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Background Lupus nephritis (LN) is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), and the accurate classification of renal pathological subtypes is crucial for reducing mortality rates ...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infectio...

Abstract Sister chromatid cohesion (SCC) is mediated by a protein complex called cohesin and by regulatory proteins that control cohesin function. A commonly used approach to evaluate the involvement of cohesin regulator...

Abstract Background Collagenous colitis (CC) is diagnosed histologically and is characterised by a thickened subepithelial collagen band together with inflammatory and epithelial changes. Although routine haematoxylin an...

IntroductionAlthough virtual reality (VR) is increasingly deployed as a tool for education, little is known about how learning modality influences cognitive processing or whether wearable neuroimaging technologies can ac...

Abstract Accurate differentiation of pulmonary emphysema from chest X-rays remains challenging due to imbalanced data, image noise, and the limitations of traditional deep networks in managing diagnostic uncertainty. In ...

This study investigates the performance of the Random Forest algorithm in measuring the quality of Higher Education Institutions (HEIs) in Indonesia. The current reliance on administrative evaluations and conventional ac...

In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator's gaze. We first establish the reliability of instructors to assign similar quality to an aviator's scan ...

Background: Medical waste management in resource-limited healthcare facilities remains dominated by manual segregation, which is error-prone and difficult to standardize. Existing automated solutions often rely on cloud-...

Abstract Machine-generated music (MGM) has become a groundbreaking innovation with wide-ranging applications, such as music therapy, personalised editing, and creative inspiration within the music industry. However, the ...

"This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computat...

Abstract The growing importance of machine learning in geological data computing, as well as in the broader geoscience and energy sectors, makes it essential to evaluate the impact of data augmentation on model performan...

Abstract The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demons...

Abstract High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer’s disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challen...

People are increasingly reliant on the internet for communication and various devices in their daily lives. The Internet of Things (IoT) has dramatically changed many industries by allowing more automation and easy data ...

Abstract The use of animal-borne devices to measure acceleration has yielded insights into animal locomotion, behaviour and energy expenditure. We present the first systematic review of accelerometry on animals, spanning...

Options trading is a type of financial trading that can offer significant profit opportunities depending on market conditions, but its profit and loss structure is complex. Traditional trading decisions are often made ba...

Job-seeking tasks are always challenging. Often, job recommendation systems require human intervention in the job-seeking process. Therefore, the study focuses on recommendation of most relevant job sectors and prioritiz...

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced cl...

Los requerimientos para capturar información sobre la forma del cuerpo humano están creciendo rápidamente y no solo se limitan a estudios ergonómicos o diseño, sino que también resultan de interés para aplicacione...

Abstract In many real-world applications, binary and multi-class classification problems involving imbalanced data and overlapping boundaries present a significant challenge for traditional machine learning algorithms. I...

Abstract Financial institutions continue to face a significant challenge with money laundering and need intelligent, scalable ways to detect illegal financial activity. This paper introduces a machine learning-driven pla...

Abstract Background Radiomics analysis of 18F-FDG PET/CT enables extraction of high-dimensional quantitative features that may serve as imaging biomarkers for tumor biology. In particular, in non-small cell lung cancer (...