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Abstractive Text Summarization (ATS) is a task to create a novel summary by generating fresh sentences incorporating new words or rephrasing the article. It is a complex task as the model needs to understand the semantic similarity between the sentences ...

Nowadays, Human Activity Recognition (HAR) is a key research area with many ubiquitous innovative solutions, where both accelerometer and gyroscope data provide information about an observed person’s physical activity. HAR offers a diverse ...

Hard exudate (HE) is a common manifestation of various eye diseases, such as diabetic retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to visualize and quantify these exudates using deep learning (DL) and image ...

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-...

Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due ...

Car driving security is an inevitable factor due to the growing number of vehicles and the increased occurrence of road accidents. The driver assistance systems play a vital role in preventing 90% of the traffic accidents. Driver drowsiness and ...

Plant diseases can harm crops and reduce the amount of food that can be cultivated, which is problematic for farmers. Technology is being utilized to develop computer-based programs that can recognize plant diseases and assist farmers in making ...

This paper addresses the significant challenge of predicting the Remaining Useful Life (RUL) of mechanical equipment, a critical aspect of predictive maintenance and reliability engineering. Traditional deep learning methods in RUL prediction ...

• Novel Hybrid Model: Hybrid model boosts RUL prediction accuracy with self-supervised learning and Transformers.
• Uses Non-Full Lifecycle Data: Uses partial lifecycle data for broader time-series insight extraction.
• Mitigating ...

Face detection (FD) is considered to be of utmost significance in monitoring and safety systems. Finding concealed faces is a crucial research issue, particularly in populated or high-security settings like events and retail stores, where they ...

The emergence of 7T MRI scanners offers groundbreaking potential in medical imaging, providing ultra-high resolution compared to standard 3T MRI machines. However, widespread adoption faces challenges due to high costs and safety concerns. Our ...

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep ...

Most recently, the number of students dropping out of universities or higher education institutions increased dramatically. This might be partly because of the students’ limited capability exploring their own learning paths within a certain course. The ...

Cervical cancer continues to be a significant global health issue, ranking as the fourth most prevalent cancer affecting women. Enhancing population screening programs by refining the examination of cervical samples conducted by skilled ...

Deploying 5G networks in urban areas is crucial for meeting the increasing demand for high-speed, low-latency wireless communications. However, the complex topography and diverse building structures in urban environments have challenges in ...

The intricacy of the Deep Learning (DL) landscape, brimming with a variety of models, applications, and platforms, poses considerable challenges for the optimal design, optimization, or selection of suitable DL models. One promising avenue to ...

Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of ...

In an era where the demand for efficient and practical machine learning (ML) solutions on resource-constrained devices is evergrowing, the realm of tiny machine learning (TinyML) emerges as a promising frontier. Motivated by the need for ...

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of ...

In the field of drug discovery, the automated detection and analysis of cell nuclei within biological images play a pivotal role in understanding cellular responses to potential drug compounds. Leveraging the capabilities of DL techniques, this research ...

Cardiovascular diseases (CVD) rank among the leading global causes of mortality. Early detection and diagnosis are paramount in minimizing their impact. The application of ML and DL in classifying the occurrence of cardiovascular diseases holds ...