FLORA: a novel method to predict protein function from structure in diverse superfamilies. by Oliver C Redfern, Benoît H Dessailly, Timothy J Dallman, Ian Sillitoe, Christine A Orengo

“…The majority of prediction algorithms employ local templates built on known or predicted functional residues. …”
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Analysis of the effectiveness of selection of women for coronarography in real clinical practice by N. V. Izmozherova, A. A. Popov, V. E. Sherstobitov

“…Intact coronary arteries were detected in 37 % subjects. In 17 of 40 persons with intact structure of the coronary arteries spasm of the coronary arteries was diagnosed. …”
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Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery by Adam Cuellar, Daniel Brignac, Abhijit Mahalanobis, Wasfy Mikhael

“…Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. …”
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Recent Tools of Software-Defined Networking Traffic Generation and Data Collection by Tabarak Khudhair, Omar Athab

“…Results show success in generating several types of network metrics to be used in the future for training machine or deep learning algorithms. Therefore, when generating data for the purpose of congestion control, iperf3 is the best tool, whilst Ping is useful when generating data for the purpose of detecting distributed denial-of-service attacks. …”
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GALNT6 associated with O-GlcNAcylation contributes to the tumorigenesis of oral squamous cell carcinoma by Junfeng Yan, Xuan Tang, Yingying Zhou, Xin Xiong

“…Immunohistochemistry staining was used to detect the expression of GALNT6 in clinical tissue samples. …”
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Optimizing Stroke Classification with Pre-Trained Deep Learning Models by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…Methods: The study utilized advanced deep learning algorithms, specifically ConvNeXt Base, to analyze large datasets of medical imaging data, focusing on MRI scans. …”
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Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim by Ranu Agastya Nugraha, Hilman Ferdinandus Pardede, Agus Subekti

“…The new balanced data are used to train 17 classification algorithms. Our experiments show that our proposed method achieves better performance on several evaluation metrics: accuracy, precision score, F1-score, and also ROC than other referenced methods to deal imbalance data random over sampling (ROS), random under sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMO), and adaptive synthetic (ADASYN) methods. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…This study examines the impact of various partitioning techniques on crime forecasting performance, comparing the traditional static division of the city into police districts with machine learning approaches, specifically density clustering algorithms, for detecting crime hotspots. The experimental evaluation, conducted on two real-world case studies, i.e. …”
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Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay. by Patrick Neale, Shelby Brown, Tara Sill, Alison Cawood, Maria Tzortziou, Jieun Park, Min-Sun Lee, Beth Paquette

“…The Chesapeake Water Watch program seeks to enhance the monitoring of tributaries by developing and testing methods for volunteer scientists to easily measure chlorophyll, turbidity, and colored dissolved organic matter (CDOM) to inform Bay stakeholders and improve algorithms for analogous remote sensing (RS) products. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Despite its significance, there is limited evidence regarding the use of machine learning (ML) algorithms to predict medication adherence within the Ethiopian population. …”
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Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach by Yi Zheng, Yao Wang, Tayir Aziz, Ali Mamtimin, Yang Li, Yan Liu

“…The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. …”
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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…Therefore, accurately predicting bridge deformation is essential for analyzing its causes and detecting potential safety hazards in a timely manner. …”
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Clinical Phenotypes of Patients with Anti-DFS70/LEDGF Antibodies in a Routine ANA Referral Cohort by Makoto Miyara, Roger Albesa, Jean-Luc Charuel, Mohamed El Amri, Marvin J. Fritzler, Pascale Ghillani-Dalbin, Zahir Amoura, Lucile Musset, Michael Mahler

“…Although anti-DFS70 antibodies cannot exclude the presence of SARD, the likelihood is significantly lower than in patients with other IIF patterns and should be included in test algorithms for ANA testing.…”
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Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State by Andres Perea, Sajidur Rahman, Huiying Chen, Andrew Cox, Shelemia Nyamuryekung’e, Mehmet Bakir, Huping Cao, Richard Estell, Brandon Bestelmeyer, Andres F. Cibils, Santiago A. Utsumi

“…Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. No differences in MI were detected between ruminating and resting; therefore, subsequent model testing considered the combination of rumination and resting as one class. …”
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Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning by Teng-Li Lin, Arvind Mukundan, Riya Karmakar, Praveen Avala, Wen-Yen Chang, Hsiang-Chen Wang

“…These conditions are generally not easily detectable due to their comparable clinical presentations. …”
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What patients and caregivers want to know when consenting to the use of digital behavioral markers by Anika Sonig, Christine Deeney, Meghan E. Hurley, Eric A. Storch, John Herrington, Gabriel Lázaro-Muñoz, Casey J. Zampella, Birkan Tunc, Julia Parish-Morris, Jenny Blumenthal-Barby, Kristin Kostick-Quenet

“…Abstract Artificial intelligence (AI)-based computational tools for deriving digital behavioral markers are increasingly able to automatically detect clinically relevant patterns in mood and behavior through algorithmic analysis of continuously and passively collected data. …”
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MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction by Yali Sun, Yun Li, Yang Zhou, Ting Cai, Yuxuan Chen, Chao Zou, Han Song, Shenghuang Lin, Shenghua Liu

“…Finally, we illustrate the merits of integration machine learning algorithms to construct predictive models using the extensive datasets produced by our sensors for both classification and quantification tasks. …”
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Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions by Giovanna Nicora, Samuele Pe, Gabriele Santangelo, Lucia Billeci, Irene Giovanna Aprile, Marco Germanotta, Riccardo Bellazzi, Enea Parimbelli, Silvana Quaglini

“…After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. …”
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A novel infrared thermography image analysis for transformer condition monitoring by Rupali Balabantaraya, Ashwin Kumar Sahoo, Prabodh Kumar Sahoo, Chayan Mondal Abir, Manoj Kumar Panda

“…Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). …”
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COSMIC’s Large-scale Search for Technosignatures during the VLA Sky Survey: Survey Description and First Results by C. D. Tremblay, J. Sofair, L. Steffes, T. Myburgh, D. Czech, P. B. Demorest, R. A. Donnachie, A. W. Pollak, M. Ruzindana, Siemion A. P. V., S. S. Varghese, S. Z. Sheikh

“…Developing algorithms to search through data efficiently is a challenging part of searching for signs of technology beyond our solar system. …”
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