DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline by Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu

“…We developed a large, real-world data set of FRBs for training deep-learning models. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…Although performance of our model was not sufficient to be clinically implemented in our laboratory, our study provides a framework for AI-based AFB detection and a publicly available data set to support future advancements in automated detection of AFB.IMPORTANCEWe present the development of an artificial intelligence model to detect acid-fast bacilli (AFB) directly from stained clinical smears. …”
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Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time by Elhoseny Mohamed, Hassan Ahmed, Shehata Marwa H., Kayed Mohammed

“…When a snake bite occurs, the IoT sensors embedded in the wearable devices will immediately detect the bite and transmit real-time data, including vital information about the bite marks, to a central monitoring system or victim relative. …”
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Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems by Hoejun Jeong, Jihyun Kim, Doyun Jung, Jangwoo Kwon

“…DTW is applied to the magnitude data of the ex-core neutron noise signal obtained in the frequency domain, thereby enabling the effective learning of changes in sensor data values. …”
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A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment by Nwadher Suliman Al-Blihed, Dina M. Ibrahim

“…Analysis of the data generated by HAR devices may involve deep learning models and algorithms of different kinds. …”
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Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors by Artem Kozmin, Oleg Kalashev, Alexey Chernenko, Alexey Redyuk

“…Additionally, advanced signal processing algorithms are necessary for accurately determining the location and nature of detected events. …”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

“…We analyze how preprocessing techniques affect some of the most commonly used Machine Learning (ML) algorithms, namely K-means, SVM, and KNN, emphasizing their role in reducing computational load, energy consumption, and carbon emissions in data centers. …”
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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

“…The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. …”
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A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring by Daniel Krauss, Lukas Engel, Tabea Ott, Johanna Braunig, Robert Richer, Markus Gambietz, Nils Albrecht, Eva M. Hille, Ingrid Ullmann, Matthias Braun, Peter Dabrock, Alexander Kolpin, Anne D. Koelewijn, Bjoern M. Eskofier, Martin Vossiek

“…Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. …”
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Synergistic use of handcrafted and deep learning features for tomato leaf disease classification by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Abstract This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. …”
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Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning by Tianhao Li, Haoren Jing, Xinyu Gao, Te Zhang, Haitao Yao, Xipeng Zhang, Mingqing Zhang

“…Immune cell abundance quantification and statistical correlation analyses with IBD-associated transcripts were conducted via the CIBERSORTx deconvolution algorithm. To complement these findings, blood expression quantitative trait loci (eQTL) data from GTExv8.ALL.Whole_Blood were integrated with IBD genome-wide association statistics from the FinnGen consortium. …”
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Advanced genetic algorithm (GA)-independent component analysis (ICA) ensemble model for predicting trapped humans through hybrid dimensionality reduction by Enoch Adama Jiya, Ilesanmi B. Oluwafemi

“…Ultra-wideband (UWB) signal data, augmented by machine learning techniques, provides a large and quantified output that is useful for applications including engineering, scientific research, and Search and Rescue (SAR) operations. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

“…RNA-Seq was subsequently employed to analyze THP-1 cells, followed by differential expression gene (DEG) analysis and weighted gene co-expression net-work analysis (WGCNA). Using two data preprocessing methods and three feature extraction techniques, we constructed and validated models with eight machine learning algorithms. …”
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SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis by Geetika Munjal, Paarth Bhardwaj, Vaibhav Bhargava, Shivendra Singh, Nimish Nagpal

“…While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black‐box nature of many models limits interpretability, posing challenges for dermatologists. …”
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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering by Chao Chun Liu, William W. L. Hsiao

“…The models demonstrated high precision and recall on unseen test data with near-perfect accuracy in classifying clonal and short-term outbreaks. …”
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The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications by Tanja Belčič Mikič, Miha Arnol

“…In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. …”
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Machine learning–based feature prediction of convergence zones in ocean front environments by Weishuai Xu, Lei Zhang, Hua Wang

“…The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. …”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

“…Deep learning (DL) has recently been applied in attack detection because it can remove and learn deeper features of known attacks and identify unknown attacks by analyzing network traffic for anomalous patterns. …”
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