2025 May - 32 articles
This study presents a comprehensive evaluation of embedding techniques and large language models (LLMs) for Information Retrieval (IR) and question answering (QA) across languages, focusing on English and Italian. We address a significant research ga...
With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved...
This project consists of a digital platform named Tri-Collab, where investors, entrepreneurs, and other agents (mainly talents) can cooperate on their ideas and eventually co-create. It is a digital means for this triad of actors (among other potenti...
Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing en...
Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present...
With the development of the marine economy and the increase in marine activities, deep saturation diving has gained significant attention. Helium speech communication is indispensable for saturation diving operations and is a critical technology for...
Graduate employability remains a crucial challenge for higher education institutions, especially in developing economies. This study investigates the key academic and vocational factors influencing early employment outcomes among recent graduates at...
Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explo...
The present study proposes adaptive augmented reality (AR) architecture, specifically designed to enhance real-time operator assistance and occupational safety in industrial environments, which is representative of Industry 4.0. The proposed system a...
This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI...
The rapid advancements in artificial intelligence (AI) have significantly transformed various domains, including education, by introducing innovative tools that reshape teaching and learning processes. This research investigates the perceptions and a...
Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study...
In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, d...
Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defect...
To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic...
Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less s...
Effectively identifying factors related to user satisfaction is crucial for evaluating customer experience. This study proposes a two-phase analytical framework that combines natural language processing techniques with hierarchical decision-making me...
Multivariate time series data (MTSD) anomaly detection due to complex spatio-temporal dependencies among sensors and pervasive environmental noise. The existing methods struggle to balance anomaly detection accuracy with robustness against data conta...
In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enha...
Armed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi)...
This study evaluates edge and cloud computing paradigms in the context of data-driven condition monitoring of rotating electrical machines. Two well-known platforms, the Raspberry Pi and Amazon Web Services Elastic Compute Cloud, are used to compare...
Human visual attention is influenced by multiple factors, including visual, auditory, and facial cues. While integrating auditory and visual information enhances prediction accuracy, many existing models rely solely on visual-temporal data. Inspired...
Programmatic buying has attracted growing interest from manufacturers and has become a driving force behind the growth of digital advertising. Among various formats, mobile advertisements (ads) have emerged as a preferred choice over traditional ones...
Nuclear environments demand exceptional precision, reliability, and safety, given the high stakes involved in handling radioactive materials and maintaining reactor systems. Object-oriented assembly and disassembly operations in nuclear applications...
Text summarization is a complex and essential task in natural language processing (NLP) research, focused on extracting the most important information from a document. This study focuses on the Extractive and Abstractive approaches of Bengali Text Su...
The decision-making process to rule R&D relies on information related to current trends in particular research areas. In this work, we investigated how one can use large language models (LLMs) to transfer the dataset and its annotation from one l...
Deep learning applications for Edge Intelligence (EI) face challenges in achieving high model performance while maintaining computational efficiency, particularly under varying image orientations and perspectives. This study investigates the synergy...
As AI becomes indispensable in healthcare, its vulnerability to adversarial attacks demands serious attention. Even minimal changes to the input data can mislead Deep Learning (DL) models, leading to critical errors in diagnosis and endangering patie...
In this article, for the first time on this topic, we analyze the historical color palettes of Renaissance oil paintings by using machine-learning methods and digital images. Our work has two main parts: we collect data on their historical color pale...
Effective student feedback is fundamental to enhancing learning outcomes in higher education. While traditional assessment methods emphasise both achievements and development areas, the process remains time-intensive for educators. This research expl...
The intersection of medical image classification and deep learning has garnered increasing research interest, particularly in the context of breast tumor detection using ultrasound images. Prior studies have predominantly focused on image classificat...
A trajectory is a set of time-stamped locations of a moving object usually recorded by GPS sensors. Today, an abundance of these data is available. These large quantities of data need to be analyzed to determine patterns and associations of interest...
