Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. …”
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Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector by A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M.G. Giammarchi, N. Giudice, R.M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S.M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A.C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M.D.C. Torri, A. Triossi, C. Tuvè, C. Venettacci, G. Verde, L. Votano

“…We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. …”
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Using Interactive Whiteboards (IWB) in the English Classroom as Supporting Technology in the Teaching and Learning Process: Opportunities and Challenges by Tira Nur Fitria

“…They can be used in educational institutions combining traditional presentation benefits with cutting-edge technology. IWB technology consists of a digital board, computer, and projector, which can be linked to a personal computer for easy use. …”
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Large Vessel Segmentation and Microvasculature Quantification Based on Dual-Stream Learning in Optic Disc OCTA Images by Jingmin Luan, Zehao Wei, Qiyang Li, Jian Liu, Yao Yu, Dongni Yang, Jia Sun, Nan Lu, Xin Zhu, Zhenhe Ma

“…However, accurate quantification of the microvasculature requires the exclusion of large vessels, such as the central artery and vein, when present. To address the challenge of ineffective learning of edge information, which arises from the adhesion and transposition of large vessels in the optic disc, we developed a segmentation model that generates high-quality edge information in optic disc slices. …”
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A dual-stage deep learning framework for simultaneous fire and firearm detection in smart surveillance systems by Ram Pravesh, Bikash Chandra Sahana

“…Traditional video surveillance systems often treat fire detection and firearm recognition as separate tasks, missing the opportunity to address multiple security threats in an integrated manner. This paper presents a novel dual-stage deep learning framework for real-time, unified detection of fire and firearms in smart surveillance environments. …”
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Smart and Secure Healthcare with Digital Twins: A Deep Dive into Blockchain, Federated Learning, and Future Innovations by Ezz El-Din Hemdan, Amged Sayed

“…A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. …”
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A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT by Innocent Boakye Ababio, Jan Bieniek, Mohamed Rahouti, Thaier Hayajneh, Mohammed Aledhari, Dinesh C. Verma, Abdellah Chehri

“…The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. …”
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Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks by Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat, Bernardi Pranggono

“…Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. …”
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Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review by Juan Ochoa-Aldeán, Carlos Silva-Cárdenas, Renato Torres, Jorge Ivan Gonzalez, Sergio Fortes

“…Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. …”
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Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions by Nisha Thorakkattu Madathil, Fida K. Dankar, Marton Gergely, Abdelkader Nasreddine Belkacem, Saed Alrabaee

“…Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. …”
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A secure and efficient encryption system based on adaptive and machine learning for securing data in fog computing by Priyanka Rajan Kumar, Sonia Goel

“…However, its distributed and heterogeneous nature presents distinct security challenges. This research introduces a novel adaptive encryption framework powered by machine learning to address these security concerns. …”
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Deepfake Detection Using Spatio-Temporal-Structural Anomaly Learning and Fuzzy System-Based Decision Fusion by Brindha Subburaj, R. Ragavendra

“…These three frame variants present a rich representation of video for the deep learning model. 3 ResNet-50 models are employed as encoders to generate feature maps trained using above frame types, ensuring anomaly identification in spatial, temporal and structural domains. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). Five convolutional neural networks (EEGNetv1, EEGNetv4, EEGITNet, EEGInception, EEGInceptionERP) have been evaluated on resting-state EEG dataset comprising 88 subjects. …”
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DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model by Fatma S. Alrayes, Hamed Alqahtani, Wahida Mansouri, Asma Alshuhail, Wafa Sulaiman Almukadi, Ahmed Mahmud

“…Real risk and violence detection in surveillance videos signify a cutting-edge use of deep learning (DL) technologies. By using innovative neural networks, this plan proposes to improve safety by quickly classifying possible threats and occurrences of violence within surveillance footage. …”
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Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning by Mohammed Alghamdi, Hanan Abdullah Mengash, Mohammed Aljebreen, Mohammed Maray, Abdulbasit A. Darem, Ahmed S. Salama

“…Therefore, this study presents Advanced Consumer Product Recognition using the Aquila Optimization Algorithm with Deep Learning (ACPR-AOADL) technique. …”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). …”
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Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence by Kazi Ahnaf Alavee, Mehedi Hasan, Abu Hasnayen Zillanee, Moin Mostakim, Jia Uddin, Eduardo Silva Alvarado, Isabel de la Torre Diez, Imran Ashraf, Md Abdus Samad

“…Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. …”
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Federated XAI IDS: An Explainable and Safeguarding Privacy Approach to Detect Intrusion Combining Federated Learning and SHAP by Kazi Fatema, Samrat Kumar Dey, Mehrin Anannya, Risala Tasin Khan, Mohammad Mamunur Rashid, Chunhua Su, Rashed Mazumder

“…In this article, we propose a novel framework, FEDXAIIDS, converging federated learning and explainable AI. The proposed approach enables IDS models to be collaboratively trained across multiple decentralized devices while ensuring that local data remain securely on edge nodes, thus mitigating privacy risks. …”
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Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

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Efficient hardware implementation of interpretable machine learning based on deep neural network representations for sensor data processing by J. Schauer, P. Goodarzi, A. Schütze, T. Schneider

“…<p>With the rising number of machine learning and deep learning applications, the demand for implementation of those algorithms near the sensors has grown rapidly to allow efficient edge computing. …”
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