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26th International Computer Science and Engineering Conference, ICSEC 2022

作者： Lim, Tek Yong Multmedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9781665491983

A new design of web-based translation, also known as easyTranslate, is proposed for non-native English-speaking students to translate a paragraph of academic text. However, there is a need to determine whether this new web-based translation is usable or not for students during the Covid-19 pandemic. Thus, this paper describes a remote moderated usability testing of a web-based translation. Five undergraduate students took part in the usability testing through an online video conferencing tool. The results showed that all students successfully completed the given tasks and the new web-based translation is considered usable. The advantages and challenges of the remote moderated usability testing are discussed. © 2022 IEEE.

关键词： Students

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Efficient Contrast Subspace Mining Method for Categorical Data 6

Efficient Contrast Subspace Mining Method for Categorical Da...

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6th International Conference on Computer Science and Artificial Intelligence, CSAI 2022

作者： Sia Fui Sze, Florence Alfred, Rayner Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

ISBN: (纸本)9781450397773

Mining contrast subspace has recently received attention to identify contrast subspace where a query object is most likely similar to a target class but least likely similar to other class. It has many important applications in various domain such as healthcare, security, finance, and business. Tree-based contrast subspace mining method (TB-CSMiner) has been introduced that is capable to identify contrast subspace of query object in categorical data set. However, the effectiveness of the method has not been evaluated thoroughly. Besides, the efficiency of the method in finding contrast subspace has not yet been examined. Real world data sets more often than not containing large amount of data. It is important to have a method that can identify contrast subspace of query object not only effectively but also efficiently on large data set. This paper uses various classification methods to further evaluate the effectiveness of the TB-CSMiner and assesses the execution speed of the method in mining contrast subspace. TB-CSMiner is experimentally shown to be significantly faster and as effective in identifying contrast subspace of query object on real world categorical data sets. © 2022 ACM.

关键词： Trees (mathematics)

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ARCards: Marker-Based Augmented Reality Recognition for Business Cards 2

ARCards: Marker-Based Augmented Reality Recognition for Busi...

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2nd International Conference on Big Data Engineering and Education, BDEE 2022

作者： Liew, Jun-Xian Ng, Kok-Why Haw, Su-Cheng Ng, Sew-Lai Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

Augmented Reality (AR) is an advanced technology that can improve human life. It provides the users an interactive experience where the real world objects are combined with the computer-generated virtual elements such as sound, video, graphics, GPS overlays and more. This paper surveys on the existing AR applications and also introduces ARCards which is an AR mobile application for business cards using marker-based recognition approach. ARCards is designed to enhance a person's insight view of reality. It changes the users' perception by analyzing and implementing a new way of business card sharing where additional features and functions are well equipped by recognizing the markers on the business card. This could deliver an invaluable experience through AR when introducing a person or a company detail. More valuable contents can be included and displayed as virtual digital information and this is hardly to be fitted into a normal business card. AR markers, are trained to identify as a trigger to output the visual components stored digitally in a database. The main camera in the scene is replaced by ARCamera from Vuforia Engine to recognize the feature markers on a business card and automatically revokes the digital contents from the database to be rendered on the phone scene. Extended tracking is enabled so the users are not required to always place the business card in front of the camera for constantly tracking. The functionalities are combined with the physical world and instantly offer realtime feedback to the users. Recognition process takes less than a second to identify the important features. With ARCards, one can easily stand out from others. ARCards also received 86.7% of users' full satisfaction. © 2022 IEEE.

关键词： Augmented reality

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Metaheuristic Optimized Extreme Gradient Boosting for Solar Flare Prediction 16

Metaheuristic Optimized Extreme Gradient Boosting for Solar ...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Jovanovic, Luka Zivkovic, Miodrag Budimirovic, Nebojsa Petrovic, Aleksandar Strumberger, Ivana Bacanin, Nebojsa Singidunum University Faculty of Tehnical Sciences Belgrade11000 Serbia Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia

ISBN: (纸本)9798350347029

Intense electromagnetic activity on the Sun's surface can lead to events known as solar flares that often lead to mass ejections and other solar events. Sufficiently powerful solar events can make it to the Earth and interfere with telecommunication systems. Notably energetic events can even affect satellite and ground communication infrastructure potentially causing massive damage. Extensive monitoring systems in combination with robust forecasting techniques can be used as an early warning system to minimize potential damage. This work proposes a method based on extreme gradient boosting (XGBoost) applied to solar flare classification. As the performance of XGBoost heavily relies on adequate hyperparameter selection, a modified metaheuristic algorithm is also introduced to tune network hyperparameters. The introduced approach has been evaluated on a real-world dataset and a comparative analysis has been carried out against several contemporary algorithms tackling the same task under identical conditions. The introduced approach has attained promising outcomes. © 2023 IEEE.

关键词： Swarm intelligence

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A Great Deluge Algorithm for University Course Timetabling

A Great Deluge Algorithm for University Course Timetabling

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2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： Loke, Zi Xuan Goh, Say Leng Mountstephens, James Likoh, Jonathan Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Preparatory Centre for Science and Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350321302

Here we propose a two-stage approach to address the University Course Timetabling Problem. In the first stage, we utilize an existing algorithm called Tabu Search with Sampling and Perturbation to generate a feasible solution, and in stage two, we use a Great Deluge algorithm to improve the quality of this feasible solution. The proposed methodology was tested on a recent instance of real university timetabling data from Universiti Malaysia Sabah. Experiments were conducted to determine the most suitable GD algorithm parameter values that resulted in optimal performance. Additionally, the performance of the GD algorithm was compared with that of a Genetic Algorithm and was found to achieve a lower minimum and lower average cost values. © 2023 IEEE.

关键词： Tabu search

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Immersive Learning Constructs for Deaf and Hard-of-Hearing Students in TVET Practical Skill

Immersive Learning Constructs for Deaf and Hard-of-Hearing S...

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2024 International Visualization, informatics and Technology Conference, IVIT 2024

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina Universiti Malaysia Sabah User Experience Group Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Sunway University Computing and Information System School of Engineering and Technology Selangor Malaysia

ISBN: (纸本)9798350354447

The advent of advanced educational technologies, and immersive learning utilizing virtual reality (VR), augmented reality (AR), and mixed reality (MR) have emerged as a powerful tool to enhance learning experiences. However, traditional approaches often lack the ability to address the unique needs of Deaf and Hard of Hearing (DHH) students, particularly in practical skill development. The usage of immersive learning for the purpose of DHH students is still limited, especially in Malaysia. This study explores critical elements of immersive learning environments designed to facilitate practical skill acquisition for DHH students. Using the systematic literature review (SLR) determines immersive practical skill learning elements from selected 34 papers to help DHH students learn more effectively. 30 constructs are highlighted in the SLR results as recommendations for creating immersive learning for DHH practical skills. It is thus intended that the highlighted features would contribute to both the process of teaching and learning as well as the enhancement of student performance. © 2024 IEEE.

关键词： Contrastive Learning

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Optimized recurrent neural networks with attention for wind farm energy generation forecasting 16

Optimized recurrent neural networks with attention for wind ...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Zivkovic, Miodrag Jovanovic, Luka Pavlov, Marijana Bacanin, Nebojsa Dobrojevic, Milos Salb, Mohamed Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia Singidunum University Faculty of Technical Sciences Belgrade11000 Serbia

ISBN: (纸本)9798350347029

The shift towards renewable sources of power has brought several associated challenges. The sporadic nature of power generation, high reliance on weather, and difficulties with long-Term storage are just some of the challenges facing large-scale adoption. Storage is especially challenging due to the large associated upfront costs of batteries coupled with limited lifetimes further shortened by uneven charging. One possible step for improving the adoption of renewable sources is to develop robust techniques for forecasting power generation to better plan power demands. This work proposed an approach based on time series forecasting through the use of recurrent neural networks augmented with an attention mechanism. To further optimize the performance of this approach a modified version of a well-known metaheuristic algorithm is also introduced. The proposed approach is evaluated on a real-world dataset consisting of weather and wind farm power generation data from a wind farm located in mainland China. Several metaheuristics have been applied to optimize the proposed approach and the outcomes have been subjected to a comparative analysis to determine the best approach. © 2023 IEEE.

关键词： Recurrent neural networks

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Decomposition aided cloud load forecasting with optimized long-short term memory networks 16

Decomposition aided cloud load forecasting with optimized lo...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Bacanin, Nebojsa Jovanovic, Luka Zivkovic, Miodrag Salb, Mohamed Elsadai, Ali Sarac, Marko Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia Singidunum University Faculty of Technical Sciences Belgrade11000 Serbia

ISBN: (纸本)9798350347029

Cloud load forecasting plays an important role in information technology. There is the increasing reliance on online services for accurately predicting service demand in order to take timely actions and guarantee better services and greater customer satisfaction. However, the complexities associated with making accurate forecasts make this a challenging task. By formulating this task as a time series forecasting problem powerful artificial intelligence algorithms able to handle temporal data such as the long short-Term memory (LSTM) neural network may be used for this problem. Additionally, to help tackle the complex nature of the data, variational mode decomposition is incorporated into the approach. Better accuracy can be obtained by sleeting appropriate LSTM control parameters. Several well-known metaheuristics have been evaluated on their ability to optimize LSTM network parameters and improve performance when applied to a real-world cloud load forecasting dataset. The metaheuristic algorithms are subjected to comparative analysis to determine the best-performing approach. © 2023 IEEE.

关键词： Variational mode decomposition

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Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification

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Computers and Education: Artificial Intelligence 2025年 8卷

作者： Haque, Radiah Goh, Hui-Ngo Ting, Choo-Yee Quek, Albert Hasan, M.D. Rakibul Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

The application of Machine Learning (ML) for predicting graduate student employability is a growing area of research, driven by the need to align educational outcomes with job market requirements. In this context, this paper investigates the application of Large Language Models (LLMs) for tabular data transformation and embedding, specifically using Bidirectional Encoder Representations from Transformers (BERT), to enhance the performance of ML models in binary classification tasks for student employability prediction. The primary objective is to determine whether converting structured data into text format improves model accuracy. The study involves several ML models including Artificial Neural Networks (ANN), CatBoost, and BERT classifier. The focus is on predicting the employment status of graduate students based on demographic, academic, and graduate tracer study data, collected from over 4000 university graduates. Feature selection methods, including Boruta and Extra Tree Classifier (ETC) are employed to identify the optimal feature set, guided by a sliding window algorithm for automatic feature selection. The models are trained in four stages: 1) original dataset without feature selection or word embedding, 2) dataset with selected optimal features, 3) transformed data with word embedding, and 4) transformed data with feature selection applied both before and after word embedding. The baseline model (without feature selection and embedding) achieved the highest accuracy with the ANN model (79%). Subsequently, applying ETC for feature selection improved accuracy, with CatBoost achieving 83%. Further transformation with BERT-based embeddings raised the highest accuracy to 85% using the BERT classifier. Finally, the optimal accuracy of 88% was obtained by applying feature selection before and after embedding, with the BERT-Boruta model. The findings from this study demonstrate that using the dual-stage feature selection approach in combination with BERT embedding

关键词： Students

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A Comparative Study of Convex Combination and Inner Ordinate Methods For Scattered Data Interpolation Using Quartic Triangular Patch 32

A Comparative Study of Convex Combination and Inner Ordinate...

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32nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2024

作者： Tamin, Owen Karim, Samsul Ariffin Abdul Hasan, Mohammad Khatim Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Research Lab. Faculty of Computing and Informatics Jalan UMS Sabah Kota Kinabalu88400 Malaysia Centre for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor Bangi43600 Malaysia

In this study, we perform a comparative evaluation and assessment for the scattered data interpolation using a quartic polynomial triangular patch with ten control points on a triangular domain. The comparison is made using two different convex combinations and inner ordinates methods, i.e., cross derivative and cubic precision. Statistical Goodness-fit measurements used are maximum error, coefficient of determination (R2), CPU time (in seconds), and contour plot. From the result, the cubic precision method with linear convex combination methods gave better results with smaller CPU times and higher R2 value. All numerical and graphical results are presented using MATLAB programming. © 2024 University of West Bohemia. All rights reserved.

关键词： Interpolation

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