Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks by Stuart M. Chesher, Carlo Martinotti, Dale W. Chapman, Simon M. Rosalie, Paula C. Charlton, Kevin J. Netto

“…<b>Background/Objectives</b>: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. …”
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Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optim... by Opy Das, Muhammad Hamza Zafar, Filippo Sanfilippo, Souman Rudra, Mohan Lal Kolhe

“…TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. …”
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Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea by Na Hyeon Yu, Daeun Shin, Ik Hee Ryu, Tae Keun Yoo, Kyungmin Koh

“…An easy-to-use oversampling function was employed to address class imbalance, enhancing the usability of the workflow. Various machine learning algorithms were trained by incorporating all features from the health check-up data in the development set. …”
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An Image Entropy Recurrence Map-Based Screening Method for Ultrasound B-Scan Image Segments Containing Defects by Peiqiang Chen, Qunli Lishen, Bin Tang, Chenlong Yang

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry by Bogdan Ruszczak, Krzysztof Kotowski, David Evans, Jakub Nalepa

“…The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms. They were evaluated using the training-test dataset split introduced in this work, and we suggest a set of quality metrics which should be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. …”
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A Highly Sensitive Strain Sensor with Self-Assembled MXene/Multi-Walled Carbon Nanotube Sliding Networks for Gesture Recognition by Fei Wang, Hongchen Yu, Xingyu Ma, Xue Lv, Yijian Liu, Hanning Wang, Zhicheng Wang, Da Chen

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Algorithm for continual monitoring of fog based on geostationary satellite imagery by B. Jahani, B. Jahani, B. Jahani, S. Karalus, J. Fuchs, J. Fuchs, T. Zech, M. Zara, M. Zara, J. Cermak, J. Cermak

“…Validation of the algorithm against the METAR data showed that the algorithm is well suited for the detection of FLS. …”
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Developing an Algorithm for Robotic Precision Application of Crop Protection Products by M. A. Mirzaev

“…(Research purpose) To develop an algorithm for crop plant recognition by a robotic device using a state-of-the-art convolutional neural network (R-CNN) and deep learning technology. …”
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Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis by Iyad Almadani, Aaron L. Robinson, Mohammed Abuhussein

“…Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. …”
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High‐accuracy dynamic gesture recognition: A universal and self‐adaptive deep‐learning‐assisted system leveraging high‐performance ionogels‐based strain sensors by Yuqiong Sun, Jinrong Huang, Yan Cheng, Jing Zhang, Yi Shi, Lijia Pan

“…More importantly, a self‐adaptive recognition program empowered by deep‐learning algorithms is designed to compensate for sensors, creating a comprehensive system capable of dynamic gesture recognition. …”
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IDEA: Image database for earthquake damage annotationZenodo by Ilaria Senaldi, Chiara Casarotti, Martina Mandirola, Alessio Cantoni

“…The dataset aims to fill the lack of annotated data necessary for the development of deep learning methodologies with structural damage detection and/or classification purposes. …”
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Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. …”
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Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach by Shahid Allah Bakhsh, Muhammad Shahbaz Khan, Oumaima Saidani, Nada Alasbali, S. Qasim Abbas, Muhammad Almas Khan, Jawad Ahmad

“…This study explores the potential for enhancing intrusion detection in DNP3 communications and the associated industrial control system traffic through the application of state-of-the-art deep learning (DL) algorithms. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

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Predictive diagnostics of computer systems logs using natural language processing techniques by Vladislav A. Kiriachek, Soltan I. Salpagarov

“…A comparative assessment of various anomaly detection algorithms was performed, including k-nearest neighbors, autoencoders, One Class SVM, Isolation Forest, Local Outlier Factor, and Elliptic Envelope. …”
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Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke by Yuqi Tang, Sixian Hu, Yipeng Xu, Linjia Wang, Yu Fang, Pei Yu, Yaning Liu, Jiangwei Shi, Junwen Guan, Ling Zhao

“…We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. …”
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SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification by Seema Yadav, Dhruvanshu Joshi, Soham Mulye, Sandeep S. Udmale, Girish P. Bhole

“…The majority class is often favored by conventional classifiers, which can lead to biases from improper oversampling or subpar performance when detecting instances of the minority class. Consequently, there is growing concern about algorithmic fairness. …”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
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Breast cancer image classification by using HCNN and LeNet5 by Pramoda Patro, Shaik Honey Fathima, R. Harikishore, Aditya Kumar Sahu

“…Additionally, the current research attempts to improve the computation time involved in the detection process. Therefore, an effective hybrid deep learning model is introduced to improve the prediction performance and reduce the time consumption compared to the machine learning model. …”
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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…While Deep learning algorithms address these challenges, however, they typically require significant computational resources, exhibit high complexity, and demand large amounts of training data. …”
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