Scientific Publications in Information and Communication Technology
Recent Submissions
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Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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 ... -
Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology
(Peer reviewed; Journal article, 2024)Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. ... -
An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
(Peer reviewed; Journal article, 2024)Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such ... -
A Rule-Based Framework for Interpretable Natural Language Processing
(Doctoral dissertations at University of Agder; no. 500, Doctoral thesis, 2024)In recent years, neural network models have demonstrated remarkable results in natural language processing (NLP) tasks. These models have replaced complex hand-engineered methods for extracting and representing sentence ... -
Reference Design Model for a Smart e-Coach Recommendation System for Lifestyle Support Based on ICT Technologies
(Chapter; Peer reviewed, 2020)As acknowledged by the World Health Organization (WHO), the demographic development shows that by 2030, 8 out of the 10 foremost causes of death will be connected to risk conditions of lifestyle diseases, regardless of ... -
Identification of Nonlinear Causality in Multivariate Systems by Designing Interpretable Machine Learning Models
(Doctoral dissertations at University of Agder; no. 499, Doctoral thesis, 2024)The study focusing on inference and data analysis within networks has become increasingly important. This is due to the growing number of interconnected systems and the vast amounts of data they produce. Many of these ... -
The Art of Bootstrapping
(Chapter, 2020)Language workbenches are used to define languages using appropriate meta-languages. Meta-languages are also just languages and can, therefore, be defined using themselves. The process is called bootstrapping and is often ... -
A geometric street scattering channel model for car-to-car communication systems
(Chapter, 2011)This paper presents a geometric street scattering channel model for car-to-car (C2C) communication systems under line-of-sight (LOS) and non-LOS (NLOS) propagation conditions. Starting from the geometric model, we develop ... -
Towards Model-Based Reinforcement Learning for Industry-Near Environments
(Peer reviewed; Journal article, 2019)Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy ...
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