An aspect-based sentiment analysis (ABSA) aims to perform a fine-grained analysis of text to identify sentiments and opinions associated with specific aspects. Recently, transformers and large language models have demonstrated exceptional performance...
The high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news d...
In recent years, the explainable artificial intelligence (XAI) paradigm is gaining wide research interest. The natural language processing (NLP) community is also approaching the shift of paradigm: building a suite of models that provide an explanati...
Swimming performance analysis increasingly depends on multimodal sensing systems that capture physiological and biomechanical signals in real-world aquatic environments. While progress has been made in sensor fidelity and automated analysis, the inte...
This review provides an overview of explainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between 25 March and 14 April 2024 identified 17 s...
Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Art...
Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represente...
Sentiment analysis is widely applied in the financial domain. However, financial documents, particularly those concerning the stock market, often contain complex and often ambiguous information, and their conclusions frequently deviate from actual ma...
The rapid growth of user-generated content in the digital space has increased the necessity of properly and interpretively analyzing sentiment and emotion systems. This research paper presents a new hybrid model, HAM (Hybrid Attention-based Model), a...
Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many o...
Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logi...
During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboa...
With the increasing use of machine learning across various fields to address several aims and goals, the complexity of the ML and Deep Learning (DL) approaches used to provide solutions has also increased. In the last few years, Explainable AI (XAI)...
This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as f...
Heavy metal enrichment in agricultural soils can affect crop safety, ecosystem functioning, and long-term land productivity, yet farm-scale screening is often constrained by limited routine monitoring data. This study develops a GIS-based framework t...
Digital fitness has become a widely used tool for remote exercise guidance, leveraging artificial intelligence to analyze exercise videos and support self-training. This paper introduces a method for self-training in golf, a sport where automated pos...
The lack of justification of the results obtained by artificial intelligence (AI) algorithms has limited their usage in the medical context. To increase the explainability of the existing AI methods, explainable artificial intelligence (XAI) is propo...
The increasing complexity and volume of cybersecurity logs demand advanced analytical techniques capable of accurate threat detection and explainability. This paper investigates the application of Large Language Models (LLMs), specifically qwen2.5:7b...
The Android operating system has become increasingly popular, not only on mobile phones but also in various other platforms such as Internet-of-Things devices, tablet computers, and wearable devices. Due to its open-source nature and significant mark...
Deep learning has achieved remarkable performance in skin lesion classification, but its lack of interpretability often remains a critical barrier to clinical adoption. In this study, we investigate the spatial properties of saliency-based model expl...
The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigatio...
In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a crit...
Background: Breast cancer is a leading cause of cancer-related mortality among women, and earlier diagnosis significantly improves treatment outcomes. However, traditional mammography-based systems rely on single-modality image analysis and lack inte...
The study introduces an AI-enhanced Environmental, Social, and Governance (ESG) framework that integrates explainable artificial intelligence (XAI) and bias-mitigation techniques to improve transparency and comparability of sustainability assessments...
Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data a...
Myocardial infarction (MI) remains one of the most critical causes of death worldwide, demanding predictive models that balance accuracy with clinical interpretability. This study introduces an explainable artificial intelligence (XAI) framework that...
The increasing prevalence of Arabic text in digital media offers significant potential for sentiment analysis. However, challenges such as linguistic complexity and limited resources make Arabic sentiment analysis (ASA) particularly difficult. In add...
Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpret...
The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainab...
Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially le...
Food pattern recognition plays a crucial role in modern healthcare by enabling automated dietary monitoring and personalised nutritional interventions, particularly for vulnerable populations with complex dietary needs. Current food recognition syste...
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but...
Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The ad...
Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an expla...
Explainable artificial intelligence (XAI) is crucial for enhancing transparency and trust in machine learning models, especially for tabular data used in finance, healthcare, and marketing. This paper surveys XAI techniques for tabular data, building...
The long-term overextraction of groundwater in the Beijing–Tianjin–Hebei region has led to the formation of the world’s largest groundwater depression cone and the most extensive land subsidence zone, posing a potential threat to th...
This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 o...
This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural n...
The increasing complexity of manufacturing processes demands accurate defect prediction and interpretable insights into the causes of quality issues. This study proposes a methodology integrating machine learning, clustering, and Explainable Artifici...
Mobile authentication is crucial for device security; however, conventional techniques such as PINs and swipe patterns are susceptible to social engineering attacks. This work explores the integration of touch stroke analysis and Explainable AI (xAI)...
Extreme precipitation events, rapid urbanization, and irregular land use have significantly increased flood risk in recent years. In order to mitigate risks and enhance urban resilience, there is a need for the integration of innovative approaches wi...
In human–robot collaborative environments, predicting and explaining robotic grasp failures is crucial for effective operation. While machine learning models can predict failures accurately, they often lack transparency, limiting their utility...
Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomar...
In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL m...
Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-ar...
Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountabili...
Quantum entanglement plays a fundamental role in quantum mechanics, with applications in quantum computing. This study introduces a new approach that integrates quantum simulations, noise analysis, and fuzzy clustering to classify and evaluate the st...
Selecting Construction and Demolition Waste (CDW) credits in LEED-certified projects is essential for sustainable building management, often requiring specialised expertise and contextual sensitivity. However, existing studies provide limited analyti...
Metal-composite joints, leveraging the high specific strength/stiffness and superior fatigue resistance of carbon fiber reinforced polymers (CFRP) alongside metallic materials’ excellent toughness and formability, have become prevalent in aeros...
As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of the...
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