Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes by Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

“…Feature importance analysis highlighted BRI, creatinine, race, age, and HbA1c as key contributors to the model’s predictive performance. …”
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Analyzing mental stress in Indian students through advanced machine learning and wearable technologies by Shruti Gedam, Sandip Dutta, Ritesh Jha

“…Univariate feature analysis found that XGBoost regularly demonstrated good accuracy, showing its dependability for detecting mental stress. …”
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Enhanced Medical Image Classification Using LSA and PCA in CNN by Suhaifa S Thasneem, Mukthar Hussain S Faizal, R Karthikeyan, Yousuf T Sheik, Begum B Rasina, Uveise S A Mohammed

“…In this study, we present an enhanced approach that integrates Least Squares (LSA) alongside with Principal Component Analysis (PCA) within the Convolutional Neural Network (CNN) framework of deep learning to improve image processing and image resolution for medical diagnostics .Here LSA is employed to reduce the noise to the greater extent and to refine the feature for better clarity, while PCA employed in dimensionality reduction for efficient processing and preserving critical image details and at the same time CNN enables the automatic feature extraction and interpretation of image. …”
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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. …”
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MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix by Peng Chen, Fobao Liu, Jun Zhang, Bing Wang

“…The core of the framework is the MFEM-CIN hybrid model, which combines Convolutional Neural Networks (CNN) and Transformer to aggregate the correlation between local and global features. This combined analysis of local and global information is scientifically useful in clinical diagnosis. …”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…” Statistical testing validated the robustness of the technique, while sensitivity analysis identified optimal hyperparameter combinations. …”
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The Ion Formation and Quantitative Response of Isoprene, Monoterpenes and Terpenoids in Ion Mobility Spectrometry with Atmospheric-Pressure Chemical Ionization as a Function of Tem... by Thomas Mayer, Ralf Petrich, Helko Borsdorf

“…These substances are important target analytes in atmospheric monitoring and in the analysis of essential oils in different matrices. A drift tube temperature above 120 °C permitted the most sensitive detection of isoprene and monoterpenes, while 80 °C was sufficient for the sensitive detection of most terpenoids. …”
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Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach by Shokufeh Yaraghi, Toktam Khatibi

“…Objective Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysis Three pretrained models were used to extract features from the input images. …”
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Adaptive multi-scale phase-aware fusion network for EEG seizure recognition by Yanting Liang, Jingyuan Liu, Xinzhou Zhang

“…Electroencephalogram (EEG) analysis is the primary technique for detecting epileptic seizures, and accurate seizure detection is essential for clinical diagnosis, therapeutic intervention, and treatment planning. …”
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Multi-Vehicle Object Recognition Method Based on YOLOv7-W by Xin Liu, Jingbin Zhao, Zhi Zhang, Rongxing Wu, Xun Li

“…This algorithm effectively addresses three key challenges: high vehicle miss detection rates, inadequate perception of small-angle targets, and insufficient feature extraction capabilities. …”
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DeepSpoofNet: a framework for securing UAVs against GPS spoofing attacks by Aziz Ur Rehman Badar, Danish Mahmood, Adeel Iqbal, Sung Won Kim, Sedat Akleylek, Korhan Cengiz, Ali Nauman

“…These include imbalanced datasets, sub-optimal feature selection, and the accuracy of attack detection in resource-constrained environments. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…<b>Methods</b>: A retrospective analysis was conducted on patients who had undergone surgery for isolated tibial fractures. …”
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Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems by Morgane Suhas, Emmanuelle Abisset-Chavanne, Pierre-André Rey

“…The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based and data-driven models to improve fault detection and extrapolation to new usage profiles. …”
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Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…To address these critical environmental and health implications, this research delves into a comprehensive analysis of three cutting-edge object detection models: YOLOv5, YOLOv7, and YOLOv8. …”
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Leveraging remote sensing and machine learning for sustainable management of Hanoi’s Urban golf courses by Kim-Anh Nguyen, Yuei-An Liou, Tien-Dat Pham, Thuy Thi Phuong Vu, Minh Khanh Luong, Thanh Hoan Nguyen, Truong-Vinh Le

“…Through time-series multispectral data, we evaluate two detection methods: normalized difference vegetation index (NDVI) analysis and feature recognition. …”
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Toward Hand Gesture Recognition Using a Channel-Wise Cumulative Spike Train Image-Driven Model by Yang Yu, Zeyu Zhou, Yang Xu, Chen Chen, Weichao Guo, Xinjun Sheng

“…Additionally, we conducted an experiment involving 10 gestures and 10 subjects and compared the proposed method with 2 root-mean-square (RMS)-based approaches and a cw-CST-based approach, namely, RMS-image-driven convolutional neural network classification model, RMS feature with linear discrimination analysis classifier, and cw-CST discharge rate feature with linear discrimination analysis classifier. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Conclusion Our findings demonstrate that the fusion model, which integrates a convolutional neural network (CNN) with traditional machine learning and deep transfer learning techniques, can effectively differentiate between benign and malignant thyroid nodules through the analysis of ultrasound images. This model fusion approach significantly optimizes and enhances diagnostic performance, offering a robust and intelligent tool for the clinical detection of thyroid diseases.…”
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The Talent Cultivation Model of Study Travel Majors in Universities Based on the Internet of Things and Deep Learning by Yanjie Zhan

“…Moreover, the introduction of the sentiment analysis module enables the precise detection of students&#x2019; emotional changes during the learning process, achieving an accuracy rate of 0.91. …”
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Method for building segmentation and extraction from high-resolution remote sensing images based on improved YOLOv5ds. by Fangzhe Chang, Tianyue Ma, Dantong Wang, Shoujie Zhu, Dengping Li, Shuntian Feng, Xiaoyong Fan

“…The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. Due to these features, YOLOv5ds-RC can further enhance fully automated rapid extraction and historical change analysis in land use change monitoring.…”
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XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique by Arul Elango, Rene Jr. Landry

“…Different Machine learning (ML) techniques were employed to assess the importance of the features to the model’s prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. …”
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