Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design by Yingzhao Shao, Junyi Wang, Xiaodong Han, Yunsong Li, Yaolin Li, Zhanpeng Tao

“…The accelerator design integrates data validation, Triple Modular Redundancy (TMR), and other techniques, optimizing a partial fault-tolerant architecture based on SEU sensitivity. …”
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Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. …”
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Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu

“…A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. …”
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Reinforcement Learning With Selective Exploration for Interference Management in mmWave Networks by Son Dinh-van, van-Linh Nguyen, Berna Bulut Cebecioglu, Antonino Masaracchia, Matthew D. Higgins

“…Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime.…”
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Integrative Analysis of Epidemiological Profile of Sensitization Clinical Laboratory Markers in Children: Cross-Sectional Study Results by L. G. Levina, L. S. Namazova-Baranova, E. A. Vishneva, K. S. Volkov, K. Y. Efendieva, A. A. Alekseeva, V. G. Kalugina, P. S. Arimova

“…Allergy diagnosis in children should be economically optimal and as sparing as possible, while ensuring the high quality of medical care. …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…This research aims to compare the key machine learning approaches, Namely Support Vector Machines (SVM), Random Forest (RF) and Decision Trees (DT), in their ability to classify Intrusion Detection Systems (IDS) via routing networks over distributed computing systems. In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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High-efficiency sparse convolution operator for event-based cameras by Sen Zhang, Fusheng Zha, Fusheng Zha, Xiangji Wang, Mantian Li, Wei Guo, Pengfei Wang, Xiaolin Li, Lining Sun

“…However, despite their inherent sparsity, most existing visual processing algorithms are optimized for conventional standard cameras and dense images captured from them, resulting in computational redundancy and high latency when applied to event-based cameras. …”
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Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields by Ala Guennich, Mohamed Othmani, Hela Ltifi

“…The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. …”
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Innovative Approaches to Sustainable Urban Planning: Analysing Current Trends by Meruyert Baidrakhmanova, Gani Karabayev, Seimur Mamedov

“…The findings are crucial for optimizing urban planning and land management, as well as identifying innovative solutions that contribute to more sustainable and resilient urban environments. …”
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Parameter Tuning for Local-Search-Based Matheuristic Methods by Guillermo Cabrera-Guerrero, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, Enrique Cabrera

“…One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

“…Furthermore, PSO offers computational efficiency advantages over Firefly and Ant Colony Optimization, reducing hyperparameter tuning time while improving ensemble performance. …”
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Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler by Zhigui Lin, Xiaofeng Zhang, Qi Liu, Jun Cui

“…We construct an FPGA + CPU heterogeneous detection system featuring a five-tuple segmented matching architecture, which integrates hash bitmap and shift-or algorithms to achieve fast-pattern matching. A hardware compiler supporting 10,000+ detection rules is developed, enhancing hardware adaptability through packet optimization and mask compilation. …”
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Survey of Quantum Generative Adversarial Networks (QGAN) to Generate Images by Mohammadsaleh Pajuhanfard, Rasoul Kiani, Victor S. Sheng

“…In addition, developing QGANs can generate and train parameters in quantum approximation optimization algorithms. One of the useful applications of QGAN is generating medical datasets that can generate medical images from limited datasets to train specific medical models for the recognition of diseases.…”
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Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital by Carolina Deina, Flavio S. Fogliatto, Mateus Augusto dos Reis, Beatriz D. Schaan

“…Within this group, 82.98% (512 patients) did not require hospitalization, while 17.02% (105 patients) were hospitalized at least once. …”
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Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning by Rakesh Batchala, Priyank Jain, Manasi Gyanchandani, Sanyam Shukla, Rajesh Wadhvani

“…The primary emphasis is on predicting and optimizing ventilation and sedation strategies for patients in Intensive Care Units. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…This work demonstrates the robustness and adaptability of ML approaches for modeling complex subsurface conditions, paving the way for optimized carbon sequestration strategies.…”
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Development and evaluation of accurate 3D human models using scan data: a comparison with SMPL and CLO models by Gyungin Jung, Minjoo Kang, Sungmin Kim

“…This study introduces a novel 3D human modeling framework that combines skeleton-driven deformation algorithms with high-resolution 3D body scan data to generate anatomically accurate virtual bodies. …”
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A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting by Yue Yu, Pavel Loskot, Wenbin Zhang, Qi Zhang, Yu Gao

“…In this paper, we improve the performance of single-channel forecasting algorithms by designing an interpretable front-end that extracts the spatial–temporal components from the input multivariate time series. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Our approach utilizes CLIP for zero-shot multimodal feature extraction of remote sensing images, followed by the design and optimization of a mapping network based on an improved Transformer with adaptive multi-head attention to align these features with the text space of GPT-2, facilitating the generation of high-quality descriptive text. …”
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