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The effect of imbalanced data on the optical character recognition of Braille text is investigated by applying two techniques to a set of convolutional neural network image classification models. A multilabel classificat...

E-commerce companies face fierce competition. For e-commerce companies to succeed, they must keep and attract customers by offering them the best affordable services. Taking appropriate and timely actions to keep custome...

Abstract The Error Diffusion Learning Algorithm (EDLA) is a learning scheme that performs synaptically local weight updates driven by a single, globally defined error signal. Although originally proposed as an alternativ...

With the rapid advancement of online education, students’ sentiment feedback serves as a pivotal factor in enhancing course quality and refining pedagogical strategies. However, conventional sentiment analysis approach...

Abstract Federated Learning (FL) has been widely used in Internet of Things (IoT) environments as a promising decentralized framework capable of collaborative model training without exposing local data. Despite its advan...

The rapid expansion of digital communication has made social media an integral part of daily life, yet it has simultaneously amplified the reach of harmful behaviors. Early detection of cyberbullying is recognized as a c...

Abstract Individualized educational management cannot be avoided in meeting various learner needs in cases where teaching strategies are changed in line with the performance trends and preferences of students. Reinforcem...

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at...

Abstract The increasing availability of positional data in invasion sports like football drives the development of advanced performance metrics. Two widely adopted examples are space control, which quantifies spatial inf...

Autism Spectrum Disorder is a neurodevelopmental disorder with onset in early childhood and its diagnosis often requires clinical processes based on long, subjective observations. Although early diagnosis and interventio...

This study presents Duygu-Turk, a novel deep learning-based sentiment analysis framework specifically designed for the Turkish language which is characterized by its agglutinative and morphologically rich structure. Unli...

Abstract Matching college students with appropriate innovative training projects is a challenging task that often relies on static assignment techniques, which overlook individual interests, skills, and learning styles. ...

The rapid expansion of conversational artificial intelligence (AI) usage in healthcare, nutrition, and digital health education has made prompting a routine part of professional and personal practice. However, each AI pr...

Abstract The publication of DeepSeek-R1 in Nature marks a significant moment as the first widely recognized large language model to undergo formal peer review. While acknowledging its technical ambitions and contribution...

Abstract Persistent fault analysis is effective when a single element of the S-box is altered. However, most fault injection techniques tend to induce multiple faults. In such multiple-fault scenarios, existing key recov...

Abstract Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classifi...

The integration of artificial intelligence (AI) into human-robot interaction (HRI) in healthcare has fundamentally revolutionized the emotional, social, and cognitive interaction between humans and robotic systems. This ...

Background Identifying priority projects in industry–education integration is critical for optimizing policy support and resource allocation. While prior studies have primarily relied on qualitative assessments, there ...

Abstract This research is concerned with the innovative use of AI in cross-cultural music learning, more particularly in the creation of a multimodal learning model that combines audio identification, image analysis, and...

Abstract Efficiently and accurately extracting key information from videos has become a core challenge in the current computer vision. Traditional methods rely on manual features and shallow models, making it difficult t...

he orchestration of microservices in distributed cloud environments poses significant challenges due to the heterogeneous nature of cluster nodes and dynamic workload patterns. Traditional scheduling strategies in Kubern...

Abstract Accurate classification of phonocardiogram (PCG) signals is vital for the early detection of cardiovascular diseases (CVDs), a leading cause of global mortality. However, conventional machine learning and deep l...

Abstract With the increasingly widespread application scenarios of the Internet of Things, the demand for lightweight ciphers continues to grow. For some lightweight ciphers whose only nonlinear component is the AND gate...

Abstract Spatiotemporal analysis of professional sports like American football is a research area that has grown in recent years with the emergence of reliable tracking technologies for both players as well as the ball. ...