Reported Challenges in Deep Learning-Based Human Pose Estimation: A Systematic Review by Nagwa R. Fisal, Ahmed Fathalla, Dina A. Elmanakhly, Ahmad Salah

“…Human Pose Estimation (HPE) is the technique of precisely estimating the locations of human joints in images and videos. Deep learning has been frequently applied recently to improve HPE performance. …”
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Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models by Jiayi Chen, Zuhai Huang, Chengyu Qin, Zixiang Pang, Yuanming Chen

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Facial recognition targeted attack algorithm based on multiscale frequency decomposition and meta-learning by CAI Jun, HUANG Tianqiang, ZHENG Aokun, YE Feng, XU Chao

“…During this process, a meta-learning attack module was introduced to calculate the loss function and update parameters. …”
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Contrastive Learning with Image Deformation and Refined NT-Xent Loss for Urban Morphology Discovery by Chunliang Hua, Daijun Chen, Mengyuan Niu, Lizhong Gao, Junyan Yang, Qiao Wang

“…Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. …”
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Data-Driven Modeling of Electric Vehicle Charging Sessions Based on Machine Learning Techniques by Raymond O. Kene, Thomas O. Olwal

“…Based on this, we formulated an objective function, expressed as a sum of utility functions when EVs charge on the grid with constraints imposed on voltage levels and charging power. …”
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Mental Disorder Assessment in IoT-Enabled WBAN Systems with Dimensionality Reduction and Deep Learning by Damilola Olatinwo, Adnan Abu-Mahfouz, Hermanus Myburgh

“…Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. …”
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Remediation Program with Working Memory and Reading for Students with Learning Difficulties: Elaboration and Pilot Study by Isabella Nicolete Xavier, Simone Aparecida Capellini

“…Background/objectives: A child’s working memory needs to be efficient in order to perform well at school, because its manipulative function needs to work properly in order to compose and decompose words, a skill that is necessary for reading. …”
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Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement by Istiak Ahmed, Md. Tanzim Hossain, Md. Zahirul Islam Nahid, Kazi Shahriar Sanjid, Md. Shakib Shahariar Junayed, M. Monir Uddin, Mohammad Monirujjaman Khan

“…This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. …”
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Forecasting formation density from well logging data based on machine learning model by Xiankang Cheng, Haoyu Zhang, Haoyu Zhang

“…Although various prediction models have been developed using density inversion, the Terzaghi correction, and machine learning techniques, these models are difficult to meet the high-precision requirements during the calculation process. …”
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Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library by Dadi Zhang, Kwok-Wai Mui, Massimiliano Masullo, Ling-Tim Wong

“…Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. …”
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Federated Learning for Frequency-Modulated Continuous Wave Radar Gesture Recognition for Heterogeneous Clients by Tobias Sukianto, Matthias Wagner, Sarah Seifi, Maximilian Strobel, Cecilia Carbonelli

“…Federated learning (FL) is a field in distributed optimization. …”
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Transcriptional patterns of cancer-related genes in primary and metastatic tumours revealed by machine learning by Faeze Keshavarz-Rahaghi, Erin Pleasance, Steven J. M. Jones

“…Additionally, transcriptional features important in classification of PTEN aberrations, such as CDCA8, AURKA, and CDC20, were found to be closely related to PTEN function. Conclusions Our findings demonstrate the utility of machine learning in interpretation of cancer genomic data and provide new avenues for development of targeted therapies tailored to individual patients with cancer. …”
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Hybrid deep learning model for density and growth rate estimation on weed image dataset by Anand Muni Mishra, Mukund Pratap Singh, Prabhishek Singh, Manoj Diwakar, Indrajeet Gupta, Anchit Bijalwan

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Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning by Yilei Ni, Shuichi Wakimoto, Weihang Tian, Yuichiro Toda, Takefumi Kanda, Daisuke Yamaguchi

“…To enable this sensing function, an optical fiber was integrated into the sleeve consisting of multiple fibers and serving as a component of the McKibben artificial muscle. …”
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Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning by Huilin Chen, Zhenghui Wang, Jiale Shi, Jinghui Peng

“…Finally, the expression and function of the key gene SHMT2 were confirmed through in vitro experiments. …”
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Reward shaping-based deep reinforcement learning for look-ahead dispatch with rolling-horizon by Hongsheng Xu, Yungui Xu, Ke Wang, Yaping Li, Abdullah Al Ahad

“…Recently, deep reinforcement learning (DRL) methods show enormous potential in developing a dispatching agent with self-learning ability, attributed to their superior generalization, adaptability, and computational efficiency. …”
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Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. …”
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Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments by Estrella Montero, Nabih Pico, Mitra Ghergherehchi, Ho Seung Song

“…The model employs a bidirectional gated recurrent unit as a temporal function to strategically incorporate the previous context of input sequences and facilitate the assimilation of the observations. …”
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Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate by Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O. Eriksen, R. Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Hansson, Nassim Kamar, Christophe Legendre, Karin Littmann, Christophe Mariat, Toralf Melsom, Lionel Rostaing, Andrew D. Rule, Elke Schaeffner, Per-Ola Sundin, Arend Bökenkamp, Ulla Berg, Kajsa Åsling-Monemi, Luciano Selistre, Anders Larsson, Ulf Nyman, Antoine Lanot, Hans Pottel, Pierre Delanaye, Celine Vens

“…Abstract In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is normally calculated using equations, such as the European Kidney Function Consortium (EKFC) equation. …”
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Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification by Liu Wang, Li Zhuo, Hui Zhang, Jiafeng Li

“…A novel consistency loss is further proposed as the objective function of LRSL to supervise the model training. Different from the existing image-level feature vector-based self-supervised learning tasks, the proposed LRSL forces the model to capture the structure information of local representations. …”
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