Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme by Philip Held, Ashby Boland, Sarah A. Pridgen, Dale L. Smith

“…PTSD severity, depression, posttrauma cognitions, neurobehavioral symptoms). Machine learning models (neural net, random forest, elastic net, and ensemble) were trained to classify participants with clinically meaningful insomnia improvements.Results: Veterans reported large average PTSD severity reductions (d = 0.96), whereas depression and insomnia symptoms reduced moderately (d = 0.57) and modestly (d = 0.34), respectively. …”
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Improved Deviation Sparse Fuzzy C-Means-2D Cumulative Sum Average Filter and Modified Sine Cosine Crow Search Algorithm-Wavelet Extreme Learning Machine for Brain Tumor Detection a... by Suvashisa Dash, Satyasis Mishra, Mohammed Siddique, Demissie J. Gelmecha, Ram Sewak Singh

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Flight-Safe Inference: SVD-Compressed LSTM Acceleration for Real-Time UAV Engine Monitoring Using Custom FPGA Hardware Architecture by Sreevalliputhuru Siri Priya, Penneru Shaswathi Sanjana, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, Archana Pallakonda, Christian Napoli, Cristian Randieri

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A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…Results highlight that ML-based approaches, particularly the Transformer model, achieve computational time reductions of up to 49% compared to traditional SMCS methods while maintaining comparable accuracy. …”
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CNC machining data repository: Geometry, NC code & high-frequency energy consumption data for aluminum and plastic machiningMendeley Data by Markus Brillinger, Muaaz Abdul Hadi, Stefan Trabesinger, Johannes Schmid, Florian Lackner

“…Potential use cases include optimizing machining parameters for energy reduction based on power consumption patterns, and enhancing digital twin models with real-world machining data. …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

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Enhancing power transformer health assessment through dimensional reduction and ensemble approaches in Dissolved Gas Analysis by Abdelmoumene Hechifa, Saurabh Dutta, Abdelaziz Lakehal, Hazlee Azil Illias, Arnaud Nanfak, Chouaib Labiod

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AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke by Ismail Ben Abdallah, Ismail Ben Abdallah, Yassine Bouteraa, Yassine Bouteraa, Ahmed Alotaibi, Ahmed Alotaibi

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Safe Switching Model-Free Value Iteration for General Nonlinear Systems by Timotei Lala

“…This subset of the state space is determined using single-class Support Vector Machine (SVM) classification. The method includes mechanisms for early instability detection and chattering reduction near switching surfaces. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…Experimental results demonstrate that the CNN–LSTM–SAM–Attention model significantly outperforms traditional machine learning approaches in terms of accuracy, precision, recall, F1 score, ROC–AUC, and PR–AUC. …”
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Understanding the environmental health implications of tourism on carbon emissions in China by Jinhua Shao, Sheng Fang, Meiling Zhao, Wanxin Qian, Cai Wang

“…In this study, we simulate the complex relationship between the tourism industry and carbon emissions in China using machine learning models. This study is the first to employ interpretable machine learning to analyze the impact of the tourism industry on carbon emissions in China. …”
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Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a‐Si:H at High Temperatures by Zhuo Chen, Yuejin Yuan, Yanzhou Wang, Penghua Ying, Shouhang Li, Cheng Shao, Wenyang Ding, Gang Zhang, Meng An

“…In this study, we developed a neuroevolution machine learning potential based on first‐principles calculations of energy, forces, and virial, which enables accurate modeling of interatomic interaction in both a‐Si:H and a‐Si systems. …”
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Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…This article elaborately contrasts long short-term memory (LSTM)-based models with traditional machine learning models, like support vector machines (SVM), random forest, and Naive Bayes classifiers. …”
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Artificial intelligence in food system: Innovative approach to minimizing food spoilage and food waste by Helen Onyeaka, Adenike Akinsemolu, Taghi Miri, Nnabueze Darlington Nnaji, Keru Duan, Gu Pang, Phemelo Tamasiga, Samran Khalid, Zainab T. Al-Sharify, Chinenye Ugwa

“…This paper examines the deployment of AI technologies such as machine learning models, predictive analytics, and advanced algorithm in predicting food spoilage with high accuracy, thereby reducing food waste substantially. …”
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Advancing Smart City Sustainability Through Artificial Intelligence, Digital Twin and Blockchain Solutions by Ivica Lukić, Mirko Köhler, Zdravko Krpić, Miljenko Švarcmajer

“…This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). …”
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Methodics and tools of cough sound processing on basic of neural net by U. A. Vishniakou, Bahaa Shaya

“…To recognize COVID-19 cough, a classifier was analyzed using CNN as a machine learning model. The proposed CNN system is designed to classify and detect cough sounds based on ESC-50. …”
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Advancing Scholarship Management: A Blockchain-Enhanced Platform With Privacy-Secure Identities and AI-Driven Recommendations by Tu-Anh Nguyen-Hoang, Ngoc Cu Hoang, Phu Thien Hua, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Khoa Tan-Vo, Ngoc-Thanh Dinh, Hong-Tri Nguyen

“…Besides, the machine learning model achieved a good performance rating, achieving a balanced accuracy of 86.75% and a mean average precision of 91.68% on a realistically imbalanced test set, reflecting real-world conditions.…”
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Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions by Iraj Rahimi, Lia Duarte, Wafa Barkhoda, Ana Cláudia Teodoro

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Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification by Gita Indah Hapsari, Rendy Munadi, Bayu Erfianto, Indrarini Dyah Irawati

“…The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. …”
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Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis by Ziyang Li, Hong Wang, Jianing Song, Jiale Gong

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