Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy by Nicharee Wisuthiphaet, Huanle Zhang, Xin Liu, Nitin Nitin

“…To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. …”
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A comprehensive review of deepfakes in medical imaging: Ethical concerns, detection techniques and future directions by Pradepan P, Gladston Raj S, Juby George K

“…This paper discusses how deepfakes are created and highlights their potential for research and education, as well as risks such as misdiagnosis and data manipulation. We also review various deepfake detection techniques, ranging from traditional image forensics to advanced deep learning models, and highlight the strengths and weaknesses of these approaches for detecting sophisticated deepfakes. …”
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A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets by Shengteng Jiang, Xiaoyuan Ren, Canyu Wang, Libing Jiang, Zhuang Wang

“…The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.…”
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AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system by C. Rajathi, P. Rukmani

“…However, the vast amount of  network data requires an efficient processing approach. Among various studies from existing literature, Machine Learning (ML) algorithms provide a viable solution to detecting intrusion in voluminous data. …”
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A Web-Based Interface That Leverages Machine Learning to Assess an Individual’s Vulnerability to Brain Stroke by Divyansh Bhandari, Arnav Agarwal, R. Reena Roy, Rajaram Priyatharshini, Rodriguez Rivero Cristian

“…We compare a range of algorithms-including traditional classifiers and deep learning models-and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. …”
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Rolling window for detecting multiple Chan signatures to diagnose excessive water production by Ahmed MohamedSalih Musa Hamdoon, A. P. Mohammed Abdalla Ayoub Mohammed, A. P. Khaled Abdalla Elraies

“…Chan identified various distinct water problem patterns exhibited by wells during their production lifecycle based on slope features: Constant water-oil ratio (WOR), Normal Displacement, Coning, Channeling, depletion, and Multilayer Channeling. While machine learning models were developed to automate Chan plot interpretation and reduce human bias, they often failed to capture the evolution of water production over time, typically detecting only a single pattern or capturing patterns after it fully materialized, reducing the chance of early diagnosing. …”
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Enhancing credit card fraud detection: the impact of oversampling rates and ensemble methods with diverse feature selection by Mohamed Akouhar, Abdallah Abarda, Mohamed El Fatini, Mohamed Ouhssini

“…Additionally, they developed an ensemble model that integrates seven feature selection methods with the eXtreme Gradient Boosting (XGB) algorithm. The results show that the application of SMOTE significantly improves the performance of the machine learning models, with an optimal oversampling rate of 20% identified. …”
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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

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Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services by Nuzaiha Mohamed, Reem Lafi Almutairi, Sayda Abdelrahim, Randa Alharbi, Fahad M. Alhomayani, Amer Alsulami, Salem Alkhalaf

“…So, recent work has utilized machine learning and deep learning methodologies for predicting CVD on clinical datasets. …”
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The Diagnostic Classification of the Pathological Image Using Computer Vision by Yasunari Matsuzaka, Ryu Yashiro

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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

“…Crucially, AI outputs require clinician verification alongside multidimensional clinical data.DiscussionFuture research must prioritize algorithm optimization, data quality improvement, and enhanced AI transparency. …”
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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

“…The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. …”
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The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses by Liu Jun, Hongguo Li, Yu Mao, Lan Hu, Dan Wu

“…While the role of ADL as a potential biomarker for SI remains unclear, this study aims to provide new evidence supporting ADL as an early predictor of SI through statistical analysis and machine learning validation.MethodsData were derived from the 2018 CHARLS national baseline survey, comprising 10,136 participants aged 45 and above. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…MethodsWe devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. …”
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Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century by Arfan Arshad, Muhammad Shafeeque, Thanh Nhan Duc Tran, Ali Mirchi, Zaichen Xiang, Cenlin He, Amir AghaKouchak, Jessica Besnier, Md Masudur Rahman

“…This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). …”
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MobileNetV3: an efficient deep learning-based feature selection and classification technique for cardiovascular disease by B. Dhanalaxmi, B. Naveen Kumar, Yeligeti Raju, Rama Seshagiri Rao Channapragada

“…Unfortunately, most currently utilized cardiovascular disease prediction algorithms could not achieve higher accuracy due to inadequate forecasting methodology and data-recognized techniques. …”
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Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches by Dipak Kumar Agrawal, Watcharin Jongpinit, Soodkhet Pojprapai, Wipawee Usaha, Pattra Wattanapan, Pornthep Tangkanjanavelukul, Timporn Vitoonpong

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Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis by Sarah Arzani, Parisa Soltani, Ali Karimi, Maryam Yazdi, Ashraf Ayoub, Zohaib Khurshid, Domenico Galderisi, Hugh Devlin

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Development of a Machine‐Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy by Sonia Hermoso‐Durán, Nicolas Fraunhoffer, Judith Millastre‐Bocos, Oscar Sanchez‐Gracia, Pablo F. Garrido, Sonia Vega, Ángel Lanas, Juan Iovanna, Adrián Velázquez‐Campoy, Olga Abian

“…The application of ML to serum TLB data emerges as a promising  methodology for early diagnosis, representing a significant advance for detecting and managing PDAC, envisaging a minimally invasive and more efficient methodology for identifying biomarkers.…”
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