The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review by Isabelle J. Museck, Daniel L. Brinton, Jesse C. Dean

“…Overall, this review can help guide the future development of ML algorithms and wearable sensor technologies to estimate postural mechanics.…”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. …”
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Study of machine learning techniques for outcome assessment of leptospirosis patients by Andreia Ferreira da Silva, Karla Figueiredo, Igor W. S. Falcão, Fernando A. R. Costa, Marcos César da Rocha Seruffo, Carla Cristina Guimarães de Moraes

“…In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. …”
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A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network by Shaobin Cai, Han Xu, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…Abstract Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. …”
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Identification and validation of biomarkers associated with glycolysis in polycystic ovarian syndrome by Rongyan Zhu, Xiao Yu, Yulan Li

“…Utilizing publicly available datasets, biomarkers were identified via differential analysis, various PPI algorithms, and validation of expression patterns. Subsequent analyses included functional enrichment, tissue and cell-specific expression profiling, m6A modification site prediction, compound screening, molecular network construction, and molecular docking. …”
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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System by M. Y. Shams, M. R. Darwesh, Roheet Bhatnagar, N. S. A. Al-Sattary, A. A. Salama, M. S. Ghoname

“…Artificial intelligence algorithms were employed to predict quality and calculate regression coefficients. …”
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Behavior Modeling and Bio-Hybrid Systems: Using Reinforcement Learning to Enhance Cyborg Cockroach in Bio-Inspired Swarm Robotics by Le Minh Triet, Nguyen Truong Thinh

“…Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms.…”
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Machine Learning-Based Carbon Compliance Forecasting and Energy Performance Assessment in Commercial Buildings by Aditya Ramnarayan, Felipe de Castro, Andres Sarmiento, Michael Ohadi

“…The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. …”
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Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…Thirdly, a Dynamic Motion Model (DMM) is developed, enabling the robust prediction of non-linear motion based on an improved Kalman filter framework. …”
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Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection by Syed Talha Abid Ali, Sebin Kim, Young-Joon Kim

“…To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). To enhance the interpretability of the model’s predictions, we applied SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. …”
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Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches by Guohua Li, Qian Shi, Qibiao Wu, Xinbing Sui

“…Recent advances in artificial intelligence (AI) have further enhanced the field by improving target prediction and streamlining data analysis. AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. …”
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Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. …”
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Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning by Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

“…They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. …”
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Allocation of Oral Cholera Vaccines in Africa by Elisa M. Maffioli, Yutong Lu

“…Using machine learning algorithms, we assess the relative importance of socio-demographic, governance, and weather variables in predicting cholera outbreaks. …”
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Using Digital Intelligence in Promoting Mechanism for Medical Care Insurance for Rare Diseases: Concepts and Applications by YANG Xinyu, ZHANG Yuzheng, WANG Shengfeng, GUO Wudong

“…By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. …”
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Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis by Yan Wang, Rufeng Wang, Jianzhong Zhu, Ling Chen

“…Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. …”
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Raising awareness of potential biases in medical machine learning: Experience from a Datathon. by Harry Hochheiser, Jesse Klug, Thomas Mathie, Tom J Pollard, Jesse D Raffa, Stephanie L Ballard, Evamarie A Conrad, Smitha Edakalavan, Allan Joseph, Nader Alnomasy, Sarah Nutman, Veronika Hill, Sumit Kapoor, Eddie Pérez Claudio, Olga V Kravchenko, Ruoting Li, Mehdi Nourelahi, Jenny Diaz, W Michael Taylor, Sydney R Rooney, Maeve Woeltje, Leo Anthony Celi, Christopher M Horvat

“…<h4>Objective</h4>To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”
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Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization by Lucio Assis Araujo Neto, Alessandra Maia Freire, Luciano Paulino Silva

“…<b>Background:</b> Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. …”
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Structural Magnetic Resonance Imaging Brain Age Investigation in Athletes with Persistent Postconcussion Syndrome by Samuel Guay, Camille Charlebois-Plante, Sophie-Andrée Vinet, Marie-Eve Bourassa, Louis De Beaumont

“…Brain age prediction algorithms using structural magnetic resonance imaging (MRI) estimate the biological age of the brain by comparing it to a normal aging trajectory, allowing for the identification of deviations that may indicate slower or accelerated biological aging. …”
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