Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

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Unsupervised Coherent Noise Removal From Seismological Distributed Acoustic Sensing Data by Sebastian Konietzny, Voon Hui Lai, Meghan S. Miller, John Townend, Stefan Harmeling

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Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels by Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey

“…Lux is a powerful new framework for the analysis of large-scale spectroscopic survey data. …”
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Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring by Mario Francisco Munoz, Hoang Vu Huy, Thanh-Dung Le, Philippe Jouvet, Rita Noumeir

“…Our approach centers on creating a deep-learning pipeline for limited training data scenarios. …”
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Sentiment Analysis using Machine learning for forecasting Indian stock Trend: A brief Survey by A.S. Dash, U. Mishra

“…The paper presents an overview of the literature on the analysis of sentiments of financial information using lexical methods, machine learning methods and forecasting for the Indian stock market based on sentiment analysis data. …”
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Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System by Arpana Mahajan, Sanjay Chaudhary, Sheshang Degadwala

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Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events by F. A. Sharifullin, D. D. Dolotova, T. G. Barmina, S. S. Petrikov, L. S. Kokov, G. R. Ramazanov, Y. R. Blagosklonova, I. V. Arkhipov, I. M. Skorobogach, N. N. Cheremushkin, V. V. Donitova, B. A. Kobrinski, A. V. Gavrilov

“…The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.…”
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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning by Wafaa Al-Kahla, Eyad Taqieddin, Ahmed S. Shatnawi, Rami Al-Ouran

“…Specifically, we extract permissions and intent filters from static analysis files and APK API data from dynamic analysis files. …”
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Abnormality-aware multimodal learning for WSI classification by Thao M. Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, Thao Bich Dang, Jean Gao, Junzhou Huang

“…However, their gigapixel resolution, lack of pixel-level annotations, and reliance on unimodal visual data present challenges for accurate and efficient computational analysis. …”
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Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable ma... by Mahboubeh Boueshagh, Joan M. Ramage, Mary J. Brodzik, David G. Long, Molly Hardman, Hans-Peter Marshall

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Post-traumatic stress disorder, trauma and parenting stress: an individual participant data meta-analysis by Laurien Meijer, Kathleen Thomaes, Matthijs Blankers, Maja Deković, Molly R. Franz, Rolf Kleber, Elise M. van de Putte, Elisa van Ee, Elena Camisasca, Steffany J. Fredman, Dominik Moser, Larry L. Mullins, Maria Muzik, Mathilde Overbeek, Abigail Palmer Molina, Jessica Riggs, Katherine Rosenblum, Kristin Samuelson, Daniel Schechter, Chiara Suttora, Catrin Finkenauer

“…Findings are supplemented with information on the process of performing an individual participant data meta-analysis (IPDMA) and lessons learned.Methods: Using one-stage IPDMA, data from published studies and unpublished datasets were synthesized and analysed using multilevel linear regression.Results: Twelve datasets were included (N = 1249: 92.5% female, M age = 32.8 years, 53.8% ethnic minority). …”
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RuleKit2: Faster and simpler rule learning by Adam Gudyś, Cezary Maszczyk, Joanna Badura, Adam Grzelak, Marek Sikora, Łukasz Wróbel

“…The former complies with scikit-learn, the most popular machine learning library for Python, allowing RuleKit2 to be straightforwardly integrated into existing data analysis pipelines. …”
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Visualising and evaluating learning/achievement consistency in introductory statistics by Taryn Axelsen, Rachel King, Elizabeth Curtis

“…This research employed a multi-layered approach, including innovative ‘consistency of learning’, ‘combination analysis’ and ‘heatmap’ techniques, to examine performance across 11 learning modules. …”
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Single-cell data combined with phenotypes improves variant interpretation by Timothy Chapman, Timo Lassmann

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Effective k-nearest neighbor models for data classification enhancement by Ali A. Amer, Sri Devi Ravana, Riyaz Ahamed Ariyaluran Habeeb

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Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study by Sun Q, Liu Z, Ding T, Shi C, Hou N, Sun C

“…Qihang Sun,1 Zhongxiao Liu,1 Tao Ding,1 Changzhou Shi,2 Nailong Hou,2 Cunjie Sun1 1Department of Medical Imaging, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, People’s Republic of China; 2School of Medical Imaging, Xuzhou Medical University, Xuzhou, People’s Republic of ChinaCorrespondence: Cunjie Sun, Affiliated Hospital of Xuzhou Medical University, No. 99 West Huaihai Road, Quanshan District, Xuzhou City, Jiangsu Province, 221006, People’s Republic of China, Tel +8618052268897, Email cunjiesxyfy@163.comPurpose: This study aims to develop a machine learning-based model for the objective assessment of CT pulmonary angiography (CTPA) image quality.Patients and Methods: A retrospective analysis was conducted using data from 99 patients who underwent CTPA between March 2022 and January 2023, alongside two public datasets, FUMPE (21 cases) and CAD-PE (30 cases). …”
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A systematic review of data privacy in Mobility as a Service (MaaS) by Zineb Garroussi, Antoine Legrain, Sébastien Gambs, Vincent Gautrais, Brunilde Sansò

“…Using the PRISMA framework, a comprehensive literature search across Web of Science, Elsevier, and IEEE Xplore databases resulted in the selection of 32 studies for detailed analysis.The review is structured around three main themes: (1) Privacy-Preserving Techniques, including anonymization strategies (k-anonymity, differential privacy, obfuscation), encryption methods (blockchain, cryptographic protocols), federated learning for decentralized data processing, and advanced algorithms for optimizing privacy budgets and balancing utility-privacy trade-offs; (2) User Trust and Privacy Perceptions, highlighting that trust in service providers is essential for MaaS adoption, privacy concerns may impact adoption but do not necessarily prevent it (the “privacy paradox”), and awareness of data misuse affects user trust and willingness to adopt MaaS; and (3) Regulatory Frameworks, focusing on the importance of GDPR compliance to ensure strict data protection through consent and transparency, and embedding privacy-by-design principles within MaaS architectures to safeguard user data from the outset.This review emphasizes the need for a holistic approach, integrating technological innovation, user-centered design, and strong regulatory oversight to effectively address privacy challenges in MaaS. …”
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Transformer-based transfer learning on self-reported voice recordings for Parkinson’s disease diagnosis by Ilias Tougui, Mehdi Zakroum, Ouassim Karrakchou, Mounir Ghogho

“…By using advanced data analysis, these methods improve early detection and diagnosis, which is crucial for managing the disease effectively. …”
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Analysis of Adaptation of Students Studying under the Flipped Classroom Model to the Conditions of Distance Learning by G. R. Chaynikova

“…The analysis of pedagogical literature on blended learning made it possible to identify a number of important principles which the learning process should be based on in the flipped classroom model, the analysis of which, in turn, showed that they fully correspond with the principles of distance learning. …”
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Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning by Yukta Mehta, Vincent Lo, Vijen Mehta, Kunal Agrawal, Charan Teja Madabathula, Eugene Chang, Jerry Gao

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