ECG heartbeat classification using progressive moving average transform by Rabah Mokhtari, Samir Brahim Belhouari, Khelil Kassoul, Abderraouf Hocini

“…These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems.…”
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An attention based residual U-Net with swin transformer for brain MRI segmentation by Tazkia Mim Angona, M. Rubaiyat Hossain Mondal

“…The proposed hybrid model was evaluated on both the BraTS 2020 and BraTS 2019 datasets. It achieved an average Dice Similarity Coefficients (DSC) of 88.27 % and average Intersection over Union (IoU) of 79.93 % on BraTS 2020. …”
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Glycolytic pathways: The hidden regulators in Parkinson's disease by Jing Shen, Ensheng Yao, Weidong Tian, Jia He, Yukai Gu, Dong Zhao

“…Parkinson's disease (PD) is a widespread neurodegenerative condition [1]; however, its association with glycolysis, specifically the activity of genes related to glycolysis, has not yet been explored. We downloaded 3 datasets related to PD from the GEO database and identified the glycolytic genes related to PD. …”
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Efficient Sample Location Selection for Query Zone in Geo-Social Networks by Kejian Tang, Shaohui Zhan, Tao Zhan, Hui Zhu, Qian Zeng, Ming Zhong, Xiaoyu Zhu, Yuanyuan Zhu, Jianxin Li, Tieyun Qian

“…Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. …”
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Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm by Chin-Feng Lai, Ying-Hsun Lai, Ming-Chin Kao, Mu-Yen Chen

“…According to experimental results, the proposed deep neuro-fuzzy clustered cyclic algorithm outperforms methods such as FedAvg, FedProx, and CyclicFL on various non-IID datasets, with accuracy approaching that of centralized learning in certain experiments. …”
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Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition by Sota Nemoto, Shunsuke Kitada, Hitoshi Iyatomi

“…Evaluation experiments on four NER datasets (Japanese and English) showed that MoM learning improves the prediction performance of the minority classes without sacrificing the performance of the majority class and is more effective than widely known and state-of-the-art methods. …”
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A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification by Wenxin Wu, Haibo Zou, Jiusheng Shan, Shanshan Wu

“…Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). …”
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School Leadership and Management in the Age of Artificial Intelligence (AI): Recent Developments and Future Prospects by Turgut Karakose, Tijen Tülübaş

“…With its capability to process large datasets, engage in human-like cognition, thinking, and conversation, make decisions, and execute actions by this means, AI technologies offer several opportunities to improve school-wide leadership, practice open management based on the principles of transparency, participation, and digital skills, create the required synergy to achieve ever-changing educational goals by integrating teachers, students, and parents into educational processes. …”

Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing by Amruta Awasthi, Lenka Krpalkova, Joseph Walsh

“…As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…The article also presents a detailed experimental analysis of deep learning models, benchmarked using TOF MRA datasets. Key research gaps are identified, including the need for large training samples, challenges in Maximum Intensity Projection (MIP) imaging, limitations of 2D architectures, and issues related to overfitting and computational complexity. …”
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A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting by Bin Li, Yuqing He

“…Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.…”
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Model Selection from Multiple Model Families in Species Distribution Modeling Using Minimum Message Length by Zihao Wen, David L. Dowe

“…Experimental results demonstrate that our proposed method outperforms competing methods by selecting the best models on both artificial and real-world datasets. More specifically, there was one test on artificial data that all methods got wrong. …”
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From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…Deep learning’s capacity to handle vast and complex datasets by extracting high-level features and enabling predictive analytics is particularly beneficial in healthcare, where timely and accurate diagnosis can significantly influence treatment outcomes. …”
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Analyzing large text data for vocabulary profiling in corpus-based studies of academic discourse by Ismail Xodabande, Mahmood Reza Atai, Mohammad R. Hashemi

“…This methodology not only provides a framework for academic vocabulary profiling but also offers scalable solutions for educators and researchers dealing with extensive text datasets. The findings contribute to advancing vocabulary research in chemistry and related fields, offering practical applications for improving educational resources and designing more effective curricula for academic English. …”
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Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques by Samitha Vidhanaarachchi, Janaka L. Wijekoon, W. A. Shanaka P. Abeysiriwardhana, Malitha Wijesundara

“…The introduced methods were tested and validated using datasets collected from Matara, Puttalam, and Makandura, Sri Lanka. …”
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Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer by Chunyan Wei, Xiaoqing Liu, Qin Wang, Qipei Li, Min Xie

“…The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis. …”
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Success or Failure in the City? Social Mobility and Rural-Urban Migration in Nineteenth- and Early-Twentieth-Century Groningen, the Netherlands by Richard Paping, Jacek Pawlowski

“…This article studies the relation between rural-urban migration and the upward and downward social mobility of different social groups from the perspective of the sending countryside and not of the receiving city. It utilizes two datasets regarding people born in the Groningen clay soil region (the Netherlands). …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. …”
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Mix-layers semantic extraction and multi-scale aggregation transformer for semantic segmentation by Tianping Li, Xiaolong Yang, Zhenyi Zhang, Zhaotong Cui, Zhou Maoxia

“…The MEMAFormer-B0 model demonstrates superior performance compared to SegFormer-B0, exhibiting gains of 4.8%, 4.0% and 3.5% on the ADE20K, Cityscapes and COCO-stuff datasets, respectively.…”
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The Utility of ChatGPT in Educational Research—Potential Opportunities and Pitfalls by Turgut Karakose

“…Educational researchers could benefit from the utility of ChatGPT in identifying gaps in the literature, generating new ideas and developing hypothesis, devising surveys or rating scales, conducting systematic reviews, eliminating human error in analysis of large datasets as well as drafting and editing scientific manuscripts. …”
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