A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model by Hafiz Muhammad Tayyab Khushi, Tehreem Masood, Arfan Jaffar, Sheeraz Akram

“…The proposed methodology improves the quality and quantity of the publicly accessible Br35h dataset by applying data augmentation techniques. The suggested model had 99.67% accuracy on Br35h data and 0.33% miss categorization. …”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…Predicting survival outcomes in critical accidents has been a focal point in machine learning research. This study addresses several limitations of existing methods, including insufficient management of data imbalance, lack of emphasis on hyperparameter tuning, and proneness to overfitting. …”
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What do nursing students need in learning English? by Ami Pradana, Wisma Yunita, Irma Diani

“…The results of the study revealed that the existing materials used by the English teachers tended to general English materials. In addition, the analysis results of target situation show that; 1) most students learn English for looking for jobs; 2) the most needed language proficiency components were vocabulary mastery, speaking and listening skill; 3) the materials needed in each language skill and knowledge were the materials that were related to nursing activities and job; 4) almost all of the topics were needed by nursing students such as giving information on treatment plans or diagnosis. …”
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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

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Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation by Meng Sun, Xingling Sun, Fei Wang, Li Liu

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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…In conclusion, machine learning holds strong potential to support predictive systems and data-driven policies for stunting prevention in Indonesia. …”
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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

“…However, challenges such as multimodal data integration and joint analysis in Internet of Medical Things (IoMT) environments and data heterogeneity across sources hinder efficient and accurate fall detection. …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. …”
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Image dataset of Taro Leaf Blight disease collected from the West African Sub-RegionMendeley Data by Chidiebere Nwaneto, Chika Yinka-Banjo, Ogban-Asuquo Ugot, Obiageli Umeugochukwu, Thompson Annor

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Toward Understanding Federated Learning over Unreliable Networks by Chenyuan Feng, Ahmed Arafa, Zihan Chen, Mingxiong Zhao, Tony Q. S. Quek, Howard H. Yang

“…This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) – a machine learning method that preserves data privacy in the training process – over wireless networks. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

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Identifying patterns of health care utilisation among physical elder abuse victims using Medicare data and legally adjudicated cases: protocol for case–control study using data lin... by Tony Rosen, Yuhua Bao, Yiye Zhang, Sunday Clark, Katherine Wen, Alyssa Elman, Philip Jeng, Elizabeth Bloemen, Daniel Lindberg, Richard Krugman, Jacquelyn Campbell, Ronet Bachman, Terry Fulmer, Karl Pillemer, Mark Lachs

“…We will use machine learning approaches to extend our ability to identify patterns suggestive of potential physical elder abuse exposure. …”
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PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation by Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong

“…Abstract Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. …”
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Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data by Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang, Xu Li

“…However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. …”
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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

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Adversarial Graph Regularized Deep Nonnegative Matrix Factorization for Data Representation by Songtao Li, Weigang Li, Yang Li

“…Specifically, for the purpose of effectively learning low-dimensional representations of data. This work constructs a deep NMF method to approximate the coding structure of the original data, which enables AGDNMF to learn the hierarchical mapping relationship between the categories of the original data, and learn the hidden association information of the data from low-to-high in the middle layer. …”
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Talent scouting and standardizing fitness data in football club: systematic review by Moch Yunus, Ronal Surya Aditya

“…The analysis highlights the transformative potential of integrating quantitative player statistics with advanced data science and data-driven approaches in revolutionizing sports performance, enabling clubs to make informed recruitment decisions and enhance team performance. …”
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Data-driven decision making in patient management: a systematic review by Guoliang Lyu

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An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning by Prasanna Kanti Ghoshal, A.P. Joshi, Kunal Chakraborty

“…This analysis underscores the ability of machine learning algorithms to enhance the accuracy of model-simulated surface pCO2 outputs.…”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Purpose The purpose of this study is to evaluate the efficacy of lower extremity kinematic gait data for detecting and rating the severity of unilateral hip OA using machine learning algorithms. …”
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