Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach by Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi, Masoud Kaveh

“…To enhance SEE, we introduce a hybrid deep-learning (DL) framework that integrates convolutional neural networks (CNN) and long short-term memory (LSTM) networks for joint EE and security optimization. …”
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Inequalities in Mild Cognitive Impairment Risk Among Chinese Middle-Aged and Older Adults: Insights from an Integrated Learning Model by Bi S, Guo D, Tan H, Chen Y, Li G

“…Shengxian Bi,1 Dandan Guo,2 Huawei Tan,1 Yingchun Chen,1 Gang Li3 1School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China; 2School of Public Health and Health Sciences, Hubei University of Medicine, Shiyan, Hubei, 442000, People’s Republic of China; 3School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of ChinaCorrespondence: Yingchun Chen, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China, Email chenyingchunhust@163.com Gang Li, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China, Email ligang2024@sjtu.edu.cnObjective: This study aims to address inequalities in mild cognitive impairment (MCI) risk among Chinese middle-aged and older adults by developing an integrated learning framework to predict MCI risk and identify key contributing factors.Methods: Using CHARLS data of 4626 participants, we developed a convolutional neural network-bidirectional long short-term memory-attention (CNN-BiLSTM-Attention) model to capture the temporal and spatial features of MCI progression. …”
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Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification by Eshika Jain, Pratham Kaushik, Vinay Kukreja, Modafar Ati, Shanmugasundaram Hariharan, Vandana Ahuja, Abhishek Bhattacherjee, Rajesh Kumar Kaushal

“…Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. …”
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Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River by N. Dong, H. Hao, H. Hao, M. Yang, J. Wei, S. Xu, H. Kunstmann, H. Kunstmann, H. Kunstmann

“…We propose a deep-learning-based modelling framework for sub-seasonal joint precipitation and streamflow ensemble forecasts for a lead time of up to 30 d. …”
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TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…This convolutional operation is embedded following standard convolution to mitigate the loss of detailed features. …”
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Deep cascaded registration and weakly-supervised segmentation of fetal brain MRI by Valentin Comte, Mireia Alenya, Andrea Urru, Judith Recober, Ayako Nakaki, Francesca Crovetto, Oscar Camara, Eduard Gratacós, Elisenda Eixarch, Fatima Crispi, Gemma Piella, Mario Ceresa, Miguel A. González Ballester

“…To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks. …”
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Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review by ShiYing Shen, Wenhao Qi, Jianwen Zeng, Sixie Li, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Yankai Shi, Jiani Yao, Bingsheng Wang, Xiajing Lou, Simin Gu, Pan Li, Jinghua Wang, Guowei Jiang, Shihua Cao

“…Deep learning models (eg, convolutional neural networks and long short-term memory) showed high accuracy, while traditional ML (eg, random forest) remained prevalent due to better interpretability. …”
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An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries by Arun Jose, Sonam Shrivastava

“…This research specifically examines the potential of the convolutional neural network–long short-term memory algorithm to improve the precision of State of Health forecasts for the battery model. …”
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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 study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). …”
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A two phase ensembled deep learning approach of prominent gene extraction and disease risk prediction by Prajna Paramita DEBATA, Alakananda TRIPATHY, Pournamasi PARHI, Smruti Rekha DAS

“…Therefore, a two phase ensembled deep learning approach can be considered as a dependable framework for the root level investigation of genomic data. …”
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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques by Wishal Arshad, Tehreem Masood, H. M. Shahzad, Hassan A. Ahmed, Syed Hamza Ahmed, Hafiz Muhammad Tayyab Khushi

“…This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches (<inline-formula> <tex-math notation="LaTeX">$50\times 50$ </tex-math></inline-formula> pixels) from Paul Mooney&#x2019;s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. …”
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Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Sultan Ahmad, Alhasan A. Alharbi, Mohammed Alarfaj, Mohammed Alarfaj, Mukta Dhopeshwarkar, Theyazn H. H. Aldhyani

“…The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. …”
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Acoustic cues for person identification using cough sounds by Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

“…Methods: We collected a custom dataset from 19 subjects and developed a lightweight yet effective deep learning framework for CPID. The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. …”
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GDS-YOLOv7: A High-Performance Model for Water-Surface Obstacle Detection Using Optimized Receptive Field and Attention Mechanisms by Xu Yang, Lei Huang, Fuyang Ke, Chao Liu, Ruixue Yang, Shicheng Xie

“…To address the challenges of navigation and obstacle detection on the water’s surface, this paper presents CDS-YOLOv7, an enhanced obstacle-detection framework for aquatic environments, architecturally evolved from YOLOv7. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…Additionally, a convolutional neural network model is designed to detect deepfake images with two distinct architectures, namely, ResNet50 and DenseNet121. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.…”
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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…To maximize the efficiency of solar energy systems and allow for prompt maintenance, our suggested framework provides a practical and dependable method for detecting anomalies in PV cells in real-time. …”
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Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network by Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen

“…Secondly, establish a financial flow data forecasting framework using MVE²-STFN. Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. …”
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OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation by Naveenkumar G. Venkataswamy, Poorna Ravi, Stephanie Schuckers, Masudul H. Imtiaz

“…A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. …”
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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. …”
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