Enhancing Reliability in Redundant Homogeneous Sensor Arrays with Self-X and Multidimensional Mapping by Elena Gerken, Andreas König

“…Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. …”
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Research on Mobile Agent Path Planning Based on Deep Reinforcement Learning by Shengwei Jin, Xizheng Zhang, Ying Hu, Ruoyuan Liu, Qing Wang, Haihua He, Junyu Liao, Lijing Zeng

“…A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. …”
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Research Status and Progress of Acoustic Fire Extinguishing Technology by Xinyue Shi, Zhaojun Tian, Yi Lu, Qing Ye

“…Future advancements require breakthroughs in acoustic metamaterials, the integration of intelligent algorithms, and the collaborative optimization of multi-technology systems to facilitate the transition of acoustic wave-based fire suppression from laboratory settings to real-world industrial firefighting applications. …”
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An in-depth exploration of supervised and semi-supervised learning on face recognition by Purnawansyah, Wibawa Aji Prasetya, Widiyaningtyas Triyanna, Haviluddin, Darwis Herdianti, Azis Huzain

“…Although there are some instances of sub-optimal performance in certain algorithms like CNN on specific datasets, this research provides comprehensive insights into the effectiveness of various models in contexts of limited-label learning. …”
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Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge by Changyun Li, Yonghan Zhao, Qihui Mo, Zhibing Wang, Xi Xu

“…The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. …”
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AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes by David B. Olawade, Navami Leena, Eghosasere Egbon, Jeniya Rai, Aysha P. E. K. Mohammed, Bankole I. Oladapo, Stergios Boussios

“…While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. …”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN COMPUTER-AIDED DESIGN OF STAINED GLASS ART by Andrii Petrushevskyi, Yevhen Antonovych

“…The scientificance novelty The analysis of artificial intelligence capabilities in the form of automated algorithms for the development and optimization of stained glass design is a new research direction. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”
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VTGAN based proactive VM consolidation in cloud data centers using value and trend approaches by Aya I. Maiyza, Hanan A. Hassan, Walaa M. Sheta, Karim Banawan, Noha O. Korany

“…Additionally, incorporating VTGAN into the VM placement algorithm to disregard hosts predicted to become overloaded further improves performance. …”
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Modern advancements of energy storage systems integrated with hybrid renewable energy sources for water pumping application by Marwa M. Ahmed, Haneen M. Bawayan, Mohamed A. Enany, Mahmoud M. Elymany, Ahmed A. Shaier

“…Despite their potential, challenges such as renewable energy intermittency, optimal sizing, and cost-effectiveness are addressed. …”
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A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks by Qingxu Li, Hao Li, Renhao Liu, Xiaofeng Dong, Hongzhou Zhang, Wanhuai Zhou

“…Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…On this basis, we further propose a robust matrix-weighted multi-view hard c-means (MW-MVHCM) clustering, which assigns view-specific weights at the cluster level, allowing for more detailed intra-view contribution modeling. This matrix-weighted approach enables MW-MVHCM to dynamically capture the varying importance of each view across clusters, improving clustering performance. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Furthermore, based on the optimal cut-off value of the model, subjects were categorized into low- and high-risk groups, and differences in the actual number of RBD patients between the two sets were compared to assess the clinical effectiveness of the model. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…In this study, we developed machine learning (ML) algorithms to estimate 30-day mortality risk post-hip fracture surgery in the elderly using data from the National Surgical Quality Improvement Program (NSQIP 2012–2017, n = 62,492 patients). …”
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Battery Energy Storage Systems in Microgrids: A Review of SoC Balancing and Perspectives by Thales Augusto Fagundes, Guilherme Henrique Favaro Fuzato, Lucas Jonys Ribeiro Silva, Augusto Matheus dos Santos Alonso, Juan C. Vasquez, Josep M. Guerrero, Ricardo Quadros Machado

“…In addition, this article explores optimization processes aimed at reducing operational costs while considering SoC equalization. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…This model aims to enhance feature fusion, dynamically adapt to signal characteristics, optimize feature selection, and reduce computational complexity. …”
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Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System by Byung Hee Won, Hee-Sung Shin, Se Hwan Park, Seong-Kyu Ahn

“…It is also important to quantitatively evaluate the performance of NMA system including NRTA from the standpoints of Safeguards and Security by Design (SSBD) prior to construction of nuclear-material-handling facilities. Such evaluation improves safeguards effectiveness and efficiency. Modeling and Simulation (M&S) work is a good way to evaluate performance for various NMA systems and to determine the optimal one among different options. …”
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