Investigate the Use of Deep Learning in IoT Attack Detection by Mohamed Saddek Ghozlane, Adlen Kerboua, Smaine Mazouzi, Lakhdar Laimeche

“…This study contributes a comprehensive comparative analysis of deep learning models for IoT security, focusing on the effectiveness of weighted features in improving detection accuracy. …”
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The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization by Xu Junhao

“…An empirical analysis utilizing recent U.S. market data reveals that ML models improve risk assessment, asset selection, and adaptive portfolio allocation. …”
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MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach by Hua Yang, Teresa Gonçalves

“…The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. …”
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Measuring the impact of technological innovation, green energy, and sustainable development on the health system performance and human well-being: Evidence from a machine learning-... by Alaeddine Mihoub, Montassar Kahia, Mohannad Alswailim

“…Within this context, this study intended to examine the impact of recent innovations, logistical measures, Information and Communication Technology (ICT) diffusion, environmental quality improvements, economic growth, and green (renewable) energy exploitation on health performance and well-being, in Saudi Arabia from 1990 to 2022, by implementing machine learning models (random forest and gradient boosting) and regression algorithms (ridge and lasso). …”
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A Review of Smart Camera Sensor Placement in Construction by Wei Tian, Hao Li, Hao Zhu, Yongwei Wang, Xianda Liu, Rongzheng Yang, Yujun Xie, Meng Zhang, Jun Zhu, Xiangyu Wang

“…This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…To further enhance the training dataset with the aim of improving the robustness of the models, a Variational Autoencoder (VAE) was employed to generate synthetic data. …”
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The Impact of Biometric Surveillance on Reducing Violent Crime: Strategies for Apprehending Criminals While Protecting the Innocent by Patricia Haley

“…This paper examines the current state of biometric surveillance systems, emphasizing the application of new sensor technologies and machine learning algorithms and their impact on crime prevention strategies. …”
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Trust in Smart City Mobility Applications: A Multi-Agent System Perspective by Maryam Javaherian

“…The research aims to create algorithms and models for safe, efficient, sustainable mobility solutions, addressing data exchange and decision-making issues. …”
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Twofold dynamic attention guided deep network and noise-aware mechanism for image denoising by Zihao Chen, Alex Noel Joseph Raj, Vijayarajan Rajangam, Wei Li, Vijayalakshmi G.V. Mahesh, Zhemin Zhuang

“…Convolutional neural networks are given extensive attention towards noise removal due to their good performance over traditional denoising algorithms. With shallow conventional neural networks, the feature extraction ability is not profound. …”
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A Comparative Study of Breast Cancer Detection and Recurrence Prediction Using CatBoost Classifier by Rana Dhia’a Abdu-aljabar, Khansaa Dheya Aljafaar, Zinah Jaffar Mohammed Ameen, Hala A. Naman

“…The goal is to evaluate models using key metrics (sensitivity, specificity, precision, F1 score, accuracy, ROC, and AUC score). …”
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The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review by Saran Singh Gill, Hariharan Subbiah Ponniah, Sho Giersztein, Rishi Miriyala Anantharaj, Srikar Reddy Namireddy, Joshua Killilea, DanieleS.C. Ramsay, Ahmed Salih, Ahkash Thavarajasingam, Daniel Scurtu, Dragan Jankovic, Salvatore Russo, Andreas Kramer, Santhosh G. Thavarajasingam

“…Method: ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. …”
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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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Multi-objective optimization of HESS control for optimal frequency regulation in a power system with RE penetration by Ousama M.T. Ajami, Rodney H.G. Tan, Mithulan Nadarajah, Farah Adilah Jamaludin, Adlan Bagus Pradana

“…To optimize the control parameters with the best objectives, all possible sets of objectives with four different optimization algorithms are studied. The three control models considered for the HESS incorporate Virtual Synchronous Generator (VSG) or Virtual Inertia (VI) control with independent or simultaneous optimization of control parameters. …”
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Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning by Xueshen Li, Yu Gan, David Duan, Xiao Yang

“…Abstract In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep learning models, class activation mapping (CAM), and periodical feature detector for the purpose of evaluating human gastric motility by detecting the presence of gastric peristalsis and measuring the period of gastric peristalsis. …”
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New Strategies Based on Hierarchical Matrices for Matrix Polynomial Evaluation in Exascale Computing Era by Luisa Carracciuolo, Valeria Mele

“…Advancements in computing platform deployment have acted as both push and pull elements for the advancement of engineering design and scientific knowledge. Historically, improvements in computing platforms were mostly dependent on simultaneous developments in hardware, software, architecture, and algorithms (a process known as co-design), which raised the performance of computational models. …”
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Multimodal Deep Learning for Android Malware Classification by James Arrowsmith, Teo Susnjak, Julian Jang-Jaccard

“…DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. …”
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Energy Optimisation of Industrial Limestone Grinding Using ANN by Dagmara Kołodziej, Patryk Bałazy, Paweł Knap, Krzysztof Lalik, Damian Krawczykowski

“…This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. …”
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Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review by Thanh Dat Le, Nchumpeni Chonpemo Shitiri, Sung-Hoon Jung, Seong-Young Kwon, Changho Lee

“…The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. …”
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Population-level predictive variation in machine learning diagnosis of symptomatic bacterial vaginosis by Diandra P. Ojo, Cameron Celeste, Dion Ming, Ruogu Fang, Ivana K. Parker

“…To determine the ability of ML models to perform equitably, this study evaluates the performance of ML algorithms in predicting symptomatic BV across different ethnic groups using 16S rRNA sequencing data. …”
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Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis by Ancheng Chen, Xiangda Peng, Tao Shen, Liangzhen Zheng, Dong Wu, Sheng Wang

“…The shift from traditional experiment‐driven models to data‐driven and computationally driven intelligent models is already underway. …”
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