Twenty‐five years of addressing cutting‐edge scientific, policy, and regulatory issues through collaboration: The Forum for Collaborative Research by Robin Schaefer, Alessi Ayvaz, Christopher R. Hoffman, Margot Yann, Zachary Rooney, Mitchell Leus, Shilpa Mitra, Veronica Miller, for the Forum for Collaborative Research

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Alzheimer’s Disease Detection Using Deep Learning and Federated Learning by Taha Bin Niaz, Usman Amjad, Humera

“…The goal of this paper is to defend sensitive patient data by proposing a deep learning algorithm that utilizes Federated learning techniques in an IoT-based edge computing framework. …”
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Real-Time Hand Gesture Recognition in Clinical Settings: A Low-Power FMCW Radar Integrated Sensor System with Multiple Feature Fusion by Haili Wang, Muye Zhang, Linghao Zhang, Xiaoxiao Zhu, Qixin Cao

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DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization. by Sundreen Asad Kamal, Youtian Du, Majdi Khalid, Majed Farrash, Sahraoui Dhelim

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AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO by Lorenzo Palazzetti, Daniele Giannetti, Antonio Verolino, Donato A. Grasso, Cristina M. Pinotti, Francesco Betti Sorbelli

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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

“…By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.…”
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A Review of the Authentication Techniques for Internet of Things Devices in Smart Cities: Opportunities, Challenges, and Future Directions by Ashwag Alotaibi, Huda Aldawghan, Ahmed Aljughaiman

“…Additionally, we examine cutting-edge developments that offer improved security and scalability, such as blockchain technology, biometric authentication, and machine learning-based solutions. …”
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Eyes on nature: Embedded vision cameras for terrestrial biodiversity monitoring by Kevin F. A. Darras, Marcel Balle, Wenxiu Xu, Yang Yan, Vincent G. Zakka, Manuel Toledo‐Hernández, Dong Sheng, Wei Lin, Boyu Zhang, Zhenzhong Lan, Li Fupeng, Thomas C. Wanger

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Deep Learning Scheduling on a Field-Programmable Gate Array Cluster Using Configurable Deep Learning Accelerators by Tianyang Fang, Alejandro Perez-Vicente, Hans Johnson, Jafar Saniie

“…This paper presents the development and evaluation of a distributed system employing low-latency embedded field-programmable gate arrays (FPGAs) to optimize scheduling for deep learning (DL) workloads and to configure multiple deep learning accelerator (DLA) architectures. …”
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High-Performance and Lightweight AI Model with Integrated Self-Attention Layers for Soybean Pod Number Estimation by Qian Huang

“…Finally, we aim to implement the complete system in edge devices and conduct real-world testing in soybean fields.…”
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Deep Learning System for E-Waste Management by Godfrey Perfectson Oise, Susan Konyeha

“…The deep learning system for e-waste management presented in this proposal is a transformative solution designed to address the escalating challenges of garbage collection and management in urban environments. …”
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Tuberculosis detection using few shot learning by Kamran Riasat, Akhtar Jamil, Shaha Al-Otaibi, Sania Zeb, Saima Riasat, Shamsa Kanwal

“…Owing to scarce annotated datasets in medical domain augmented datasets are generated which is not a recommended technique in medical domain. This study presents TB-FSNet consisting of Few Shot Learning - Prototypical Network (FSL-PT) with a modified MobileNet-V2 backbone, incorporating a Self-Attention layer. …”
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An Edge Computing-Based and Threat Behavior-Aware Smart Prioritization Framework for Cybersecurity Intrusion Detection and Prevention of IEDs in Smart Grids With Integration of Mod... by Abdulmohsen Algarni, Zulfiqar Ahmad, Mohammed Alaa Ala'Anzy

“…As smart grids grow and are integrated into energy distribution networks, these systems become more vulnerable to cybersecurity threats due to their increased connectivity, usage of IEDs, and reliance on digital communication channels. This study presents an edge computing-based, threat behavior-aware smart prioritization framework with binary and multidimensional classification and detection of cybersecurity intrusions through modified machine learning methods. …”
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A Comprehensive Review on ISAC for 6G: Enabling Technologies, Security, and AI/ML Perspectives by Sultan Aldirmaz-Colak, Mustafa Namdar, Arif Basgumus, Serdar Ozyurt, Selman Kulac, Nurullah Calik, Mehmet Akif Yazici, Ahmet Serbes, Lutfiye Durak-Ata

“…We also outline possible future research directions, with a specific emphasis on security, artificial intelligence, and machine learning.…”
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Exploring historical maps for insights into ecosystem extent and condition: a case study landscape in Poland by Marta Sylla, Iga Kołodyńska, Piotr Krajewski

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Deep Learning, Critical Thinking and Teaching for Law Reform by Anne Macduff

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Hybrid Machine Learning for IoT-Enabled Smart Buildings by Robert-Alexandru Craciun, Simona Iuliana Caramihai, Ștefan Mocanu, Radu Nicolae Pietraru, Mihnea Alexandru Moisescu

“…This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. …”
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Deep Learning-Based Diagnosis Algorithm for Alzheimer’s Disease by Zhenhao Jin, Junjie Gong, Minghui Deng, Piaoyi Zheng, Guiping Li

“…In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. …”
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A Deep Learning Biomimetic Milky Way Compass by Yiting Tao, Michael Lucas, Asanka Perera, Samuel Teague, Timothy McIntyre, Titilayo Ogunwa, Eric Warrant, Javaan Chahl

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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…Conventional techniques for identifying retinopathies depend on ophthalmologists manually examining retinal pictures, a laborious process prone to human error. By using cutting-edge algorithms and artificial intelligence (AI) to evaluate retinal images, automated image analysis presents a promising option that makes retinopathies in diabetes patients quickly, accurately, and scalable to identify. …”
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