BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers. by Tanzir Hossain, Ar-Rafi Islam, Md Humaion Kabir Mehedi, Annajiat Alim Rasel, M Abdullah-Al-Wadud, Jia Uddin

“…Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing complex dataset and its linguistic patterns in Bangla to achieve higher accuracy. Addressing the challenge, this study presents a comprehensive approach to classify Bangla news articles into eight distinct categories using various machine learning and deep learning techniques. …”
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Nascent flagellar basal bodies are immobilized by rod assembly in Bacillus subtilis by Caroline M. Dunn, Daniel J. Foust, Yongqiang Gao, Julie S. Biteen, Sidney L. Shaw, Daniel B. Kearns

“…We conclude that the peritrichous patterning of flagella of B. subtilis is different from the polar patterning of other bacteria, and we infer that the B. subtilis rod probes the PG for holes that can accommodate the machine.…”
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Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain) by Enrique Cerrillo-Cuenca, Primitiva Bueno-Ramírez

“…This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). …”
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A new multistep Si{1 0 0} machining process for aspheric cavities: the role of high-index surfaces by Wilfrido Calleja-Arriaga

“…Regarding microlens fabrication, we note the lack of thorough studies on this type of concave structure, particularly when another pattern is machined over the aspheric surface. By adding lithography and etching steps to this well-known process, the aspheric morphology can be improved. …”
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Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. by Quang Dung Dinh, Daniel Kunk, Truong Son Hy, Vamsi Nalam, Phuong D Dao

“…This study presents a novel Machine Learning (ML) approach to automate the annotation of EPG signals. …”
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Navigating the data frontier in science assessment: Advancing data augmentation strategies for machine learning applications with generative artificial intelligence by Paul P. Martin, Nicole Graulich

“…Machine learning (ML) techniques are commonly seen as an inductive learning procedure, typically involving the identification of patterns in a specific training dataset to make predictions in novel contexts. …”
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Modeling of Systematic Errors and Precision Optimization Methods for Workpiece Clamping and Alignment System in Aeroengine Gearbox Automated Line Machining by DU Xueming, XIANG Yang, LIU Shun, JIN Sun

“…However, it has also brought about an increase in systematic errors during multi-process machining. To mitigate the adverse impact of these new systematic errors on machining accuracy, this paper investigates error modeling and precision optimization methods for the alignment system under uncertainty. …”
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Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures by Mohammed Alqarni, Ali Alqarni

“…Abstract Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. …”
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Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach by Lisi Lai, Hui Zhang, Jiahui Gu, Long Wen

“…Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. …”
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Eye tracking in digital pathology: A comprehensive literature review by Alana Lopes, Aaron D. Ward, Matthew Cecchini

“…Thematic analysis was conducted to organize each study into one or more of the 10 themes identified to characterize the visual search patterns of pathologists: (1) effect of experience, (2) fixations, (3) zooming, (4) panning, (5) saccades, (6) pupil diameter, (7) interpretation time, (8) strategies, (9) machine learning, and (10) education. …”
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Multi-Dimensional AE Signal Features in Eccentrically Loaded Concrete Structures: A Machine Learning Classification for Damage Progression by Shilong Ding, Alipujiang Jierula, Abudusaimaiti Kali, Tong Han, Tae-Min Oh

“…These findings provide quantitatively validated evidence supporting the efficacy of machine learning-based methodologies for enabling stage-specific damage characterization in structural health monitoring applications.…”
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Machine learning approach for identifying and forecasting streamflow droughts in data limited basins of South Korea using threshold levels by Young-Ho Seo, Jang Hyun Sung, Byung-Sik Kim, Junehyeong Park

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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…We evaluate the proposed method in three machine learning models, Random Forest, Gradient Boosting, and XGBoost, to classify road surface type into three classes: asphalt, dirt, and cobblestone. …”
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Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…Visualization of the relationships between numerical features also reveals significant correlation patterns between physiological variables and patient anxiety levels. …”
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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and Machine Learning Models by A. Aylin Tokuc, Tamer Dag

“…Unlike prior research, which typically focuses on single representations, our approach combines aggregated session-level summaries with granular, sequential user actions to capture both long-term and short-term behavioral patterns. Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. …”
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Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems by Vasilios Plakandaras, Fahad Khan Khadim, Vassiliki Kazana, Emmanouil Anagnostou, Amvrossios C Bagtzoglou

“…Harnessing the analytical power of machine learning models in extracting patterns from data, the informational content of social surveys coupled with hydrological data for the survey region from a calibrated MODFLOW-NWT groundwater (GW) model, we draw inferences on the importance of socioeconomic rather than hydrological variables as drivers in agricultural decisions about crop selection and planting period, underscoring those factors as potential criteria in drawing successful agricultural policies for crop yield optimization in the Great South area. …”
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A Deep Learning Model Leveraging Time-Series System Call Data to Detect Malware Attacks in Virtual Machines by A. Alfred Raja Melvin, Jaspher W. Kathrine, Andrew Jeyabose, D. Cenitta

“…Abstract A Tenant Virtual Machine (TVM) user in the cloud may misuse its computing power to launch malware attack against other tenant VMs, Host OS, Hypervisor, or any other computing devices/resources inside the cloud environment of a Cloud Service Provider. …”
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Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms by Antonio Panico, Alberto Corvi, Luca Collini, Corrado Sciancalepore

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Analisis Visual dan Machine Learning untuk Mengukur Validitas Dokumen Akademik Perpustakaan: Studi pada Data Turnitin by Hesti Ari Wardani, Imam Yuadi

“…Using a data visualization approach and machine learning algorithms, this research explores the relationship between Turnitin scores and document validity status. …”
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Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models by Farzaneh Ansari, Ali Neshasteh-Riz, Reza Paydar, Fathollah Mohagheghi, Sahar Felegari, Manijeh Beigi, Susan Cheraghi

“…We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. …”
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