• Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification 

  Ranga, Deepak; Prajapat, Sunil; Akhtar, Zahid; Kumar, Pankaj; Vasilakos, Athanasios (Peer reviewed; Journal article, 2024)

  Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are ...

• PersianEmo: Enhancing Farsi-Dari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model 

  Hussiny, Mohammad Ali; Payenda, Mohammad Arif; Øvrelid, Lilja (Journal article; Peer reviewed, 2024)

  Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise ...

• A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance 

  Saba, Noushin; Zafar, Afia; Suleman, Mohsin; Zafar, Kainat; Zafar, Shahneer; Saleem, Adil Ali; Siddiqui, Hafeez Ur Rehman; Iqbal, Muhammad; Sajid Ullah, Syed (Peer reviewed; Journal article, 2024)

  Colon cancer remains a leading cause of cancer-related mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation by ...

• Citizens-Focused Design Principles for Human-Centered AI in Public Services 

  Schmager, Stefan; Pappas, Ilias; Vasilakopoulou, Polyxeni (Chapter; Peer reviewed, 2024)

  Artificial Intelligence (AI) technologies undergo rapidly increasing integration in all areas of everyday life, including healthcare, employment, and public services. To reconcile abstract theoretical concepts with the ...

• Advanced Human Pose Estimation and Event Classification using Context-Aware Features and XGBoost Classifier 

  Wahid, Wasim; Alarfaj, Aisha Ahmed; Alabdulqader, Ebtisam Abdullah; Sadiq, Touseef; Rahman, Hameedur; Jalal, Ahmad (Peer reviewed; Journal article, 2024)

  This paper presents an advanced approach to Human Pose Estimation (HPE) and Semantic Event Classification (SEC), emphasizing the need for sophisticated human skeleton models, context-aware feature extraction, and machine ...

• Isolating Coefficient-Based Framework to Recognize Influential Nodes in Complex Networks 

  Mohammad, Buran Basha; Dhuli, Venkata Sateeshkrishna; Enduri, Murali Krishna; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2024)

  Identifying influential nodes within complex networks holds significant importance for enhancing network resilience and understanding vulnerabilities, thereby providing insights for both theoretical exploration and practical ...

• Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning 

  Aqeel, Muhammad; Sohaib, Ahmed; Iqbal, Muhammad; Sajid Ullah, Syed (Journal article; Peer reviewed, 2024)

  Food adulteration poses significant health risks globally and is rigorously monitored by safety authorities. In developing nations, where milk is highly prone to contamination (with Brazil, India, China, and Pakistan ...

• Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models 

  Fernandes, Fara Aninha; Ge, Mouzhi; Chaltikyan, Georgi; Gerdes, Martin; Omlin, Christian Walter Peter (Journal article; Peer reviewed, 2024)

  Objectives: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures. ...

• Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions 

  Ranga, Deepak; Rana, Aryan; Prajapat, Sunil; Kumar, Pankaj; Kumar, Kranti; Vasilakos, Athanasios (Journal article; Peer reviewed, 2024)

  Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques ...

• Promoted Osprey Optimizer: a solution for ORPD problem with electric vehicle penetration 

  Liu, Ziang; Jian, Xiangzhou; Sadiq, Touseef; Shaikh, Zaffar Ahmed; Alfarraj, Osama; Alblehai, Fahad; Tolba, Amr (Journal article; Peer reviewed, 2024)

  This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted ...

• Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving 

  Pravallika, Ambati; Hashmi, Mohammad Farukh; Gupta, Aditya (Peer reviewed; Journal article, 2024)

  Self-driving cars, or autonomous vehicles (AVs), represent a transformative technology with the potential to revolutionize transportation. This review delves into the critical role of 3D object detection in enhancing the ...

• Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique 

  Ahmed, Saghir; Raza, Basit; Hussain, Lal; Sadiq, Touseef; Dutta, Ashit Kumar (Peer reviewed; Journal article, 2024)

  Deep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by ...

• Deep Learning-Based Brain Tumor Detection in Privacy-Preserving Smart Health Care Systems 

  Kusum, Lata; Singh, Prashant; Saini, Sandeep; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2024)

  Deep learning has been widely used in medical image processing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options available ...

• Explainable Deep Learning for Human Behaviour Understanding: Sleep Monitoring, Human Activity Recognition, and Future Opportunities for Healthcare 

  Dutt, Micheal (Doctoral dissertations at University of Agder;no. 511, Doctoral thesis, 2024)

  This Ph.D. thesis investigates the transformative potential of explainable deep learning in human behavior analysis, focusing on sleep monitoring and human activity recognition. The research addresses a critical need in ...

• An Interpretable Deep Learning-based Feature Reduction in Video-Based Human Activity Recognition 

  Dutt, Micheal; Goodwin, Morten; Omlin, Christian Walter Peter (Peer reviewed; Journal article, 2024)

  This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes ...

• Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders 

  Dutt, Micheal; Redhu, Surender; Goodwin, Morten; Omlin, Christian Walter Peter (Chapter, 2022)

  Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using ...

• Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network 

  Dutt, Micheal; Goodwin, Morten; Omlin, Christian Walter Peter (Peer reviewed; Journal article, 2021)

  Polysomnography (PSG), the gold standard for sleep stage classification, requires a sleep expert for scoring and is both resource-intensive and expensive. Many researchers currently focus on the real-time classification ...

• Information Diffusion Model in Twitter: A Systematic Literature Review 

  Firdaniza, Firdaniza; Ruchjana, Budi Nurani; Chaerani, Diah; Radianti, Jaziar (Peer reviewed; Journal article, 2022)

  Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics ...

• A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limit 

  Zhang, Jixian; Wang, Zhemin; Vasilakos, Athanasios; Li, Weidong (Peer reviewed; Journal article, 2024)

  Resource allocation in Internet of Vehicles (IoV) edge computing is currently a research hotspot. Existing studies focus on social welfare or revenue maximization. However, there is little research on lowest revenue ...

• Exploring Affinity-Based Reinforcement Learning for Designing Artificial Virtuous Agents in Stochastic Environments 

  Vishwanath, Ajay; Omlin, Christian Walter Peter (Chapter; Peer reviewed, 2024)

  Artificial virtuous agents are artificial intelligence agents capable of virtuous behavior. Virtues are defined as an excellence in moral character, for example, compassion, honesty, etc. Developing virtues in AI comes ...

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