Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlie...
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Batik is a cultural heritage of Indonesia, recognized by WHO as an Intangible Cultural Heritage. Batik is dyed by skilled craftsmen who make patterns with dots and lines on the fabric from melted wax. The process is c...
Batik is a cultural heritage of Indonesia, recognized by WHO as an Intangible Cultural Heritage. Batik is dyed by skilled craftsmen who make patterns with dots and lines on the fabric from melted wax. The process is complicated, so few people can experience all the steps in crafting Batik. From these problems, immersive learning media are needed so everyone can learn and gain experience in Batik crafting from start to finish. In this study, we will present Nge-BatikVR, a serious game application that introduces Batik through Virtual Reality and offers an immersive experience of learning Batik from various regions with interactive hands-on feature aimed at people to better understand and learn Batik. Nge-BatikVR offers four main features called Sinau (Learn), Kuis (Quiz), Nge-Batik (Simulation) and Toko (Shopping), with the main purpose to present interactive and engaging media useful for introducing and learning Batik with a fully immersive experience.
Diagnosis of diabetes disease is very promising because it may create various other acute or chronic health problems in the human body. This study proposes a recommendation system for diagnosis and treatment of Diabet...
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Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s ...
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Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.
Question answering (QA) in Egyptian history presents a unique and complex challenge for Arabic natural language processing (NLP). This study aims to explore and assess how large language models (LLMs) can enhance the ...
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ISBN:
(数字)9798350362633
ISBN:
(纸本)9798350362640
Question answering (QA) in Egyptian history presents a unique and complex challenge for Arabic natural language processing (NLP). This study aims to explore and assess how large language models (LLMs) can enhance the accuracy and performance of Arabic question answering (QA), specifically in this domain. To conduct this investigation, we utilize two comprehensive datasets: the Arabic History-QA dataset and the Contextual Articles Dataset, which cover pivotal historical periods. We evaluate transformer-based models, including AraBERTv2, BERT-large-Arabic with Retrieval-Augmented Generation (RAG), fine-tuned LLaMa-2, and zero-shot LLaMa-3 with Retrieval-Augmented Generation (RAG). Through a rigorous and detailed evaluation process, we analyze how these models address various questions related to Egyptian history. This research contributes valuable insights into advancing the capabilities of Arabic NLP in specialized domains such as historical question answering. Our best results, summarized as the superiority of LLMs, beat those with transformers; additionally, the RAG significantly raised the performance level overall.
Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predict...
Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model’s ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates.
One of the major purposes of this study is to investigate the potential impact of gender and information and computertechnology (ICT) resources on students’ computational thinking (CT) competencies. To this end, the...
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The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and pre...
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ISBN:
(数字)9798350378511
ISBN:
(纸本)9798350378528
The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image classification. The research employed Apache Spark, a distributed computing framework, to manage large datasets and enhance the performance of the multilayer perceptron (MLP) model via hyperparameter tuning and cross validation. Utilizing resources more effectively and achieving faster training times were made possible by Apache Spark. To support data-driven decision-making, the study also emphasized the significance of distributed platforms for analyzing large amounts of real-time diabetic data. To produce discriminative features for classification, the VGG16 architecture was employed for feature extraction. In the last epoch, the MLP model performed remarkably well, with an accuracy of 97%. The study also underlined the value of distributed platforms for data-driven decision-making by analyzing substantial volumes of real-time diabetes data.
This article proposes StrawberryTalk, an Internet of Things (IoT) platform for image-based strawberry disease detection. StrawberryTalk reuses the wall-mounted monitoring cameras without extra hardware cost. The contr...
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In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic fil...
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