Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in...
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Global anxiety and depression have become 25% more prevalent, with teenagers and women being the most affected. Approximately 280 million people suffer from depression. Doctors and psychologists are able to diagnose d...
Global anxiety and depression have become 25% more prevalent, with teenagers and women being the most affected. Approximately 280 million people suffer from depression. Doctors and psychologists are able to diagnose depressive disorders through counselling sessions and ask relevant questions to the subject, despite being vulnerable to mistakes due to the examiner's lack of experience. Therefore, automated depression detection development is necessary to validate doctor and psychiatrist assessment. Electroencephalography (EEG) is considered to be a popular option for the detection and investigation of various mental disorders. In this study, a comparison and analysis of each existing brain wave is carried out, namely Alpha (8-12Hz), Beta (13-30Hz), Theta (4–8 Hz), Delta (0.5-4 Hz) and Gamma (30-50Hz). From each wave, an accuracy testing is carried out for three groups of features: linear features, nonlinear features, and a combination of linear and nonlinear features. The given results demonstrate that the combination of linear and nonlinear data consistently yields the highest accuracy outcomes across all waves. Also, the combination of theta waves and linear nonlinear features contributed the highest accuracy (84%) using LSTM as the classifier.
Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer...
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Adaptive virtual learning environments provide an ideal foundation for enhancing personalized learning experience. Moreover, the incorporation of game elements enhances motivation levels, further enhancing the potenti...
Adaptive virtual learning environments provide an ideal foundation for enhancing personalized learning experience. Moreover, the incorporation of game elements enhances motivation levels, further enhancing the potential for improving learning outcomes. This paper explores the integration of fuzzy logic as a dynamic adaptivity tool in virtual educational games. By employing user-specific characteristics as input, including student progress, gaming time, and knowledge, the output of the fuzzy logic system represents the challenging level presented to the player in the educational game. Then, a set of rules is applied to tailor the complexity and pacing of challenges presented to learners. This adaptation extends to virtual character behavior, the learning path, and task complexity, aligning with learning objectives and proficiency levels. The study presents a practical implementation of this fuzzy logic-driven adaptivity mechanism in a virtual game environment designed for C++ programming education. The virtual game has been evaluated and the results of the evaluation showcase the potential of this approach in enhancing the learning process by tailoring the environment to learners’ specific needs and preferences.
Mental health disorders can affect a person's emotions and behavior and can impact their health and daily activities. The most serious consequence of poor mental health is death by suicide. The problem in this res...
Mental health disorders can affect a person's emotions and behavior and can impact their health and daily activities. The most serious consequence of poor mental health is death by suicide. The problem in this research is about mental health which can be analyzed using the field of natural language processing with the science of sentiment analysis on the developing TikTok platform. Content contained on the TikTok platform will have comments made by fellow users, then these comments are collected for sentiment analysis. This study utilizes a classification algorithm, namely naïve Bayes. The dataset obtained is preprocessed by text first, then the process from TF-IDF will be used to be able to see the appearance of words in the data. The data used in this study is 6300 data. The results of this study are accuracy as measured by a metric evaluation which produces 80.95%. The sentiment in this study focuses on positive and negative sentiments. With visualization, words that often appear are tired, sick, tired, and hurt in negative comments. Meanwhile, the words that often appear in positive comments are able, passionate, strong, and happy.
The main goal of this paper was to find out how the gender and age group acoustical models behave on audio data that is in no way related to the data corpora used to train and evaluate the models. These models could b...
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The ultimate challenge that arises in 6G and 5G and beyond networks is how the mobile networks of the future will be able to cope with the constantly increasing number of devices. In Multiuser Multiple-Input and Multi...
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In the modern era marked by technological advancement, vehicle recognition using computer vision has become increasingly important. The ability to accurately identify and classify vehicles has far-reaching implication...
In the modern era marked by technological advancement, vehicle recognition using computer vision has become increasingly important. The ability to accurately identify and classify vehicles has far-reaching implications, from efficient traffic management to better road safety. Some research on vehicle classification have been performed, but for multi-type vehicles still needs to be carried out due to the limited efforts on it, especially in vehicles variation, not only on specific vehicle such as cars, but also for two-wheeled and more. The problems of automated vehicle recognition are the variety of vehicle types on the roads; the ability to recognize many vehicles in real-time; as well as the accuracy of classifying vehicles where many obstacles and occlusions appears in the real-world. Hence, this research develops a web-based application that can recognize multi-type vehicles in real-time. The YOLOv5 method is used because of its advantages in fast recognition time and high accuracy, especially for surveillance data. Testing on dataset of multi-type vehicles including cars, trucks, motorcycles, and bicycles gave results with the accuracy rate of 93.23% and F1 score of 0.93. This research contributes to produce a web-based application for fast detecting and classifying multiple types of vehicles with the superior accuracy rate.
Coronavirus Disease of 2019 began in Wuhan in December 2019 and it was declared as a global pandemic by WHO. Until January 2021, it affected all of human activities on earth i.e., experiencing many obstacles from rest...
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In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be install...
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