Generating Synthetic Datasets with Deep Learning Models for Human Physical Fatigue Analysis by Arsalan Lambay, Ying Liu, Phillip Morgan, Ze Ji

“…To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. …”
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Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2 by Gustav Müller-Franzes, Firas Khader, Robert Siepmann, Tianyu Han, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn

“…MST combines a Transformer architecture with a 2D feature extractor, i.e., DINOv2. We evaluate its diagnostic performance against a 3D convolutional neural network (3D ResNet) across three clinical datasets: breast MRI (651 patients), chest CT (722 patients), and knee MRI (1199 patients). …”
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Toward AI-Driven Cough Sound Analysis for Respiratory Disease Diagnosis by Houda Benaliouche, Houda Hafi, Hakim Bendjenna, Zeyad Alshaikh

“…We methodically evaluate different model architectures, ranging from custom-built networks to pre-trained deep models, applying spectrogram or Mel-Frequency Cepstral Coefficients (MFCC) in transfer learning-based feature extraction, to determine which is the best approach in terms of accuracy, precision, recall, F1-score, and loss. …”
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Application of Vis/NIR Spectroscopy in the Rapid and Non-Destructive Prediction of Soluble Solid Content in Milk Jujubes by Yinhai Yang, Shibang Ma, Feiyang Qi, Feiyue Wang, Hubo Xu

“…Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. …”
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Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning by Arvind Prasad, Wael Mohammad Alenazy, Naved Ahmad, Gauhar Ali, Hanaa A. Abdallah, Sadique Ahmad

“…Abstract With IoT networks expected to exceed 29 billion connected devices by 2030, the risk of cyberattacks has never been higher. …”
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Detection of disease on nasal breath sound by new lightweight architecture: Using COVID-19 as an example by Jiayuan She, Lin Shi, Peiqi Li, Ziling Dong, Renxing Li, Shengkai Li, Liping Gu, Zhao Tong, Zhuochang Yang, Yajie Ji, Liang Feng, Jiangang Chen

“…Objective This study aims to develop a novel, lightweight deep neural network for efficient, accurate, and cost-effective detection of COVID-19 using a nasal breathing audio data collected via smartphones. …”
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ESLC-YOLOv8: Advancing real-time pineapple recognition with lightweight deep learning by Weihua Shen, Mengyao Dong, Zhaoxin Zhang, Xiaying Hao, Yuzhen Su, Zhong Xue

“…First, we propose the EIEStem module to enhance the backbone network's convolutional layers, significantly improving edge feature extraction and spatial information preservation. …”
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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. …”
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The impact of performing arts on mental health, social connection, and creativity in university students: a Randomised Controlled Trial by Kat R. Agres, Yifan Chen

“…We introduce a participatory arts programme, Movin’ and Groovin’ for Wellness (MGW), that features facilitated drumming and dancing sessions. …”
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Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy. by Andres Portocarrero-Bonifaz, Salman Syed, Maxwell Kassel, Grant W McKenzie, Vishwa M Shah, Bryce M Forry, Jeremy T Gaskins, Keith T Sowards, Thulasi Babitha Avula, Adrianna Masters, Jose G Schneider, Scott R Silva

“…Seven supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, Multi-Layer Perceptron Neural Networks, and XGBoost) were constructed and evaluated. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

“…Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. Along with these methods, due to the capability to process data with highly nonlinear dependences between features, machine learning methods such as the k-nearest neighbor method, and the support vector machine (SVM) method, are widely used in related areas. …”
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A segment-based framework for explainability in animal affective computing by Tali Boneh-Shitrit, Lauren Finka, Daniel S. Mills, Stelio P. Luna, Emanuella Dalla Costa, Anna Zamansky, Annika Bremhorst

“…Saliency maps are among the most widely used methods for explainability, where each pixel is assigned a significance level indicating its relevance to the neural network’s decision. Although these maps are frequently used in research, they are predominantly applied qualitatively, with limited methods for quantitatively analyzing them or identifying the most suitable method for a specific task. …”
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Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity by Nasser Shoeibi, Majid Abrishami, Seyedeh Maryam Hosseini, Mohammad-Reza Ansari-Astaneh, Razieh Farrahi, Bahareh Gharib, Fatemeh Neghabi, Mojtaba Abrishami, Mehdi Sakhaee, Mehrdad Motamed Shariati

“…Among the machine learning models evaluated, the XGBoost and ANN models achieved the highest accuracy at 96%. …”
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γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders by Zhong-Peng Dai, Qiang Wen, Ping Wu, Yan-Ni Zhang, Cai-Lian Fang, Meng-Yuan Dai, Hong-Liang Zhou, Huan Wang, Hao Tang, Si-Qi Zhang, Xiao-Kun Li, Jian-Song Ji, Liu-Xi Chu, Zhou-Guang Wang

“…We investigate how monitoring dynamic features of γ oscillations allows for detailed evaluations of neuromodulation effectiveness. …”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. …”
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An improved deep learning approach for automated detection of multiclass eye diseases by Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana, Tchiotsop Daniel

“…The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. …”
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Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication by Bowen Jia, Wenmin Ge, Jingxuan Cheng, Zihao Du, Renming Wang, Guangbin Song, Yufan Zhang, Chengye Cai, Sitong Qin, Jing Xu

“…In this paper, deep neural networks (DNNs) with strong feature extraction capabilities are introduced to automatically learn the patterns of the light source from diverse images. …”
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Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course by Sujan Ghimire, Shahab Abdulla, Lionel P. Joseph, Salvin Prasad, Angela Murphy, Aruna Devi, Prabal Datta Barua, Ravinesh C. Deo, Rajendra Acharya, Zaher Mundher Yaseen

“…This study introduces an interpretable hybrid model, optimised through Tree-structured Parzen Estimation (TPE) and Support Vector Regression (SVR), to predict overall scores (OT) utilising five assignments and one examination mark as predictors. Neural Network-based, Tree-Based, Ensemble-Based, and Boosting-based methods are evaluated against the hybrid TPE-optimised SVR model for forecasting final examination grades among 492 students enrolled in the TPP7155 (General Science) course at the University of Southern Queensland, Australia, during the 2020-2021 academic year. …”
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Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods by Hongran Li, Nuo Wang, Zixuan Du, Deyu Huang, Mengjie Shi, Zhaoman Zhong, Dongqing Yuan

“…This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. …”
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Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration by Francesco Saverio Sorrentino, Marco Zeppieri, Carola Culiersi, Antonio Florido, Katia De Nadai, Ginevra Giovanna Adamo, Marco Pellegrini, Francesco Nasini, Chiara Vivarelli, Marco Mura, Francesco Parmeggiani

“…Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. …”
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