Proteomic profiling of the outer membrane fraction of the obligate intracellular bacterial pathogen Ehrlichia ruminantium. by Amal Moumène, Isabel Marcelino, Miguel Ventosa, Olivier Gros, Thierry Lefrançois, Nathalie Vachiéry, Damien F Meyer, Ana V Coelho

“…These experimental data were compared to the predicted subcellular localization of the entire E. ruminantium proteome, using three different algorithms. …”
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DYNAMIC DISCIPLINE OF PARALLEL SERVICE IN CONCEPT FLIGHT AND FLOW – INFORMATION FOR A COLLABORATIVE ENVIRONMENT by L. E. Rudel'son, S. N. Smorodskiy, V. A. Chernyshyova

“…The essence of ICAO's proposals is in the transition from the "tracking" system which responds to the deviations from the balanced model to the control system which predicts the tendencies to changing the air situation in real time. …”
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Enhancing Air Quality Forecasting Using Machine Learning Techniques by Zeinab Shahbazi, Zahra Shahbazi, Slawomir Nowaczyk

“…In envisioning a future where urban commuting becomes synonymous with eco-friendliness and air quality improvement, a comprehensive platform harnesses the power of data analytics and real-time information to empower commuters and city planners alike. Its intelligent algorithms continuously analyse air quality information, allowing it to predict and address poor air quality. …”
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Determination of the area index of lettuce leaves with a monocular camera by Laimonas Kairiūkštis, Başak Yalçıner, Emre Özkul

“…The integration of Gaussian Mixture Model clustering with the dataset further enhanced the precision of the lettuce growth and harvest predictions. …”
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SIMULATION OF PERIPHERAL DEVICE SECURITY: IMPLEMENTATION AND PRACTICAL EVLUATION OF ADDRESS SPACES PROTECTION IN A TRUSTED MICROPROCESSOR EMULATOR by Mikael A. Kondakhchan, Nikita A. Grevtsev, Peter A. Chibisov

“…This approach aims to predict the operational characteristics of the final product through the analysis of its virtual model, reducing the risk of implementing vulnerable design solutions. …”
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Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates by N. Ragu Ramalingam, K. Johnson, M. Pagani, M. Pagani, M. L. V. Martina

“…The ML model serves as a surrogate, predicting the tsunami waveform on the coast and the maximum inundation depths onshore at the different test sites. …”
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Cryptographic hardness assumptions identification based on discrete wavelet transform by Ke Yuan, Yu Du, Yizheng Liu, Rongjin Feng, Bowen Xu, Gaojuan Fan, Chunfu Jia

“…Experimental results demonstrate that the proposed scheme accurately predicts cryptographic hardness assumptions, achieving an Accuracy, Recall, Precision, and F1 Score of 0.8500, 0.8533, 0.8500, and 0.8493, respectively, in the mixed plaintext scenario.…”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. …”
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Identification of markers correlating with mitochondrial function in myocardial infarction by bioinformatics. by Wenlong Kuang, Jianwu Huang, Yulu Yang, Yuhua Liao, Zihua Zhou, Qian Liu, Hailang Wu

“…The Cytoscape and miRWalk databases were then used to predict the transcription factors and target miRNAs of the central MitoDEG, respectively. …”
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Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks by Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov

“…The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1–9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. …”
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Online Learning to Cache and Recommend in the Next Generation Cellular Networks by Krishnendu S. Tharakan, B. N. Bharath, Vimal Bhatia

“…An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). …”
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Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model by Juho Han, Sebeen Yoon, Mingyun Kang, Taehoon Kim

“…The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. …”
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The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…Second, the importance of each influencing factor is predicted using 4 machine learning methods. Finally, fault identification is carried out on the fault identification map, which is fused with multi-source feature information, using the Convolutional Neural Network Model. …”
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Photovoltaic Power Estimation for Energy Management Systems Addressing NMOT Removal with Simplified Thermal Models by Juan G. Marroquín-Pimentel, Manuel Madrigal-Martínez, Juan C. Olivares-Galvan, Alma L. Núñez-González

“…The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. …”
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Modeling the Effect of Greenways’ Multilevel Visual Characteristics on Thermal Perception in Summer Based on Bayesian Network and Computer Vision by Yongrong Zheng, Siren Lan, Jiayi Zhao, Yuhan Liu, Songjun He, Chang Liu

“…Using the hill climbing algorithm in R to construct a Bayesian network, model validation results indicated prediction accuracies of 0.799 for TS and 0.838 for TP. …”
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Intelligent error-proof logic multi-factor cooperative active optimization based on knowledge base and generative adversarial network by Jinming Liu, Sheng Yang, Dunlin Zhu, Tianyun Luo, Jinglong He, Shengyuan Li

“…In order to solve the reduced prediction accuracy caused by insufficient training samples, data imbalance and improper feature selection of the intelligent error prevention system in the power grid, an intelligent errorproof logic multi-factor cooperative active optimization based on a knowledge base and generative adversarial network was designed. …”
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A neural network design for black-box identification of converter impedance models in arbitrary operating conditions by CHEN Bing, ZHAO Chongbin, JIANG Qirong, WANG Xu, WANG Fangming

“…In the model verification phase, the network is fed with set operating conditions, achieving highly accurate identification of stable operating conditions and offline prediction.…”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

“…The results indicated that corrosion in the upper flange gave the most severe strength reduction up to 39.7 %, although this was effectively mitigated by CFRP reinforcement. The ML prediction showed that the CatBoost algorithm had the highest accuracy, with an R2 score of 0.954. …”
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