Client aware adaptive federated learning using UCB-based reinforcement for people re-identification by Dinah Waref, Yomna Alayary, Nadeen Fathallah, Mohamed A. Abd El Ghany, Mohammed A.-M. Salem

“…The surveillance data contains personally identifiable information such as facial images, behavioral patterns, and location data, which can be used for malicious purposes such as identity theft, stalking, or discrimination. …”
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Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data by Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

“…Abstract Microseismic monitoring is crucial for characterizing and assessing fracture systems developed during subsurface operations like geothermal reservoir development, CO2 sequestration, and hydraulic fracturing. …”
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CircDock6 drives metabolic dysfunction–associated steatotic liver disease progression in mice and mouse hepatocytes via mmu-let-7g-5p/insulin-like growth factor 1 receptor regulati... by Hongpeng Lu, Xiaoyun Ding, Peifei Li

“…Objective Circular RNAs belong to a category of noncoding RNAs that feature a unique continuous, covalently bonded ring configuration. …”
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A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards by Arthur Gonsales da Silva, Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Caio de Oliveira Bastos, Elena Almeida de Carvalho, Bruno Duarte Gomes

“…Additionally, we evaluated five classical ML models for classifying the extracted patterns: (a) Support Vector Machine (SVM); (b) GaussianNB (GNB); (c) AdaBoost (ADB); (d) K-Nearest Neighbors (KNN); and (e) Random Forest (RF). …”
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Extraction of <i>Suaeda salsa</i> from UAV Imagery Assisted by Adaptive Capture of Contextual Information by Ning Gao, Xinyuan Du, Min Yang, Xingtao Zhao, Erding Gao, Yixin Yang

“…The model accurately delineates fine-grained spatial distribution patterns of <i>Suaeda salsa</i>, outperforming existing approaches in capturing ecologically critical structural details.…”
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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

“…This research aims to enhance fetal health assessment accuracy by developing a robust model through the integration of advanced ensemble learning techniques with feature selection, scaling, and adaptive weighting mechanisms. …”
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Analysis of the structural changes in domestic consumption of FUEL and energetic resources of Moscow by L. G. Moiseykina, E. S. Darda

“…That is why, a comprehensive analysis of the dynamics and structure of consumption of fuel and energy resources will allow not only to assess the existing patterns of distribution and trends, but also as a consequence to form a highly effective energy strategy for the megacity.…”
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Phenotypic diversity and provenance variation of Cupressus funebris: a case study in the Sichuan Basin, China by Wang Yan, Yongqi Xiang, Mei Gao, Ruoyu Deng, Yan Sun, Renping Wan, Xianyi Pan, Wanzhen Li, Yu Zhong

“…Furthermore, conifer trees sharing similar growth characteristics tend to display comparable patterns of random variation, suggesting potential underlying genetic mechanisms. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules by Yutong Xia, Dawei Qiu, Cheng Zhang, Jing Liu

“…This improves the model’s ability to extract and understand complex data patterns.ResultsThe experimental results demonstrated that the model achieved recognition accuracies of 98.24%, 93.52%, and 92.27% respectively on the Ninapro DB1, Ninapro DB2, and Ninapro DB4 datasets. …”
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Insights into the evolution and regulation of miRNAs from the view of their DNA replication temporal domains by Xudong Wu, Tingting Liu

“…Furthermore, we performed the simulation of miRNA-target regulatory networks to elucidate the co-regulation patterns among late-miRNAs. To advance predictive capabilities, we developed a a support vector machine (SVM) classifier based on RNA-FM embedding, enabling prediction of miRNAs’ replication timing domains.Results and DiscussionOur study indicated that the late pre-miRNAs maintained their ability to fold into hairpin structures through extending their lengths at both ends under the premise of maintaining a certain GC content of the precursors. …”
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Utilization of Vestibular Information for Balance Control in Children with Chiari I Malformation by Irene Stella, Philippe Perrin, Matthieu Casteran, Anthony Joud, Art Mallinson, Olivier Klein

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Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models by Majaliwa M. Masolele, J. Grant C. Hopcraft, Colin J. Torney

“…However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. …”
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Predicting depressive symptoms through social support: a machine learning approach in military populations by Kun-Huang Chen, Pao-Lung Chiu, Ming-Hsuan Chen

“…Subgroup analyses demonstrated similarly high prediction performance, measured by AUPRC, across gender, SES, and future orientation subgroups. Feature importance analyses using the Gini index indicated that different support sources (e.g. leader, peer, senior student) played varying roles across subgroups.Conclusions: Machine learning approaches demonstrate high AUPRC in predicting depressive symptoms and reveal nuanced subgroup patterns in perceived social support needs. …”
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Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection by Thomas Kinyanjui Njoroge, Kelvin Mugoye Shindu, Rachael Kibuku

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Accelerated preprocessing in task of searching substrings in a string by A. V. Mazurenko, N. V. Boldyrikhin

“…Two examples illustrating features of the practical application of the developed preprocessing procedure are given.Discussion and Conclusions. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Initially, data is passed through preprocessing to address outliers and missing values. In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. …”
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Depression and Anxiety Screening for Pregnant Women via Free Conversational Speech in Naturalistic Condition by Rafael T. Sousa, Gustavo D. M. Silva, Paula L. L. Pinto, Juliano Backes, Amanda S. Mota, Thiago M. Paixao, Maria G. S. Teixeira, Wilian H. Hisatugu, Anilton S. Garcia, Kelly S. Prado, Rodrigo S. Dias, Marco A. Galletta, Hermano Tavares

“…By leveraging machine learning techniques to analyze speech patterns, this research seeks to offer a non-invasive, objective, and accessible method for early detection and intervention. …”
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The Evolution of the Idiolect over the Lifetime: A Quantitative and Qualitative Study of French 19th Century Literature by Olga Seminck, Philippe Gambette, Dominique Legallois, Thierry Poibeau

“…Using the Corpus for Idiolectal Research (CIDRE) that contains the dated works of 11 prolific 19th century French fiction writers, we propose new methods to identify, quantify and describe the grammatical-stylistic changes that take place using lexico-morphosyntactic patterns, also called motifs. To examine the strength of the chronological signal of change, we developed a method to calculate if a distance matrix of literary works contains a stronger chronological signal than expected by chance. …”
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MESS to live with schizophrenic parental history: A systematic review of developmental checkpoints. by Maryam Khan, Rabia Batool, Uzma Mushtaq, Shakir Iqbal, Sana Shaheen, Aimen Zafar Butt, Anees Ahmed

“…Parental history of schizophrenia, a complex and multifaceted psychological disorder, is recognized as a well-established risk factor in the development of the disorder among offspring. However, the developmental patterns of such children and adolescents before the onset of the problem have not yet been systematically documented. …”
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