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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Department of Computing and Information System School of Engineering and Technology Sunway University No 5 Jalan University Selangor Bandar Sunway57500 Malaysia

ISBN: (纸本)9789819729760

The use of technology, such as Augmented Reality Immersive Learning (ARIL), in education to improve student learning by fusing the actual world with the virtual world is thoroughly examined. This research aims to ascertain how Augmented Reality (AR) technology might improve immersive learning for practical skills. AR has exciting opportunities for immersive learning since it can blend digital content with the real world. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and three databases—Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and the Education Resources Information Center (ERIC)—206 publications have been carefully selected for the study. Three main themes emerged from the 34 articles that met the qualifying requirements: Enhancing Learning Experiences with AR in Education, Innovative AR Applications in Education, and Immersive Technologies in Education: Challenges and Innovations. The three themes’ findings point to a common theme: the effective use of technology in the classroom to improve students’ immersive learning, confidence, memory retention abilities, positive concern, creative thinking, and self-awareness. Furthermore, the usage of AR may be coupled with other technologies such as chatbots, Artificial Intelligence (AI), Internet of Things (IoT), Mixed Reality (MR), Extended Reality (XR), and others. One significant finding from the study was that certain frameworks, such as the AR Immersive Learning Design (AILeaD), the Game-Based Assessment Framework (GBAF), and AR immersion (ARI), might serve as useful recommendations for implementing AR technology for educational purposes. Further studies should be conducted and considering that every student has a particular learning style and preference, more research should investigate using this framework and approach to help students learn theory or practical in various academic topics. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Contrastive Learning

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Twitter-Based Early Depression Detection Through Machine Learning 4th

Twitter-Based Early Depression Detection Through Machine Lea...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Ting, Kee Hui Majid, Mazlina Abdul Ibrahim, Ashraf Osman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (纸本)9789819729760

Depression, a prevalent mental health disorder, significantly affects individuals’ quality of life, emphasizing the critical need for early detection and intervention to ensure successful treatment. However, conventional methods for early depression detection have limitations that can impact accuracy and effectiveness. The proposed research advocates integrating technology to overcome these challenges, explicitly leveraging social media and machine learning. The focus is on detecting early signs of depression in Twitter users. A dataset of tweet texts is gathered and employed in machine learning classifiers, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). The research aims to identify the most effective algorithm for depression detection by comparing testing results and determining the classifier with the highest accuracy level. This contributes to achieving Sustainable Development Goal 3 (SDG3) by enhancing individuals’ quality of life. The result proved that the Support Vector Machine (Linear Support Vector Classifier) is best performed with the highest accuracy value of 90.57% among the three tested classification models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

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Computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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Theft Detection and Monitoring System Using Machine Learning 6th

Theft Detection and Monitoring System Using Machine Learning

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6th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2020

作者： Arora, Jatin Bangroo, Aayush Garg, Shivi Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India

ISBN: (纸本)9789811613418

Theft is a common criminal activity that is prevailing over the years and is increasing day by day. To tackle this problem, many surveillance systems have been introduced in the market. Some are simply based on video surveillance monitored by a human while some are AI-based capable of detecting suspicious activity and raising an alarm. However, none of them is intelligent enough to identify what kind of suspicious activity is being carried out and what kind of protective measures should be taken in real time. This paper presents the design of an effective surveillance system using machine learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Spatio-Temporal Clustering of Forest Fire Hotspots in the Wallacea Region in 2012 – 2022 Using Chronnet 11

Spatio-Temporal Clustering of Forest Fire Hotspots in the Wa...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Ismanto, Rido Dwi Vatresia, Arie Wahyuni, Intan Nuni Utama, Ferzha Putra Gusmanto, Rizki Anees, Mohd Talha Research Center for Computing National Research and Innovation Agency Bogor Indonesia Informatics-Engineering Faculty University of Bengkulu Bengkulu Indonesia Information System-Engineering Faculty University of Bengkulu Bengkulu Indonesia Department Geology Faculty of Science Universiti Sains Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331542313

Data from the Ministry of Environment and Forestry of Indonesia show that millions of hectares were burned by fires in Indonesia with 2015 being the greatest incidence in the last of ten years. Therefore, a research is required to examine the distribution pattern of hotspots for forest fires. This study used Chronnet to display the clustering of forest fire hotspot data in the Wallacea Region (North Maluku) between 2012 and 2022. The NASA FIRMS provided the MODIS data used in the study. Several findings have been made from the implemented Chronnet method. The degree of connectivity map shows a medium degree which suggests that the North Maluku frequently experiences forest and land fires of a moderate intensity. The strength map shows a complex pattern of fire activity, with high-intensity flames creating major concerns in some, particularly in West Halmahera Regency and low to moderate fire events occurring in most other places. The results from the Chronnet cluster show that most fires occurred in production forests, indicating the need for stronger fire prevention and regulations. While protected forests had fewer incidents, there are still several clusters that require attention. Additionally, the frequent fires in non-forest areas emphasize the need for better land-use planning and coordination. Finally, although conservation forests remained unaffected, it is crucial for the government to prioritize maintaining this record. © 2024 IEEE.

关键词： Premixed flames

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Optimum Resource Allocation at Emergency Department’s Yellow Zone Using Simulation and Data Envelopment Analysis 1

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4th International Conference on Mathematics, Statistics, and computing Technology, ICMSCT 2023

作者： Yusoff, Nazhatul Sahima Mohd Rasidi, Nur Fatini Nordin, Muhammad Irham Siregar, Budi Halomoan Fauzi, Mas Ayu Diana Mohd College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Machang Malaysia Faculty of Mathematics and Natural Science Universitas Negeri Medan Sumatera Utara Medan Indonesia Faculty of Business and Management Universiti Teknologi MARA Kelantan Machang Malaysia

ISBN: (数字)9789819734504

ISBN: (纸本)9789819734498

The Yellow Zone of Emergency Department Hospital Universiti Sains Malaysia (EDHUSM) plays an imperative role in providing emergency treatments to semi-critical patients who could possibly be critical if proper treatment is not administered within 30 min. Patients are clustered into four categories namely normal, semi-critical, asthmatic and pediatric patients. Generally, the efficiency of the Yellow Zone is evaluated based on its crowdedness and patient waiting period. Therefore, this study applied the hybrid method of encompassing Discrete Event Simulation to conduct proper planning, and Data Envelopment Analysis models such as Banker, Charnes and Cooper (BCC)-input-oriented and Super-Efficiency to assist the hospital’s management in improving patient flow and strategizing resource allocations and services in EDHUSM’s Yellow Zone. The simulation results revealed that the current average waiting time for normal and asthmatic patients exceeds 30 min. Meanwhile, the DEA model was developed to determine the most efficient series among 768 alternatives of resource allocations for doctors, nurses and beds. The improvement resulted in a significant decrease in average waiting time for normal patients from 88.71 to 16.28 min and for asthmatic patients from 40.57 to 8.58 min as equated to the actual present scenario. Increments of 2 doctors, 3 nurses and 2 beds are crucial in meeting the present demand while drastically improving the patient flow in Yellow Zone. The number of patients served increased by 13.95%. Ultimately, the strategically allocated resources will improve the quality, performance and services, and meet the Key Performance Indicators set in a more persistent and timely manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nursing

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Supplier Selection Incorporating Weights of Criteria using an Order Preference by Similarity Method and Interval-valued Intuitionistic Fuzzy Sets 8

Supplier Selection Incorporating Weights of Criteria using a...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Abdullah, Lazim Najib, Norliana Mohd Azzah Awang, Noor Universiti Malaysia Terengganu Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Kuala Terengganu Malaysia Universiti Teknologi Mara Kedah Branch Mathematical Sciences Studies College of Computing Informatics Mathematics Sungai Petani Campus Malaysia Universiti Teknologi Mara Mathematical Sciences Studies College of Computing Informatics Mathematics Shah Alam Malaysia

ISBN: (纸本)9798350315516

Choosing an appropriate supplier involving multiple choices is a complex decision to maximize the overall profit and increase customer satisfaction. This paper proposed the TOPSIS-based method and interval-valued intuitionistic fuzzy sets (IVIFS) to deal with multiplicity in solving food supplier selection. In this study, the qualitative evaluations concerning criteria are described by IVIFS linguistic variables. The linguistic evaluations are analyzed using the ten-step computational procedures including entropy weight of criteria and relative weight. The closeness coefficient and score degree of alternatives is obtained to select the best performer. The findings suggest that the supplier Hex-food (A3) was the most preferred as this alternative scored the highest closeness coefficient and score function and C3 (delivery performance) was the most important criterion. This study contributes to assisting the practicality of business in selecting the best supplier and provides some insights to the potential food manufacturers in choosing the appropriate supplier. © 2023 IEEE.

关键词： Customer satisfaction

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Data Augmentation Approach for Language Identification in Imbalanced Bilingual Code-Mixed Social Media Datasets 5

Data Augmentation Approach for Language Identification in Im...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Md Suhaimin, Mohd Suhairi Ahmad Hijazi, Mohd Hanafi Moung, Ervin Gubin Mohamad Hamza, Mohd Azwan Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia Ministry of Higher Education Malaysia Polytechnic and Community College Education Department Putrajaya Malaysia Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia Research Group Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9798350304152

Addressing the problem of language identification in code-mixed datasets poses notable challenges due to data scarcity and high confusability in bilingual contexts. These challenges are further amplified by the associated imbalance and noise characteristic of social media data, complicating efforts to optimize performance. This paper introduces an augmentation approach designed to enhance language identification in bilingual code-mixed social media data. By incorporating reverse translation, semantic similarity, and sampling techniques alongside customized reprocessing strategies, our approach offers a comprehensive solution to these complex issues. To evaluate the effectiveness of the proposed approach, experiments were conducted on language identification at both the sentence and word levels. The results demonstrated the potential of the approach in optimizing language identification performance, offering a compelling combination of generation techniques for addressing the challenges of language identification in code-mixed data. © 2023 IEEE.

关键词： Semantics

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Gold Prices Forecasting Using Recurrent Neural Network with Attention Tuned by Metaheuristics

Gold Prices Forecasting Using Recurrent Neural Network with ...

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Applied Intelligence and computing (AIC), IEEE World Conference on

作者： Andjela Jovanovic Tea Dogandzic Milos Dobrojevic Marko Sarac Nebojsa Bacanin Miodrag Zivkovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia

A large number of statistical studies have proven that the value of gold is highly valued nowadays, despite the numerous crises that have hit the global market. Due to the great risks that daily affect sudden changes in gold prices on the global market, it is of great benefit to develop tools that will enable automated monitoring and prediction of gold price changes. This can be quite helpful for those countries and major corporations whose economy depends on this. In this paper recurrent neural network with attention layer was used to forecast the gold price, while the hyperparameters of the network were tuned by the novel variant of the particle swarm optimization algorithm. Obtained results in terms of standard regression metrics were compared to those generated by other cutting-edge methods and proposed method proved to be very promising in this area.

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic

Optimizing Machine Learning for Malicious Blockchain Node De...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Nebojsa Bacanin Marko Vićentijevic Luka Jovanovic Angelina Njegus Branislav Radomirovic Miodrag Zivkovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance.

关键词： Proof of stake Analytical models Accuracy Prevention and mitigation Metaheuristics Machine learning Linear programming Security Particle swarm optimization Convergence

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