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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Dimas Ramdhan Galih Dea Pratama Michael Francis Wijaya Muhammad Edo Syahputra Wihendro Computer Science Department Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

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.

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Factors Influencing Romanian Teachers' Choice of IT Training to Avoid Issues Raised by Online Education: A Data Mining Approach

Factors Influencing Romanian Teachers' Choice of IT Training...

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International Conference on Development and Application Systems

作者： Corina Simionescu Mirela Danubianu Adina-Luminița Bărîlă Bogdănel Constantin Grădinaru Doctoral School of Applied Sciences and Engineering Computers and Information Technology Ștefan Cel Mare University Suceava Romania Faculty of Electrical Engineering and Computer Science Ștefan Cel Mare University Suceava Romania

ISBN: (数字)9798350349290

ISBN: (纸本)9798350349306

Raising the students' academic performance and improving the overall quality of education is an important goal of our society. With the exploding development of information technologies, Educational Data Mining has become a growing activity that uses the essence of Data Mining concepts to help institutions learn useful information on various academic issues. Romanian schools are facing several problems and inequalities, many of which were accentuated during the Covid-19 pandemic, when, due to restrictions on physical proximity, classes were held entirely or partly online. This paper aims to presents an experimental study on modelling teachers' attitudes towards attending IT professional development courses to deliver quality online lessons. A real data set collected through a questionnaire answered by 956 teachers is used. The final goal was that, after the best-performing model was built, the features that are significant in this approach would be analyzed. These features are influential factors in teachers' choices. We initially considered four classification techniques, after which, we refined the optimization process for Random Forest, which proved, from the first experiments, the best performance.

关键词： Training Technological innovation Law Government Collaboration Turning Data mining

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Natural language Processing and Ontology based Decision Support System for Diabetic Patients 9

Natural language Processing and Ontology based Decision Supp...

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9th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2022

作者： Chandra, Ritesh Shukla, Abhaya Tiwari, Sadhana Agarwal, Sonali Svafrullah, M. Adiyarta, Krisna Indian Institute of Information Technology Allahabad Information Technology Department Prayagraj India Manipal Institute of Technology Udupi Electronics and Communication Department Manipal India Universitas Budi Program of Master of Computer Science Luhur Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

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 Diabetes Mellitus as suggested by the Clinical Practice Guidelines (CPG). A treatment plan is designed which includes medications and lifestyle changes, according to the patient information, symptoms, and lab tests. Various tasks such as gathering, modelling, and implementing diabetic domain expertise knowledge collected from the experts, CPG, and other sources are used to construct an ontology for diabetes and a decision support system to manage diabetes in its early stages. This work also uses an Optical Character Recognition (OCR) and a Natural Language Processing (NLP) model that help directly to read doctors prescription and extract useful information useful for developing the ontology. Web Ontology Language (OWL) is used to build the proposed ontology, and the Semantic Web Rule Language (SWRL) is used to create the rules for providing restrictions on the ontology. © 2022 Institute of Advanced Engineering and science (IAES).

关键词： Electronic Health Records Natural Language Processing Optical Character Recognition SWRL

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

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.

关键词： NB-IoT UAV GSK stop points optimization

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HistoryQuest: Arabic Question Answering in Egyptian History with LLM Fine-Tuning and Transformer Models

HistoryQuest: Arabic Question Answering in Egyptian History ...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Samaa Maged Asmaa ElMaghraby Ali Marzban Mohamed Essawey Amira Ahmed Esraa Negm Wael H. Gomaa Artificial Intelligence Program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Beni-Suef University

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.

关键词： Analytical models Accuracy Large language models Transformers Question answering (information retrieval) History

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data

Optimizing Random Forest Classification Using Chi-Square and...

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International Conference on Informatics and Computing (ICIC)

作者： Andri Roni Yunis Tanti Undergraduate Program of Computer Science Department Universitas Mikroskil Medan Indonesia Undergraduate Program of Information Systems Department Universitas Mikroskil Medan Indonesia Undergraduate Program of Information Technology Department Universitas Mikroskil Medan Indonesia

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.

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Diabetic Retinopathy Detection: A PySpark-Driven Approach with VGG 16 Feature Extraction and MLP Classification

Diabetic Retinopathy Detection: A PySpark-Driven Approach wi...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Toka Nabil Sama Ayman Samaa Ahmed Mawada Nagy Muhammad Megahed Abdulmoniem Elsaeed Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Infromation Technology and Computer Science Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

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.

关键词： Diabetic retinopathy Computational modeling Distributed databases Cluster computing Machine learning Multilayer perceptrons Feature extraction Retina Real-time systems Medical diagnosis

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Detect Areas of Upward and Downward Fluctuations in Bitcoin Prices Using Patterned Datasets

Detect Areas of Upward and Downward Fluctuations in Bitcoin ...

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information technology International Seminar (ITIS)

作者： Rizky Parlika Mustafid Basuki Rahmat Department of Doctoral Program of Information System Pascasarjana School Diponegoro University Semarang Indonesia Department of Informatics Faculty of Computer Science University of Pembangunan Nasional “Veteran” JawaTimur Surabaya Indonesia

Many researchers have proposed various theories and techniques to estimate the value of Bitcoin. However, there is still a great opportunity to suggest a new approach to Bitcoin forecasting. This research will examine the concept of forming a patterned dataset to prove the hypothesis regarding the start of a bitcoin price fluctuation until the end of the fluctuation using patterned dataset pairs in conditions of price decline (moon) and in conditions of price decline (crash). Patterned datasets were initially built from data on the movement of all cryptocurrency assets on a digital cryptocurrency trading market. The Patterned Dataset is programmed based on the response of all crypto assets to changes in the price of bitcoin over time. Patterned datasets are better able to describe the conditions of the digital cryptocurrency market, along with patterns of potential increases and decreases in Bitcoin prices in particular and cryptocurrencies in general. To deepen the observations, resampling of the patterned dataset was carried out with a 4-hour timeframe using the mean, median, min, max, and sum filter functions on pairs of crash and moon conditions per month. The results show that filter functions other than sum, namely mean, median, min, and max, provide markings of fluctuation area patterns that are easier to recognize. Two hypotheses related to fluctuations were also successfully proven in this research.

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IoT-Based Strawberry Disease Detection With Wall-Mounted Monitoring Cameras

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IEEE Internet of Things Journal 2024年第1期11卷 1439-1451页

作者： Lin, Yi-Bing Liu, Chun-You Chen, Wen-Liang Chang, Chia-Hui Ng, Fung-Ling Yang, Krista Hsung, Jerry Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan China Medical University College of Humanities and Sciences Taichung404 Taiwan National Cheng Kung University MiinWu School of Computing Tainan701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Yang Ming Chiao Tung University College of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Industrial Development Graduate Program College of Biological Science and Technology Hsinchu30010 Taiwan Proto Solution System Company Ltd. Hsinchu302052 Taiwan

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 contributions of StrawberryTalk are the utilization of IoT for automatic photo shoot and the wind detection mechanism to eliminate the obscure photos due to the wind effects. Also, the data preprocessing and the infection detection models are manipulated as IoT devices to simplify the implementation of the multicascade artificial intelligence (AI) models. We derive the relationship between the camera zoom factor and the distance between the camera and the strawberries for optimal disease detection. Accuracy of detection may be affected by obscure photos. In terms of eliminating obscure photos due to wind effect, we analytically derive the relationship between the wind alert delay and the number of obscure photos that must be retaken. For the greenhouses in the Bao Mountain, we only need to retake one photo. Based on the experiments, the mean average precision (mAP) of StrawberryTalk (to detect exact spots in a leaf) can be up to 92.37%, which is better than the previous approaches. To detect if a pot has infected leaves, the accuracy of StrawberryTalk can be up to 97.92% at the zoom factor 30×. In commercial operation, it is important to detect all infected strawberry pots. StrawberryTalk is able to detect all infected pots (i.e., recall is 100%) with a camera of zoom factor of 12× . The accuracy is 96.88%. © 2014 IEEE.

关键词： Cameras

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Behavior Profiling of Role-Playing Game Players Based on Heuristic Event Log Mining 25th

Behavior Profiling of Role-Playing Game Players Based on Heu...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Jason Tsai, Chia-Wei Yang, Chun-Wei Lafortune, Elie Department of Computer Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University Taichung406040 Taiwan

ISBN: (纸本)9789811995811

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 filtering rules are applied to eliminate uninteresting events. The proposed model uses sequences and tendencies of event types as the main mining criteria to extract significant player action behaviors. Action sequences reflect the interdependencies of different actions and how a player may use their insights about the game rules to form a (hidden) action priority list (APL) with respect to action types. By mining for significant events from play logs, we aim to produce the APM and APL for different players. In experiments, the fuzzy miner tool was used to analyze a few event logs for the World of Warcraft game to obtain some noticeable discoveries about the structural and timing patterns of effective players. Experimental results show that two players in the same class with similar contributions to encounters will have a similar mined APM and may not have a minimal delay in the timing of actions. Players with a high contribution to an encounter will have an APM like a simulated optimal player along with large thickness edges in the APM to indicate a significant routine of actions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Causal net Fuzzy miner Player behavior

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