A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19 by Jannatul Ferdousi, Soyabul Islam Lincoln, Md. Khorshed Alom, Md. Foysal

“…The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). …”
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Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle. by Muhammad Usman Riaz, Luke O'Grady, Conor G McAloon, Finnian Logan, Isobel Claire Gormley

“…Dimension reduction techniques, such as principal component analysis (PCA) and functional principal component analysis (fPCA), offer solutions by reducing the dimensionality of the data while retaining key information and allowing for the application of a broader set of ML approaches. …”
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Multimodal Deep Learning Model for Cylindrical Grasp Prediction Using Surface Electromyography and Contextual Data During Reaching by Raquel Lázaro, Margarita Vergara, Antonio Morales, Ramón A. Mollineda

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Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…Abstract The high-pressure pulsation test (HPPT) bench is used to test the functionality and resilience of components under high pressure and pulsation. …”
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USING CORPORATE MECHANISMS TO IMPROVE ACADEMIC ENGLISH LANGUAGE PROFICIENCY AMONG POSTGRADUATE STUDENTS: IMPACT ON THE LEARNING PROCESS by Ольга Володимирівна Чайковська

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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD₅) concentrations over a five-day horizon. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…Its ability to dynamically interact with DSA’s learning dynamics makes it not just a suitable choice, but the optimal optimization choice for this hybrid deep learning system. …”
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Lessons learned from an fMRI-guided rTMS study on performance in a numerical Stroop task. by Lysianne Beynel, Hannah Gura, Zeynab Rezaee, Ekaete C Ekpo, Zhi-De Deng, Janet O Joseph, Paul Taylor, Bruce Luber, Sarah H Lisanby

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Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model by Xiangming Xu, Xinyi Zhang, Linghua Qin, Rui Li

“…Furthermore, the driving factors of ESV were explored using the explainable machine learning method.ResultsThe findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…Novel cybersecurity technologies are continually developing and give opportunities and challenges to IoT cybersecurity organizations. Deep learning (DL) is one of the main technologies of today’s smart cybersecurity policies or systems for functioning intelligently. …”
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Relational coordination and team‐based care: Change initiative overload and other challenges in a learning health system by Lauren Hajjar, Olawale Olaleye, Julius Yang, Susan McGirr, Erin E. Sullivan

“…Conclusions Within the healthcare context, this study contributes to our learning about implementing and measuring relational interventions. …”
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Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation by Alice Finocchi, Silvia Campagnini, Andrea Mannini, Stefano Doronzio, Marco Baccini, Bahia Hakiki, Donata Bardi, Antonello Grippo, Claudio Macchi, Jorge Navarro Solano, Michela Baccini, Francesca Cecchi

“…An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. …”
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Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning by Fei Li, Haolan Song, Yifan Wang

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Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning by Xiaosheng Huang, Tiansheng Chou, Tiansheng Chou, Xinhua Liu, Kun Zeng, Liangnan Sun, Zonghui Yan, Shaoyi Mei, Wenqun Xi, Zongyi Zhan, Yi Liu, Songguo Dong, Siqi Liu, Jun Zhao

“…Aging proteins (APs) and their functional enrichment were evaluated using various statistical and bioinformatics methods, while aging modulators were predicted using multiple machine-learning models.ResultsAH proteomic expression patterns exhibited various types of linear and nonlinear changes across the age groups. …”
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Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG by Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang

“…Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. …”
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Entorhinal cortex layer III Adgrl2 expression controls topographical circuit connectivity required for sequence learning by Jordan D. Donohue, Crisylle Blanton, Anna Chen, Amna Ahmad, Elizabeth D. Liu, Lisette Saab, Rajbir Kaur, Woojin Yang, Garret R. Anderson

“…Abstract The entorhinal cortex and hippocampus are interconnected brain regions required for episodic learning and memory. For this functional encoding, correct assembly of specific synaptic connections across this circuit is critical during development. …”
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A novel MRI-based deep learning imaging biomarker for comprehensive assessment of the lenticulostriate artery-neural complex by Yan Song, Yunlong Jin, Jianguo Wei, Jiajia Wang, Zhong Zheng, Ying Wang, Ru Zeng, Weiping Lu, Bingcang Huang

“…The model’s accuracy in Task1 and Task2 fine-tuning classification reached 100%. Conclusions Deep learning features extracted from the basal ganglia nuclei effectively reflect cerebrovascular and neurological functions and reveal the damage status of the LSA. …”
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HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. …”
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Energy Optimal Trajectory Planning for the Morphing Solar-Powered Unmanned Aerial Vehicle Based on Hierarchical Reinforcement Learning by Tichao Xu, Wenyue Meng, Jian Zhang

“…Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. …”
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Towards enhancing security for upcoming 6G-ready smart grids through federated learning and cloud solutions by J. Jithish, Nagarajan Mahalingam, Bo Wang, Kiat Seng Yeo

“…To enhance grid security, Deep Learning (DL) techniques can be utilized to identify deviations from normal network traffic and detect cyberattacks. …”
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