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This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge comput...

This work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to le...

Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies h...

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classificat...

ObjectiveTo develop an accurate and efficient binary classification model for predicting the binding capacity of endocrine-disrupting chemicals （EDCs） to the G protein-coupled estrogen receptor （GPER）.MethodsGPER...

High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinic...

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One o...

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...

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...

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...

Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic ...

This study examines public sentiment toward Indonesia’s Constitutional Court (Mahkamah Konstitusi, MK) following its 2024 regional election ruling. Using sentiment analysis and Martin and White’s (2005) Appraisal The...

The foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machi...

"Falls pose a significant health risk to older adults, often resulting in serious injuries and reduced independence. This study explored two machine learning approaches for detecting falls using data from wearable sensor...

This dataset comprises physiological signals recorded via a wearable device (Empatica E4) during structured acute stress induction and both aerobic and anaerobic exercise sessions. Collected metrics include blood volume ...