Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks? by Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou, Fadi Aloul

“…In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. …”
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Fusing Sentinel-1 and Sentinel-2 Data With Machine Learning for Large-Scale Detection of Coastal Erosion and Accretion by Hairuo Yu

“…We employed advanced image preprocessing, robust coastline delineation using spectral indices and edge detection, and machine learning-based change analysis on multitemporal datasets spanning 2020–2022. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…This paper presents a Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model. …”
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HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). …”
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How do foodservice dietitians and dietetic students learn about environmental sustainability? A scoping review protocol by Christy Noble, Joanna McCormack, Lynda Ross, Denise Cruickshank, Andrea Bialocerkowski

“…Australian dietitians, however, are not required to learn about environmental sustainability during their tertiary education. …”
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Hardware-Software Stitching Algorithm in Lightweight Q-Learning System on Chip (SoC) for Shortest Path Optimization by Yahwista Salomo, Infall Syafalni, Nana Sutisna, Trio Adiono

“…This paper presents a novel hardware-software co-design approach to accelerate Q-learning algorithms using a RISC-V-based System-on-Chip (SoC) design. …”
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Advancing Water Quality Management: An Integrated Approach Using Ensemble Machine Learning and Real-Time Interactive Visualization by Jigna K. Pandya, Suresh S. Khandelwal, Rupesh Kumar Tipu, Kartik S. Pandya

“…This integration reflects the heightened prominence of deep learning methods and aligns the study’s methodology with cutting-edge research trends. …”
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Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…Encoding such data in a network-based or graph-based representation allows the explicit incorporation of such relationships into various biomedical big data tasks, including (but not limited to) disease subtyping, interaction prediction, biomarker identification, and patient classification. This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
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The utilization of 21st century ICT Tools in Teaching and Learning of Technology and Vocational Courses in a Nigerian Public University by Nnaemeka Martin Agbo, Ngozi Favour Onyemachi, Emmanuel Abiodun Ogunmola

“…Increasing trend of virtual learning facilitated by cutting-edge ICT tools and the the impact of the COVID-19 pandemic on the educational sector in Nigeria, underscore the importance of assessing their utilization in Technology and Vocational Education (TVE) to better prepare graduates for the global labor market. …”
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Automatic Liver segmentation Using Vector Field Convolution and Artificial Neural Network in MRI Images by Hassan Masoumi, Ahad Salimi, Hamidreza Sadeghi Madavani

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A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems by Zishuo Zhou, Zahid Akhtar, Ka Lok Man, Kamran Siddique

“…To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. …”
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GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning by Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang

“…E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity. This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems. …”
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Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning by Johannes Pauli, Maximilian Hoffmann, Ralph Bergmann

“…Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. …”
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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging by Arwa Alzughaibi, Adwan A. Alanazi, Mohammed Alshahrani, Ines Hilali Jaghdam, Abaker A. Hassaballa

“…This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. …”
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GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning by Tianhao Peng, Qiang Yue, Yu Liang, Jian Ren, Jie Luo, Haitao Yuan, Wenjun Wu

“…Most current methods analyze this complex task solely based on the frequency of student activities, overlooking the rich spatial and temporal features present in these activities, as well as the diverse textual content provided by various learning artifacts. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…They are still imprecise and ineffective, though. Deep Learning (DL), which can precisely learn the intrinsic features of the websites and recognize phishing websites, is one of the innovative techniques utilized to solve this issue. …”
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End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment by Cong Wang, Valerio Mariani, Frédéric Poitevin, Matthew Avaylon, Jana Thayer

“…As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. …”
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A Scalable Framework for Real-Time Network Security Traffic Analysis and Attack Detection Using Machine and Deep Learning by Zineb Maasaoui, Mheni Merzouki, Abdella Battou, Ahmed Lbath

“…Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. …”
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A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning by Pedro Franco-Gonçalo, Pedro Leite, Sofia Alves-Pimenta, Bruno Colaço, Lio Gonçalves, Vítor Filipe, Fintan McEvoy, Manuel Ferreira, Mário Ginja

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Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning by Mohamed Shenify

“…This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. …”
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