QoS-Aware Fault Detection in Wireless Sensor Networks by Alessandra De Paola, Giuseppe Lo Re, Fabrizio Milazzo, Marco Ortolani

“…The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.…”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. …”
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Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network by Jun Li, Meiqi Zhang, Kaixuan Wu, Hengxu Chen, Zhe Ma, Juan Xia, Guangwen Huang

“…Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. …”
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Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges by Chenzuo Ye, Hao Wu, Takashi Oguchi, Yuting Tang, Xiangjun Pei, Yufeng Wu

“…In contrast, data-driven models, primarily developed using machine learning and statistical algorithms, often provide acceptable predictive accuracy in assessing landslide susceptibility. …”
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Constitutive Model for Hot Deformation Behavior of Fe-Mn-Cr-Based Alloys: Physical Model, ANN Model, Model Optimization, Parameter Evaluation and Calibration by Jie Xu, Chaoyang Sun, Huijun Liang, Lingyun Qian, Chunhui Wang

“…The parameters and architecture of the ANN model are then systematically optimized using optimization algorithms to enhance training efficiency and prediction accuracy. …”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. …”
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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
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Low-Cost, LiDAR-Based, Dynamic, Flood Risk Communication Viewer by Debra F. Laefer, Evan O’Keeffe, Kshitij Chandna, Kim Hertz, Jing Zhu, Raul Lejano, Anh Vu Vo, Michela Bertolotto, Ulrich Ofterdinger

“…The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. …”
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Parallel Simplex Method Improvements by Bashir Salih, Muhammad Ali

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Development of the automatic system for switching on the generating equipment for parallel operation with the electric power system by V. A. Fyodorova, V. F. Kirichenko, G. V. Glazyrin

“…The system has extended functionality compared to analogs, allowing the use of traditional methods and the developed method of accelerated synchronization. The results predict reduction of capital costs for automation systems, as one device provides synchronization on several circuit breakers and reduction of generator operation costs, as using this system, machine parts exposed to unacceptable thermal and mechanical effects. …”
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Color-Coded Compressive Spectral Imager Based on Focus Transformer Network by Jinshan Li, Xu Ma, Aanish Paruchuri, Abdullah Alrushud, Gonzalo R. Arce

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Analyzing Fairness of Computer Vision and Natural Language Processing Models by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

“…Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. …”
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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain by Shangmin Chen, Yongshan Gao, Lin Du, Mengzhen Min, Lei Xie, Liping Li, Xiaodong Chen, Zhigang Zhong

“…This study included 13,074 middle-aged and older adults from the China health and retirement longitudinal study (wave 2011–2015) to separately develop four-year fall risk prediction models for older adults with and without pain, using five machine learning algorithms with 145 input variables as candidate features. …”
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Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles by Hamza Shahab Awan, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan

“…This study proposes novel approaches and evaluates prediction models with classic machine learning algorithms and cutting-edge deep learning architecture. …”
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Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…The results showed that NCTD consistently surpassed the majority of competing algorithms in nine out of ten datasets, indicating its potential as a robust tool for extending CNN applicability beyond conventional image-based domains, particularly in complex classification and prediction.…”
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Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care by Muhammad Hamza Shuja, Firzah Shakil, Syed Hasan Shuja, Minal Hasan, Maliha Edhi, Abeera Farooq Abbasi, Afia Jawaid, Shajia Shakil

“…This review examines the impact of AI on cardiology, focusing on its role in analyzing medical images, predicting risk, and managing chronic diseases. AI algorithms and intense learning models have demonstrated exceptional proficiency in interpreting imaging modalities, surpassing human diagnostic capabilities. …”
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MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models by Oussama Bouguerra, Bilal Attallah, Youcef Brik

“…In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. …”
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Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan, Kabilan Saravanan

“…We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. …”
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