A Deep Learning Approach to Assist in Pottery Reconstruction from Its Sherds by Matheus Ferreira Coelho Pinho, Guilherme Lucio Abelha Mota, Gilson Alexandre Ostwald Pedro da Costa

“…In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…In conclusion, integrating multi-sensor data and with advanced machine learning techniques provides a feasible and reliable approach for screening salt-tolerant pea varieties, paving the way for better utilization of salt-alkali region.…”
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Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment by Weidong Zhang, Chenxi Wu, Peifeng Ni, Sheng Zhang, Hongwei Zhang, Ying Zhu, Wei Hu, Mengyuan Diao

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Participatory Learning is Needed to Increase Family Independence in Handling Post-stroke Patients at Home by Ali Hamzah, Ihat Hatimah, Asep Saepudin, Joni Rahmat Pramudia, Khemaradee Masingboon

“…This research aims to identify an educational model that allows families to actively participate in learning so that they can be independent in treating post-stroke patients at home. …”
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NEO adjuvant chemotherapy in breast cancer: What have we learned so far? by Raut Nirmal, Chordiya Nilesh

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Brain Tumor Classification of Medical Images based on Transfer Pre-trained Learning Models by Mohamed H Ghaleb Abdkhaleq Abdkhaleq, Ibraheem Al-Jadir, Loay George, Yuri Yuryevich Gromov, Eliseev Aleksey Igorevich

“…This endeavor holds immense importance in promptly diagnosing and treating brain tumors. Transfer learning greatly facilitates the classification of different forms of brain tumors visible in medical imaging, enabling the achievement of this target. …”
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A multi-agent reinforcement learning approach for continuous battery cell-level balancing by Yasaman Tavakol-Moghaddam, Mehrdad Boroushaki

“…This paper proposes a multi-agent reinforcement learning (MARL) approach for continuous cell-level balancing, particularly for electric vehicles (EVs). …”
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Integrating pharmacogenetics, multi‐omics and machine learning in the novel therapeutic view of pulmonary hypertension by Chenxi Li, Jipeng Xue, Dongchen Zhou, Xiaomeng Qi, Ting Chen, Zhoubin Li

“…In addition to the commonly used drugs for treating PH, pharmacogenomic therapies are facilitating the innovation of personalized medicine to treat PH. …”
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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. …”
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Evaluation of a Deep Learning Model for Automatic Detection of Schizophrenia Using EEG Signals by Swetha Padmavathi Polisetty, Radhamani Ellapparaj, Karthikeyan M P

“…Early diagnosis of schizophrenia plays an important role in treating and limiting the effects of the disease. An automated diagnosis system for schizophrenia detection through a deep learning model is suggested in this research. …”
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Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

“…Early risk factor detection is essential for managing and treating cardiovascular disease (CVD), a global health issue. …”
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Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection by Ranpreet Kaur, Hamid GholamHosseini, Maria Lindén

“…An image segmentation network based on deep learning is then used to extract lesion regions for detailed analysis and calculate the optimized classification features. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. …”
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A multi-modal dental dataset for semi-supervised deep learning image segmentation by Yaqi Wang, Fan Ye, Yifei Chen, Chengkai Wang, Chengyu Wu, Feng Xu, Zhean Ma, Yi Liu, Yifan Zhang, Mingguo Cao, Xiaodiao Chen

“…Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. …”
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Reinforcement Learning-Driven Secrecy Energy Efficiency Maximization in RIS-Enabled Communication Systems by Akram Y. Sarhan, Osamah A. Abdullah, Hayder Al-Hraishawi, Faisal S. Alsubaei

“…Given the intricate nature of this problem, we utilize artificial intelligence, particularly deep reinforcement learning. By treating the problem as a Markov decision process (MDP), we make it easier to make decisions in real-time by creating specific states and actions, along with a reward system designed to balance privacy and energy efficiency. …”
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Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor by Katarina Elez, Tim Hempel, Jonathan H. Shrimp, Nicole Moor, Lluís Raich, Cheila Rocha, Robin Winter, Tuan Le, Stefan Pöhlmann, Markus Hoffmann, Matthew D. Hall, Frank Noé

“…The identified inhibitor holds promise for treating viral and other diseases involving TMPRSS2.…”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

“…This paper proposes a Federated Learning-based framework with Multimodal Residual Fusion and Pareto-optimized Client Selection (FLPCS-MRF). …”
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Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods by Weiwen Li, Lijun Zhong, Kewen Zhao, Jincheng Xie, Shaodong Deng, Yunyong Fang

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Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks by Yanan Zhu, Hui Zhang, Xuan Zhao, Xin Wang, Lina Xing, Sijie Yao, Xiao Jin, Tingting Li, Ting Dai, Xinyue Ding, Zhen Qi, Zongjun Liu

“…Identifying novel compounds for the treatment of ARVC is crucial for advancing drug development.PurposeIn this study, we aim to identify novel compounds for treating ARVC.MethodsMachine learning (ML) models were constructed using proteins analyzed from the scRNA-seq data of ARVC rats and their corresponding protein-protein interaction (PPI) network to predict binding affinity (BA). …”
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