Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices.…”
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Malicious Traffic Detection on Tofino Using Graph Attention Model by Xichang Gao, Lizhuang Tan, Shengpeng Chen, Peiying Zhang, Jian Wang

“…Subsequently, Maltof runs a lightweight Edge-based Graph Attention Network model on the CPU data plane of the switch, performing in-depth analysis on suspicious packets identified in the initial screening, learning and capturing complex relational features in network traffic to further determine whether malicious behavior is present. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…This work implements a zero-trust security approach for storing information in local device edges and only sharing the weights that were learned with the centralised federal network. …”
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Deep Learning Models for Rotated Object Detection in Aerial Images: Survey and Performance Comparisons by Jiaying He, K. L. Eddie Law

“…Rotated object detection in aerial images presents unique challenges due to the variability in object orientation and aspect ratios. …”
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Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine. by Varun Malik, Ruchi Mittal, Deepali Gupta, Sapna Juneja, Khalid Mohiuddin, Swati Kumari

“…These methodologies empower the distinguishing proof of medication targets and the improvement of customized treatment techniques that might actually upgrade endurance results for NSCLC patients. Using cutting-edge methods of feature extraction and transfer learning, we present a drug discovery model for the identification of therapeutic targets in this paper. …”
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Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment by Shu-Ping Yeh, Sonia Bhattacharya, Rashika Sharma, Hassnaa Moustafa

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Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer Chickens by Hengtai Li, Hongfei Chen, Jinlin Liu, Qiuhong Zhang, Tao Liu, Xinyu Zhang, Yuhua Li, Yan Qian, Xiuguo Zou

“…Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming.…”
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Deep Learning with Convolutional Neural Networks: A Compact Holistic Tutorial with Focus on Supervised Regression by Yansel Gonzalez Tejeda, Helmut A. Mayer

“…In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. …”
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Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques by S. Vijayalakshmi, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

“…Abstract Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. …”
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Enhancing Medical Education: Building a Comprehensive E-Learning Platform with CodeIgniter 4 by Meftah Zouai, Ahmed ALOUI, Houcine BELOUAAR, Ilyes Naidji, Okba KAZAR

“…This article explores creating and implementing a cutting-edge e-learning platform designed exclusively for medical education. …”
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Learnings from the 2024 Utah Cardiac Recovery Symposium: A Roadmap for the Field of Myocardial Recovery by Muthu Kumar Krishnamoorthi, Konstantinos Sideris, Arvind Bhimaraj, Stavros G. Drakos

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A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things by Wu Song, Xiangyuan Zhu, Sheng Ren, Wenxue Tan, Yibo Peng

“…Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. …”
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Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping by Lung‐Fai Tuen, Ching‐Lieh Li, Yu‐Jen Chi, Chien‐Ching Chiu, Po Hsiang Chen

“…Abstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. …”
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Privacy-Preserving Deep Learning: A Survey on Theoretical Foundations, Software Frameworks, and Hardware Accelerators by Eric Jahns, Milan Stojkov, Michel A. Kinsy

“…Deep Learning as a Service (DLaaS) has become a cornerstone in enabling access to deep learning capabilities, allowing users to train models or leverage pre-trained ones through APIs. …”
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Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. by Jamil Ahmad, Khan Muhammad, Sung Wook Baik

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TrapMI: A Data Protection Method to Resist Model Inversion Attacks in Split Learning by Hyunsik Na, Daeseon Choi

“…Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. …”
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Information extraction from green channel textual records on expressways using hybrid deep learning by Jiaona Chen, Jing Zhang, Weijun Tao, Yinli Jin, Heng Fan

“…In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. …”
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AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis by Mahmood Khalghollah, Azam Zare, Esmaeil Shakeri, Behrouz Far, Amir Sanati-Nezhad

“…However, accurate analysis of capillary-driven flow experiments remains constrained by the labour-intensive, time-consuming, and inconsistent nature of manual fluid path tracking. Here, we present AI-CMCA, an artificial intelligence framework designed for capillary microfluidic chip analysis, which automates fluid path detection and tracking using deep learning-based segmentation. …”
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Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis by Agostino Marengo, Vito Santamato

“…This paper presents a systematic review of quantum computing approaches to healthcare-related computational problems, with an emphasis on quantum-theoretical foundations and algorithmic complexity. …”
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Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Asim Wadood, Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Safeer Khan, M. Irfan Uddin, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson’s Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. …”
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