From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh by Md. Abu Saleh, H.M. Rasel, Briti Ray

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities by Shoaib Hassan, Qianmu Li, Khursheed Aurangzeb, Affan Yasin, Javed Ali Khan, Muhammad Shahid Anwar

“…Abstract Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. …”
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Robustness of topological persistence in knowledge distillation for wearable sensor data by Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Matthew P. Buman, Hyunglae Lee, Pavan Turaga

“…Abstract Topological data analysis (TDA) has shown great success in various applications involving wearable sensor data. …”
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Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomen... by I. Yu. Torshin, O. A. Gromova, L. V. Stakhovskaya, N. P. Vanchakova, A. N. Galustyan, Zh. D. Kobalava, T. R. Grishina, A. N. Gromov, I. A. Ilovaiskaya, V. M. Kodentsova, A. G. Kalacheva, O. A. Limanova, V. A. Maksimov, S. I. Malyavskaya, E. V. Mozgovaya, N. I. Tapilskaya, K. V. Rudakov, V. A. Semenov

Subjects: “…big data analysis…”
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Relaxing the confirmatory factor model in the Big Five: Exploratory, Bayesian, and machine learning approaches by Jacob S. Gray

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Machine learning classification meets migraine: recommendations for study evaluation by Igor Petrušić, Andrej Savić, Katarina Mitrović, Nebojša Bačanin, Gabriele Sebastianelli, Daniele Secci, Gianluca Coppola

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Exploring deep learning for landslide mapping: A comprehensive review by Zhi-qiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao

“…Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. …”
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Optimizing Federated Learning With Aggregation Strategies: A Comprehensive Survey by Naeem Khan, Shibli Nisar, Muhammad Asghar Khan, Yasar Abbas Ur Rehman, Fazal Noor, Gordana Barb

“…The analysis delves into the advantages and limitations of these aggregation strategies, particularly their role in tackling challenges like non-IID data, communication efficiency, and resistance to adversarial attacks. …”
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An Analysis of the Severity of Alcohol Use Disorder Based on Electroencephalography Using Unsupervised Machine Learning by Kaloso M. Tlotleng, Rodrigo S. Jamisola

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Feasibility Analysis for Predicting Indian Ocean Bigeye Tuna (<i>Thunnus obesus</i>) Fishing Grounds Based on Temporal Characteristics of FY-3 Microwave Radiation Imager Data by Yun Zhang, Jinglan Ye, Shuhu Yang, Yanling Han, Zhonghua Hong, Wanting Meng

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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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A Data Storage, Analysis, and Project Administration Engine (TMFdw) for Small- to Medium-Size Interdisciplinary Ecological Research Programs with Full Raster Data Capabilities by Paulina Grigusova, Christian Beilschmidt, Maik Dobbermann, Johannes Drönner, Michael Mattig, Pablo Sanchez, Nina Farwig, Jörg Bendix

“…Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. …”
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BenchMake: turn any scientific data set into a reproducible benchmark by A S Barnard

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…To achieve these objectives, a survey was meticulously designed in collaboration with psychologists, counselors, and therapists. Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. …”
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Machine Learning–Based Prediction for In‐Hospital Mortality After Acute Intracerebral Hemorrhage Using Real‐World Clinical and Image Data by Koutarou Matsumoto, Kazuaki Ishihara, Katsuhiko Matsuda, Koki Tokunaga, Shigeo Yamashiro, Hidehisa Soejima, Naoki Nakashima, Masahiro Kamouchi

“…The net benefit of ML‐based models was evaluated using decision curve analysis. The area under the receiver operating characteristic curves were 0.91 (95% CI, 0.86–0.95) for the ICH score, 0.93 (95% CI, 0.89–0.97) for the ICH grading scale, 0.83 (95% CI, 0.71–0.91) for the ML‐based model fitted with raw image data only, and 0.87 (95% CI, 0.76–0.93) for the ML‐based model fitted using clinical data without specialist expertise. …”
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A Catalog of Hα Emission-line Stars and 785 Newly Identified Young Stellar Object Candidates from LAMOST Based on a Deep Learning Method by Lei Tan, Ying Mei, Jiale Qian, Xiaolong Wang, Yue Cui, Aixin Huang, Feng Wang, Hui Deng, Chao Liu, Huanbin Chi

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Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning by Cheng-Shun Lee, Jenn-Jier James Lien, Kai Chain, Li-Chun Huang, Zhong-Wei Hsu

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Qualitative and Quantitative Analysis of Volatile Molecular Biomarkers in Breath Using THz-IR Spectroscopy and Machine Learning by Akim Tretyakov, Denis Vrazhnov, Alexander Shkurinov, Viacheslav Zasedatel, Yury Kistenev

“…Machine learning methods enable the establishment of latent dependencies in spectral data and the conducting of their qualitative and quantitative analysis. …”
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Design of an Improved Model for Gear Fault Diagnosis Using Acoustic Data and EfficientNet-Based Deep Learning Process by Bundele Shubham, Kane P.V.

“…This research proves that acoustic-based fault analysis combined with advanced deep learning models achieves capture of efficiency, compared to conventional vibration-based diagnostics. …”
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Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. by Enrico Glaab, Jaume Bacardit, Jonathan M Garibaldi, Natalio Krasnogor

“…Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. …”
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