Density-Based Detection Rapid Exploration Random Tree for Multirobot Formation Cooperative Path Planning by Yuzhuo Shi, Yang Yang, Jinjun Liu, Kun Hao, Jiale Zhao, Haoyi Chai

“…Second, the repulsion field function in the artificial potential field (APF) is optimized for local collaborative obstacle avoidance to enable multiple robots, and a rotational potential field is introduced to solve the problems of unreachable targets and local oscillations. …”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…Almost 85% of companies polled said they were looking into anomaly detection (AD) technologies for their industrial image anomalies. …”
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Enhancing Real-Time Road Object Detection: The RD-YOLO Algorithm With Higher Precision and Efficiency by Weijian Wang, Wei Yu

“…Second, we design a high-resolution detection branch to enhance the accuracy of small-scale object detection. …”
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Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

“…Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

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Detection of Aflatoxigenic Fungi in Dairy Cattle Feed: Findings From Urban Districts of Tigray, Ethiopia by Sisay Weldegebriel Zeweld, Enquebaher Kassaye Tarekegn, Meressa Abraha Welearegay

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YOLO-UIR: A Lightweight and Accurate Infrared Object Detection Network Using UAV Platforms by Chao Wang, Rongdi Wang, Ziwei Wu, Zetao Bian, Tao Huang

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Robustness Benchmark Evaluation and Optimization for Real-Time Vehicle Detection Under Multiple Adverse Conditions by Jianming Cai, Yifan Gao, Jinjun Tang

“…This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. …”
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Machine Learning-Based Intrusion Detection Systems for the Internet of Drones: A Systematic Literature Review by Mostafa Ogab, Sofiane Zaidi, Abdelhabib Bourouis, Carlos T. Calafate

“…This infrastructure enables seamless real-time data exchange and collaborative operations across diverse applications, ranging from surveillance to delivery services, while ensuring adaptability, scalability, and security in dynamic aerial environments. …”
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Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region by Beijing XIE, Heng LI, Zheng LUAN, Zhen LEI, Xiaoxu LI, Zhuo LI

“…In the model compression phase, the proposed FCW-YOLO model undergoes channel-level sparsity through a collaborative pruning algorithm, automatically identifying unimportant channels and reducing them, resulting in the FCWP-YOLO model, achieving secondary lightweight design of the coal mine pedestrian-vehicle detection model. …”
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Research and practice on technologies for full stack deployment of autonomous networks by Fei XUE, Bin CHEN, Jing LIU, Xiaoyang LIANG, Lin ZHU, Feng WANG, Tian LI, Liang ZHANG, Zhenzhen CHEN, Xiao LI

“…Autonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The deployment phase of autonomous networks was systematically studied.Firstly, it elaborated on the independent deployment mode and full stack deployment mode of autonomous networks, and pointed out that full stack deployment was the main direction.Secondly, a detailed introduction was given to the full stack architecture with “five layers, dual domains, and four closed-loops”, which achieved full life cycle intelligence through a layered closed-loop design of resources and processes.Then, three core technologies for independent innovation were proposed: AI model training and inference integration to achieve rapid iterative updates of models, AI fabric technology to achieve customized application by rapid construction, and AI model cloud-edge collaborative deployment technology to achieve efficient application.Finally, the effectiveness of these three core technologies was verified through cases such as anomaly detection, smart telecommunication rooms, and equipment inspections.The deployment of autonomous networks was systematically explored, especially in terms of architecture design and core technology innovation, which had important reference value for telecommunication operators’ network digital transformation.…”
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A versatile HPLC method collaborated with NaDES-ultrasound-assisted LLE and machine learning for purity testing and trace analysis of 1,4-benzodiazepines in crime scene evidence, h... by Walaa Nabil Abd-AlGhafar, Rasha Abo Shabana, Rania El-Shaheny, Manar M. Tolba

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Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

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Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia by Jiehan Teoh, Zuliani Binti Zulkoffli, Kian Meng Yap, Huang Shen Chua

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Practical Implementation of Federated Learning for Detecting Backdoor Attacks in a Next-word Prediction Model by Jimmy K. W. Wong, Ki Ki Chung, Yuen Wing Lo, Chun Yin Lai, Steve W. Y. Mung

“…Abstract This article details the development of a next-word prediction model utilizing federated learning and introduces a mechanism for detecting backdoor attacks. Federated learning enables multiple devices to collaboratively train a shared model while retaining data locally. …”
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Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis by Kavinda Ashan Kulasinghe Wasalamuni Dewage, Raza Hasan, Bacha Rehman, Salman Mahmood

“…Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.…”
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AI Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation by Kavitha Dhanushkodi, S. Thejas

“…This comprehensive review examines the role of artificial intelligence (AI) in enhancing threat detection and cybersecurity, focusing on recent advancements and ongoing challenges in this dynamic field. …”
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Securing the internet of things: A comprehensive review of ransomware attacks, detection, countermeasures, and future prospects by Peizhi Yan, Tala Talaei Khoei

“…Looking ahead, our study highlights potential research directions such as advancing real-time detection, leveraging blockchain for enhanced security and fostering cross-sector collaboration to bolster collective threat intelligence. …”
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Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models by Umile Giuseppe Longo, Benedetta Bandini, Letizia Mancini, Mario Merone, Emiliano Schena, Alessandro de Sire, Pieter D’Hooghe, Leandro Pecchia, Arianna Carnevale

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Strengthening Outbreak Detection in Africa to Achieve the 7-1-7 Global Framework: Challenges and Opportunities by Frantz Jean Louis, Lisa Nichols, Cristina de la Torre, Anicet G. Dahourou

“…Field examples from Uganda, Senegal, and Nigeria demonstrate improved timeliness where coordinated investments and multisectoral collaboration have been implemented.ConclusionMeeting the 7-1-7 detection target requires integrated, country-owned strategies that align diagnostics, surveillance, workforce, and governance within resilient national health security frameworks, underpinned by sustained domestic investment.…”
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