Andreas Kanavos

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Verified

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• Doctor of Philosophy
• Professor (Associate) at Ionian University

Digital Twin (DT) technologies are widely discussed in the context of Industry 4.0 and advanced manufacturing; however, their role in supporting the sustainability and survival of academic spin-offs remains underexplored. This paper argues that, particularly in peripheral and resource-constrained innovation ecosystems, Digital Twins should be under...

As Artificial Intelligence (AI) becomes increasingly embedded in higher education systems, AI literacy is emerging as a critical leadership capability for enabling effective human–AI collaboration. This study examines how AI literacy influences leadership effectiveness and proposes a strategic AI Literacy Leadership Framework for higher education....

We consider a comprehensive framework to predict the probability of Bitcoin price discontinuities (jumps). To this end, we employ six classifiers namely, XGBoost, AdaBoost, Random Forest, Logistic Regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). We integrate real- ized measures based on high-frequency price data, alo...

This study investigates univariate multi-horizon forecasting of national electricity demand as a controlled benchmark for settings where exogenous drivers (e.g., weather and calendar variables) are unavailable or uncertain, through a comparative evaluation of representative deep learning architectures. The examined models include the Long Short-Ter...

Introduction Infant mortality is a pivotal indicator of community health and socioeconomic conditions. Despite global advancements in healthcare and significant reductions in infant mortality rates, substantial disparities persist, particularly in underserved populations. This research aims to tackle these disparities by enhancing the predictive ac...

Posture, defined as the body’s alignment relative to gravity, plays a vital role in musculoskeletal health by influencing muscle efficiency, joint integrity, and overall balance. The global shift to remote and sedentary work environments during the COVID-19 pandemic has amplified concerns regarding posture-related disorders and long-term ergonomic...

The increasing demand for scalable and privacy-preserving processing of legal documents has intensified the need for accurate Named Entity Recognition (NER) systems tailored to the legal domain. In this work, we introduce LegNER , a domain-adapted transformer model designed for both legal NER and text anonymization. The model is trained on a corpus...

Explainable machine learning is paramount in enhancing the transparency and interpretability of machine learning (ML) models, especially within the medical domain. This study employs six different ML algorithms to develop a predictive model for heart disease by utilizing a specific dataset dedicated to this condition. To augment interpretability, w...

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterog...

The limits of conventional recommender systems, such collaborative filtering, have made it more difficult to find suitable content inside large digital movie archives given the explosive growth of online streaming services. A issue known as the cold start problem, these systems often suffer from data sparsity, insufficient scalability, and an incap...

Effective detection of corn leaf diseases is vital for preventing significant agricultural losses. This paper presents a novel methodology that integrates Transfer Learning, Kernel Principal Component Analysis (Kernel PCA)-based feature enhancement, and bio-inspired optimization to improve disease classification on corn leaves. Several pretrained m...

Text classification remains a challenging task in natural language processing (NLP) due to linguistic complexity and data imbalance. This study proposes a hybrid approach that integrates grammar-based feature engineering with deep learning and transformer models to enhance classification performance. A dataset of factoid and non-factoid questions,...

Software defect prediction identifies defect-prone modules before testing, reducing costs and development time. Machine learning techniques are widely used, but high-dimensional datasets often degrade classification accuracy due to irrelevant features. To address this, effective feature selection is essential but remains an NP-hard challenge best t...

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduli...

In the digital age, the rapid proliferation of misinformation and disinformation poses a critical challenge to societal trust and the integrity of public discourse. This study presents a comprehensive machine learning framework for fake news detection, integrating advanced natural language processing techniques and deep learning architectures. We r...

Software defect prediction aims to identify defect-prone modules before testing, reducing costs and duration. Machine learning (ML) techniques are widely used to develop predictive models for classifying defective software components. However, high-dimensional training datasets often degrade classification accuracy and precision due to irrelevant o...

The rapid transmission and mutation rates of viruses necessitate efficient and precise classification methods to aid public health responses. Often constrained by culturing requirements and narrow taxonomic scopes, traditional diagnostic techniques struggle to keep pace with the rapidly expanding genomic landscape. This study introduces an advanced...

Glaucoma, a leading cause of blindness, is often detected early through structural changes in the optic nerve head of the retina. This research proposes a method for segmenting the optic disc and optic cup and extracting retinal features from fundus images. The segmentation is achieved using Active Contour, Level set, and K-Means clustering-based t...

In the era where social media significantly influences public sentiment, platforms such as Twitter have become vital in predicting stock market trends. This paper presents a cutting-edge predictive model that integrates historical stock market data, Twitter sentiment analysis, and an extensive array of tweet-related features. Utilizing advanced reg...

This paper advances real-time cursor control for individuals with motor impairments through a novel brain–computer interface (BCI) system based solely on motor imagery. We introduce an enhanced deep neural network (DNN) classifier integrated with a Four-Class Iterative Filtering (FCIF) technique for efficient preprocessing of neural signals. The un...

As cloud computing continues to evolve, efficiently managing resource allocation and resolving system bottlenecks remain pivotal challenges. This paper explores the application of Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), to these critical issues. We employ the MNIST dataset, typically used for image classification, as...

Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundar...

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of ke...

: Kefalonia, an island in the western part of Greece, renowned for its unique geomorphology, lush vegetation, and Mediterranean climate, is an ideal habitat for diverse bird species, making it a prime destination for birdwatching tourism and intercultural communication. This paper harnesses the power of user-generated content (tourists) by systemat...

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and...

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique...

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of...

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and...

Augmented Reality (AR) enhances learning by integrating interactive and immersive elements that bring content to life, thus increasing motivation and improving retention. AR also supports personalized learning, allowing learners to interact with content at their own pace and according to their preferred learning styles. This adaptability not only p...

This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML...

This study introduces Protectbot, an innovative chatbot framework designed to improve safety in children’s online gaming environments. At its core, Protectbot incorporates DialoGPT, a conversational Artificial Intelligence (AI) model rooted in Generative Pre-trained Transformer 2 (GPT-2) technology, engineered to simulate human-like interactions wi...

This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the sat...

Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scen...

Biosensors have gained significant attention in various fields such as food processing, agriculture, environmental monitoring, and healthcare. With the continuous advancements in research and technology, a wide variety of biosensors are being developed to cater to diverse applications. However, the effective development of nanobiosensors, particula...

Erythemato-squamous Diseases (ESD) encompass a group of common skin conditions, including psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. These dermatological conditions affect a significant portion of the population and present a current challenge for accurate diagnosis and class...

In the digital age, effective website promotion plays a pivotal role in attracting visitors to alternative forms of tourism. This study examines the websites of 177 UNESCO Global Geoparks and 190 International Dark Sky Parks, employing specific evaluation criteria essential for enhancing the promotion of alternative tourism forms such as geotourism...

This paper investigates the integration of Artificial Intelligence (AI) into systematic literature reviews (SLRs), aiming to address the challenges associated with the manual review process. SLRs, a crucial aspect of scholarly research, often prove time-consuming and prone to errors. In response, this work explores the application of AI techniques,...

Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset from the UC Irvine Machine Learning Repository (UCI) and employs the Extra Trees Classifier for...

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud ser...

In the aviation industry, the issuance of airside passes often encounters significant delays, posing logistical challenges and hindering crucial operations. This study delves into the potential of implementing blockchain technology, particularly smart contracts, to streamline and expedite airport security processes. Our analysis of data from leadin...

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit avai...

Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken lan...

The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it rele...

Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classificati...

Due to COVID-19 restrictions, many restaurants were forced to discontinue in-person service, either by locking down or finding alternative methods of operation. Despite the fact that, in the United States of America, digital restaurants have already been established for many years, in Greece, this phenomenon became popular during the pandemic. Thes...

Social media platforms have revolutionized information exchange and socialization in today’s world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and clustering techniques. We measure user engagement ba...

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendl...

The exploitation of all possible combinations of the non-common substructure of compounds using Simplified Molecular-Input Line-Entry System (SMILES) representations is an essential part in terms of accurate chemical information processing. SMILES is a widely used encoding for representing chemical compounds as strings of characters. In our paper,...

Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classificati...

The detection of fake news is a crucial task in today's society, given the widespread use of social media and online platforms. In this study, we investigate the application of Machine Learning (ML) algorithms for the detection of fake news. We consider two different datasets of categorized news articles of various sizes and apply various ML algori...

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the en...

Extracting molecular descriptors from chemical compounds is an essential preprocessing phase for developing accurate classification models. Supervised machine learning algorithms offer the capability to detect "hidden" patterns that may exist in a large dataset of compounds, which are represented by their molecular descriptors. Assuming that molecu...

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on...

Recently, there has been a huge spike in the number of automobiles in the urban areas of many countries, particularly in India. The number of vehicles are increasing rapidly and with the existing infrastructure, the traffic systems stand still during peak hours. Some of the main challenges for traffic management are the movement of overloaded vehic...

Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concer...

As the volume of data generated and stored on a daily basis is constantly increasing, the need for finding techniques in terms of the automated discovery of information from them has arisen. This purpose can be effectively solved with the use of text mining, which uses methods derived from data mining, information retrieval, machine learning, as we...

In the modern world, social media plays a crucial role in the interchange of information and socialization with users. Twitter is a known social media platform that allows users to make relationships with others and express their opinions. The current work aims to identify the level of user engagement on Twitter with the use of graph mining. User e...