learning quantified the relative importance of key meteorological factors influencing spatial variations in yield and WP. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant women using advanced machine learning techniques. The study employs a systematic methodology including data collection, preprocessing, feature selection, and model implementation. …”
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Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li, Huihui Wang

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Smooth Guided Adversarial Fully Test-Time Adaptation by Dong Li, Panfei Yang

“…Fully test-time adaptation (FTTA) refers to a specific type of domain adaptation that involves adjusting a pre-trained machine learning model to work with a new target domain, without accessing any data from the source domain. …”
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A digitizing craftsmanship: a comparative study of traditional vs. SDF-based shoe last making using 3D scanning and computer numerical control technologies by G. Surya, Anbarasan P., B. Suresh

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Risk Model for Predicting Gaps in Surgical Oncology Care Among Patients With Stage I-III Rectal Cancer From KwaZulu-Natal, South Africa by Yoshan Moodley, Willie Brink, Jacqueline van Wyk, Shakeel Kader, Steven D. Wexner, Alfred I. Neugut, Ravi P. Kiran

“…A supervised logistic regression machine learning algorithm was used to train and test an appropriate risk model, which was translated into a nomogram. …”
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Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer by I-Chen Wu, Yen-Chun Chen, Riya Karmakar, Arvind Mukundan, Gahiga Gabriel, Chih-Chiang Wang, Hsiang-Chen Wang

“…Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI’s clinical utility in HNC diagnostics. …”
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Quantized Convolutional Neural Networks Robustness under Perturbation [version 1; peer review: 2 approved] by Guy Kember, Issam Hammad, Jack Langille

“…Contemporary machine learning models are increasingly becoming restricted by size and subsequent operations per forward pass, demanding increasing compute requirements. …”
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Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review by Arun Nair, Wilson Ong, Aric Lee, Naomi Wenxin Leow, Andrew Makmur, Yong Han Ting, You Jun Lee, Shao Jin Ong, Jonathan Jiong Hao Tan, Naresh Kumar, James Thomas Patrick Decourcy Hallinan

“…A few studies also explored machine learning-based automation software and, more recently, large language models. …”
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Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…Abstract Addressing the challenges of high scheduling costs and low efficiency in the collaborative operations of agricultural machinery across multiple dispatch centers, this paper develops a scheduling model designed to minimize total costs. It introduces a Multi-Center and Multi-Machine Path Planning Algorithm Based on Deep Reinforcement Learning (MCMPP-DRL). …”
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Screening benzimidazole derivatives for atypical antipsychotic activity by K. Yu. Kalitin, O. Yu. Mukha, V. B. Voynov

“…The antipsychotic activity of the most promising compound was assessed in vivo using tests with apomorphine in rats and mice.Results. Machine learning models were developed and tested to predict the antipsychotic activity of benzimidazole derivatives. …”
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ASSESSMENT OF TRANSMISSION EFFECTS BETWEEN “CORRUPTION-DIGITIZATION-ECONOMIC GROWTH” by Adriana Surovičová, Victoria Bozhenko, Anton Boyko, K.Yu. Petrenko

“… The last decade has seen the rapid development of digital information technology, the intellectualization of control systems, the increase in the number and capacity of mobile and computer devices, and the accumulation of large amounts of data and its processing through machine learning algorithms, which inevitably leads to new opportunities for economic development. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…Floorplan synthesis is the process of generating new, realistic floor plans for buildings and homes using machine learning and generative models. In recent years, various generative methods, including GANs and diffusion models, have been utilized for the task of floorplan generation, demonstrating promising advancements in architectural design and planning. …”
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AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus by Anna Zenonos, Sizhuo Li, Martin Brandt, Jean Sciare, Philippe Ciais

“…Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. Subsequently, a 3D model is established by incorporating tunnel face features and environmental data. …”
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Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection by Ibrahim Mutambik, Abdullah Almuqrin

“…In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. …”
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Deep Pseudogene Categorization and Genome-Wide Transcription Prediction Using GANP-Based Feature Selection and TabNet Interpretability by Zeeshan Ahmed, Kashif Munir, Muhammad Usama Tanveer, Syed Rizwan Hassan, Ateeq Ur Rehman, Habib Hamam

“…Tested on a large-scale curated transcriptomic dataset, our framework achieves an accuracy of 96%, surpassing traditional machine learning models. Visualization tools such as t-SNE, heatmaps, and SHAP plots further enhance model interpretability. …”
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