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

“…Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. …”
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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

“…Abstract A fibrous flexible sensor, with its small size, minimally burdens the human body, ranking among the most user‐friendly flexible sensors. However, its application is often limited by damage caused by electrode movement, as flexible sensors are typically attached to joints, which can be greatly alleviated by placing the two electrodes on the same side. …”
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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

“…Both of these tissues had excellent repeatability of their delineation. VF was measured most accurately by the human observers, then by CNN-based models, which outperformed transformer-based models. …”
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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…It mainly consists of a patchwise convolutional module (PTConv), pixelwise convolutional module (PXConv), residual cross-attention tokenization module (RCTM), and transformer feature fusion module (TFFM). …”
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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

“…They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. …”
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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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Enhancing anomaly detection in plant disease recognition with knowledge ensemble by Jiuqing Dong, Jiuqing Dong, Jiuqing Dong, Heng Zhou, Alvaro Fuentes, Alvaro Fuentes, Sook Yoon, Dong Sun Park, Dong Sun Park

“…Plant diseases pose a significant threat to agriculture, impacting food security and public health. Most existing plant disease recognition methods operate within closed-set settings, where disease categories are fixed during training, making them ineffective against novel diseases. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…Abstract Cancer is among the most dangerous diseases contributing to rising global mortality rates. …”
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Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Recent advancements in flexible sensing and machine learning have positioned soft sensors as promising alternatives to traditional methods for human posture detection. However, most research has centered on calibration, with limited progress in practical applications due to the challenges posed by diverse users and complex scenarios such as human-robot interaction. …”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…Abstract Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Among the various predictive models evaluated, the random forest model emerged as the most accurate tool, according to the performance metrics of R -squared, mean square error, and average absolute relative error (%), with numerical values of 0.958, 3.718, and 2.545%, respectively, for the total datapoints. …”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing high-resolution remote sensing imagery. …”
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