CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data by Jakub Malinowski, Marcin Kostrzewa, Michał Balcerek, Weronika Tomczuk, Janusz Szwabiński

“…Change point detection has become an important part of the analysis of the single-particle tracking (SPT) data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. …”
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Intelligent visibility forecasting at airports: a systematic review by Décio Alves, Margarida Belo-Pereira, Fábio Mendonça, Fernando Morgado-Dias

“…This review synthesizes findings on data-driven solutions, including machine learning and deep learning, that harness large datasets to reveal hidden patterns, offering better performance and adaptability. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…This study leverages AI methods to develop nine classification models using supervised machine learning classifiers. The author has implemented several machine learning models, including bagging, multi-layer perceptron, logistic regression, extreme gradient boosting, and random forest. …”
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Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg by Erfan Maleki, Sara Bagherifard, Okan Unal, Mario Guagliano

“…AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. …”
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Determination of Geochemical Background and Baseline and Research on Geochemical Zoning in the Desert and Sandy Areas of China by Weiji Wen, Fan Yang, Shuyun Xie, Chengwen Wang, Yuntao Song, Yuepeng Zhang, Weihang Zhou

“…This research will greatly enhance our ability to interpret the geochemical distribution and evolutionary patterns of desert elements in China, and it has important scientific significance and practical value for desert research.…”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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High-speed encrypted traffic classification by using payload features by Xinge Yan, Liukun He, Yifan Xu, Jiuxin Cao, Liangmin Wang, Guyang Xie

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Quality Metrics of Automated Machinery in Potato Plant Cultivation for Breeding and Seed Production by N. V. Sazonov, M. A. Mosyakov, V. S. Teterin, N. S. Panferov, M. M. Godyaeva, M. S. Trunov

“…(Research purpose) The research aims to determine the patterns of changes in the quality indicators of the machinery employed in cultivating potato plants. …”
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Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…The performance of several advanced machine learning models was assessed, with the Efficient Linear Support Vector Machine (ELSVM) showing the highest accuracy in daily rainfall forecasting, yielding an R² value of 0.3868, indicating its ability to effectively capture the variability in rainfall. …”
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Botnet detection in internet of things using stacked ensemble learning model by Mudasir Ali, Muhammad Faheem Mushtaq, Urooj Akram, Daniel Gavilanes Aray, Manuel Masias Vergara, Hanen Karamti, Imran Ashraf

“…The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. …”
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Detection and Mitigation of Distributed Denial of Service Attacks Using Ensemble Learning and Honeypots in a Novel SDN-UAV Network Architecture by Mohamed Amine Ould Rabah, Hamza Drid, Yasmine Medjadba, Mohamed Rahouti

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Lightweight ECG signal classification via linear law-based feature extraction by Péter Pósfay, Marcell T Kurbucz, Péter Kovács, Antal Jakovác

“…The method identifies linear laws that capture shared patterns within a reference class, enabling compact and verifiable representations of time series data. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

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Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel

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Research and predictive analysis of pyrolysis characteristics of multi-source organic solid wastes by ZHANG Zihang, XING Bo, MA Zhongqing, HU Yanjun, ZHANG Zhixiao, YUAN Shizhen, LU Rufei, CHEN Yingquan, WANG Shurong*

“…Experimental determination of thermochemical conversion characteristics of multi-source organic solid wastes is a time-consuming and labor-intensive process. By leveraging machine learning methods, the correlation mechanism between different feedstock properties and thermochemical characteristics can be explored to enable fast and accurate prediction. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

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Major depressive disorder recognition based on electronic handwriting recorded in psychological tasks by Chong Li, Kunxue Zhang, Qunxing Lin, Shan Huang, Wanying Cheng, Yueshiyuan Lei, Xinyu Zhao, Jiubo Zhao

“…Abstract Background This study aimed to determine whether handwriting patterns are altered in individuals experiencing depressive episodes. …”
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Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI by Nisreen Innab, Shtwai Alsubai, Ebtisam Abdullah Alabdulqader, Aisha Ahmed Alarfaj, Muhammad Umer, Silvia Trelova, Imran Ashraf

“…The performance of the proposed model is compared with other machine learning models to show the dominance of the proposed model. …”
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Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity by Huang-Nan Huang, Hong-Min Chen, Wei-Wen Lin, Rita Wiryasaputra, Yung-Cheng Chen, Yu-Huei Wang, Chao-Tung Yang

“…<b>Conclusions:</b> Our findings highlight the potential of machine learning-driven echocardiogram analysis to enhance patient care by providing accurate, data-driven assessments.…”
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Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks by Saúl Pérez-Fernández, Alejandro Buendía-Roca, Carlos González-Gutiérrez, Francisco García-Riesgo, Javier Rodríguez-Rodríguez, Santiago Iglesias-Alvarez, Julia Fernández-Díaz, Francisco Javier Iglesias-Rodríguez

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