Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment by Lanlan Li, Yeying Yang, Qi Zhang, Jiao Wang, Jiehui Jiang, Alzheimer’s Disease Neuroimaging Initiative

“…Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. …”
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Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review by Zhenli Chen, Jie Hao, Haixia Sun, Min Li, Yuan Zhang, Qing Qian

“…Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. …”
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Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression by Sepehr Aghajanian, Fateme Mohammadifard, Ida Mohammadi, Shahryar Rajai Firouzabadi, Ali Baradaran Bagheri, Elham Moases Ghaffary, Omid Mirmosayyeb

“…Longitudinal analyses were performed by extracting deep convolutional neural network (CNN) embeddings and gray matter radiomics for each scan, which were put into a time-aware long short-term memory (LSTM) model with an attention mechanism. …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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A role for the thalamus in danger evoked awakening during sleep by Ida Luisa Boccalaro, Mattia Aime, Florence Marcelle Aellen, Thomas Rusterholz, Micaela Borsa, Ivan Bozic, Andrea Sattin, Tommaso Fellin, Carolina Gutierrez Herrera, Athina Tzovara, Antoine Adamantidis

“…Here, we showed that neutral auditory stimuli evoked responses across parallel auditory and non-auditory pathways, including the auditory cortex and thalamus, the hippocampus and centro-medial thalamus (CMT). Using a convolutional neural network, we identified CMT activity as the most discriminant hub for auditory-evoked sleep-to-wake transitions among all recorded structures. …”
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Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures by Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav, Rituraj Jain

“…Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. …”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. …”
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Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens by Luca Eisentraut, Christopher Mai, Johanna Hosch, Amelie Benecke, Pascal Penava, Ricardo Buettner

“…Tuberculosis remains one of the most prevalent and lethal infectious diseases, with millions of cases reported each year. …”
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Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

“…Sepsis is a life-threatening condition with significant risk to individuals, most prevalent in intensive care units (ICUs). Early diagnosis and prompt treatment are crucial to reducing sepsis-related mortality. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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PBC-Transformer: Interpreting Poultry Behavior Classification Using Image Caption Generation Techniques by Jun Li, Bing Yang, Jiaxin Liu, Felix Kwame Amevor, Yating Guo, Yuheng Zhou, Qinwen Deng, Xiaoling Zhao

“…Accurate classification of poultry behavior is critical for assessing welfare and health, yet most existing methods predict behavior categories without providing explanations for the image content. …”
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An Attention‐Based Explainable Deep Learning Approach to Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed by Qianqiu Longyang, Seohye Choi, Hyrum Tennant, Devon Hill, Nathaniel Ashmead, Bethany T. Neilson, Dennis L. Newell, James P. McNamara, Tianfang Xu

“…This study introduces a spatially distributed deep learning precipitation‐runoff model that combines Convolutional Long Short‐Term Memory (ConvLSTM) with a spatial attention mechanism. …”
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MCT-CNN-LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network by Kaiyu Chen, Yue Diao, Yucheng Wang, Xiafeng Zhang, Yannian Zhou, Minming Gu, Bo Zhang, Bin Hu, Meng Li, Wei Li, Shaoxi Wang

“…Initially, a multi-channel convolutional neural network (CNN) combined with a Long Short-Term Memory Network (LSTM) is employed. …”
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Deep Learning Models and Fusion Classification Technique for Accurate Diagnosis of Retinopathy of Prematurity in Preterm Newborn by Nazar Salih, Mohamed Ksantini, Nebras Hussein, Donia Ben Halima, ali Abdul Razzaq, Sohaib Ahmed

“…   Retinopathy of prematurity (ROP) is the most common cause of irreversible childhood blindness, and its diagnosis and treatment rely on subjective grading based on retinal vascular features. …”
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Structure and Wiring Optimized TT/MT Double‐Helical Fiber Sensors: Fabrication and Applications in Human Motion Monitoring and Gesture Recognition by Ziwei Chen, Daoxiong Qian, Dandan Xie, Chunxia Gao, Jian Shi, Hideaki Morikawa, Chunhong Zhu

“…The sensor into a smart glove capable of real‐time is further integrated, five‐channel finger motion detection, and used a convolutional neural network (CNN)‐based machine learning algorithm to achieve 98.8% accuracy in recognizing six common gestures. …”
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Deep learning models for deriving optimised measures of fat and muscle mass from MRI by Belvin Thomas, M. Adam Ali, Fatima M. H. Ali, Anthony Chung, Manjiri Joshi, Sophia Maiguma-Wilson, Gabrielle Reiff, Hadil Said, Pardis Zalmay, Michael Berks, Matthew D. Blackledge, James P. B. O’Connor

“…Specifically, subcutaneous fat (SF), intra-abdominal fat (VF), external muscle (EM) and psoas muscle (PM) were evaluated using 15 convolutional neural network (CNN)-based and 4 transformer-based deep learning model architectures. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…The histopathological recognition of such diseases is generally the most significant module in defining the finest progress of action. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…The proposed hybrid deep learning models, combined model of convolutional neural network (CNN) with LSTM and GRU, respectively, achieved higher accuracy compared to individual models. …”
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Fano Resonance Mach–Zehnder Modulator Based on a Single Arm Coupled with a Photonic Crystal Nanobeam Cavity for Silicon Photonics by Enze Shi, Guang Chen, Lidan Lu, Yingjie Xu, Jieyu Yang, Lianqing Zhu

“…When the applied voltage of the MZM is biased at 4.3 V and the non-return-to-zero on–off keying (NRZ-OOK) signal at a data rate of 10 Gbit/s is modulated, the sharpest asymmetric resonant peak and the most remarkable Fano line shape can be obtained around a wavelength of 1550.68 nm. …”
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Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains by Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik, Daniel Leff, James Kinross, George Mylonas

“…Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. …”
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