Non‐Invasive Diagnosis of Chronic Myocardial Infarction via Composite In‐Silico‐Human Data Learning by Rana Raza Mehdi, Nikhil Kadivar, Tanmay Mukherjee, Emilio A. Mendiola, Akila Bersali, Dipan J. Shah, George Karniadakis, Reza Avazmohammadi

“…Herein, a completely non‐invasive methodology is developed to identify the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. …”
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Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Asim Wadood, Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Safeer Khan, M. Irfan Uddin, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson’s Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. …”
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Few-Shot SAR ATR via Multilevel Contrastive Learning and Dependence Matrix-Based Measurement by Haoyue Tan, Zhenxi Zhang, Xiaoran Shi, Xinyao Yang, Yu Li, Xueru Bai, Feng Zhou

“…However, the performance of most deep learning-based models heavily depends on a large quantity of high-quality labeled data, which presents severe challenges in the collection and annotation of SAR images. …”
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A multi-attention deep learning network for intelligent identification of rock mass fracture in mines by Ning Li, Zihao Xiong, Liguan Wang, Bibo Dai, Shugang Zhao, Haiwang Ye, Qizhou Wang

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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…This article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. …”
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Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images by Rizwana Naz Asif, Muhammad Tahir Naseem, Munir Ahmad, Tehseen Mazhar, Muhammad Adnan Khan, Muhammad Amir Khan, Amal Al-Rasheed, Habib Hamam

“…Even though AI has been utilized in the past to detect brain tumors, current techniques still have issues with accuracy and dependability. Our study presents a novel AI technique that combines two distinct deep learning models to enhance this. …”
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The Use of Authentic Materials in English-medium Instructed Business Classes in Higher Education to Improve Learning by Bilel Elmotri, Mubarak Altwaiji, Mohamed Desoky Rabeh, Mustafa Ahmed Al-humari, Farid Amri, Yasir M. Elyasa, Muna Telha

“…Method: The study incorporates data collection methods such as student questionnaire copies, teacher interviews, classroom observations, and document surveys. The results were presented in tables, charts and pros format as appropriate. …”
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Student dropout prediction through machine learning optimization: insights from moodle log data by Markson Rebelo Marcolino, Thiago Reis Porto, Tiago Thompsen Primo, Rafael Targino, Vinicius Ramos, Emanuel Marques Queiroga, Roberto Munoz, Cristian Cechinel

“…Abstract Student attrition and academic failure remain pervasive challenges in education, often occurring at substantial rates and posing considerable difficulties for timely identification and intervention. Learning management systems such as Moodle generate extensive datasets reflecting student interactions and enrollment patterns, presenting opportunities for predictive analytics. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwave Synthetic Aperture Radar (SAR) backscatter observations over the wheat fields. …”
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Two-stage object detection in low-light environments using deep learning image enhancement by Ghaith Al-refai, Hisham Elmoaqet, Abdullah Al-Refai, Ahmad Alzu’bi, Tawfik Al-Hadhrami, Abedalrhman Alkhateeb

“…This study presents a two-stage object detection system specifically tailored for low-light conditions. …”
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Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables by Haiyan He, Zhoutao Li, Qian Qin, Yue Yu, Yuanxin Guo, Sheng Cai, Zhanming Li

“…It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. …”
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing by Krzysztof Pastuszak, Michał Sieczczyński, Marta Dzięgielewska, Rafał Wolniak, Agata Drewnowska, Marcel Korpal, Laura Zembrzuska, Anna Supernat, Anna J. Żaczek

“…Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. …”
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Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms by Changshou Wei, Maosheng Zhou, Zhixing Du, Lijing Han, Hao Gao

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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…Time series forecasting plays a crucial role in various industries, particularly in predicting product demand for effective supply chain management. This paper presents a novel approach to time series forecasting by leveraging advanced deep learning techniques, specifically focusing on hybrid models that combine attention mechanisms with traditional recurrent neural networks. …”
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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

“…Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. …”
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Diagnosis of bipolar disorder based on extracted significant biomarkers using bioinformatics and machine learning algorithms by Hamid Mohseni, Massoud Sokouti, Akram Nezhadi, Ali Sayadi

“…The best model of 20 genes in the artificial neural network showed an AUC of 72% and the best model of 10 genes in the decision tree model showed an AUC of 78%. Conclusion. We presented two models to diagnose bipolar disorder. …”
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Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…IntroductionThe increasing frequency of slope disasters in urban and recreational public spaces, driven by climate change, presents significant risks to public safety and sustainable urban design. …”
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Deep Learned Feature Technique for Human Action Recognition in the Military using Neural Network Classifier by Adeola O Kolawole, Martins E Irhebhude, Philip O Odion

“…This method used the VGG16 model to automatically extract deep learned features at the max-pooling layer and the input presented to neural network classifier for classification into the various classes of human actions achieving 90% recognition accuracy which is at training time of 104.91secs. …”
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Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center by Brittany V. Barber, Douglas Sinclair, Christine Cassidy

“…Coordinating organizational structures and processes for reducing health system emissions presents challenges. Learning health systems, defined as systems that seek to continuously generate and apply evidence, innovation, quality, and value in health care, can guide health systems with planning organizational structures and processes to advance environmentally sustainable healthcare. …”
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Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach by Dini Nurmalasari, Dini Hidayatul Qudsi, Nessa Chairani, Heri R Yuliantoro

“…While Naïve Bayes shows slightly lower accuracy at 98%, it maintains a high recall rate (100%), ensuring all negative feedback is captured. This research presents a novel approach combining user sentiment analysis with a comprehensive five-year dataset. …”
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