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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Sitti Wan Fazirah Ambo'Miri Lai Po Hung Florence Sia Fui Sze Tan Soo Fun Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Proper diagnosis of the types of wounds is the first step in effective wound management. Healthcare quality can be improved with the use of artificial intelligence technology. In this project, convolutional neural network (CNN) models are employed for superficial wound image classification. The goal of this project is to implement carefully selected CNN models with fine-tuned parameters (batch sizes, augmentation parameters and epoch size) to categorize superficial wounds of abrasion, bruise and superficial skin burns. The library and functions from TensorFlow, Keras, MatplotLib and OpenCv will be used alongside the Python programming language to create this project in a Google Colaboratory notebook, also widely known as Colab. This work evaluates the CNN models of VGG-16, InceptionV3 and ResNet-50 to find out which best classifies the dataset of non-fatal superficial wounds. These models have significant differences especially in their number of layers and concepts of capturing the features of images input. By utilizing these models, the accuracy rate of more than 40% is observed after models being trained and validated on 90% of the dataset (80% for training and 20 for validation), then tested and prediction with the remaining 10% of the dataset for classification report and confusion matrix generation. Out of 3 CNN models used in this study, it is observed that InceptionV3 model's highest accuracy is 44% while both ResNet50 and VGG-16 model achieved 45% accuracy against the superficial wound database (non-fatal wound).

关键词： Training Accuracy Medical services Wounds Predictive models Internet Convolutional neural networks Artificial intelligence Image classification Biomedical imaging

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Developing an Effective ICT Strategic Framework for Higher Education Institutions: A Case of Mataram University 17th

Developing an Effective ICT Strategic Framework for Higher E...

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17th International Conference on Knowledge Management in Organisations, KMO 2023

作者： Hussein, Surya Sumarni Alfiansyah, Muhamad Wisnu Daud, Rohaizan Ya’acob, Suraya Lokman, Anitawati Mohd College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Jabatan Perkhidmatan Awam Malaysia Kuala Lumpur Malaysia Advanced Informatics Department Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9783031340444

This study proposes an Information and Communication Technology (ICT) Strategic Plan (ISP) framework aimed at providing organizations with a practical guide to implementing successful ICT strategies. The proposed framework is based on the identified as-is situation and gap analysis using Enterprise Architecture (EA) and Knowledge Management (KM) approaches. The most significant limitation of most developed ISP frameworks is the inadequate requirement gathering and analysis, leading to a lack of a comprehensive understanding of the organization’s true goals. To address this, the proposed framework integrates EA and KM practices, enabling organizations to develop models and diagrams to visualize organizational objects and capture, transfer, and create new ICT strategies. The study’s contribution lies in providing organizations with a valuable resource for developing dynamic and meaningful ICT strategies, culminating in the translation of the resulting ISP into an Enterprise Architecture diagram, called a Landscape Map Viewpoint (LMV), as a communication medium for decision-making purposes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Integrating Serious Game in Personalized Learning Environment

Integrating Serious Game in Personalized Learning Environmen...

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computing (ICOCO), IEEE International Conference on

作者： Vivian Gek-Ting Wee Fang-Fang Chua Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Computer science skills have become very important in the careers of STEM fields in today’s digital economy. However, many students often struggle in grasping programming concepts, leading to high dropout rates in computer science courses. The students have also found traditional learning environment to be ineffective in engaging and supporting them to tackle the issue. This paper recognizes the need for a pedagogical shift towards personalized learning experience to address the shortcomings of traditional teaching approaches. It aims to develop a personalized learning system named Codecaster Odyssey that caters to the diverse needs of learners. It also aims to leverage the intrinsic appeal of gaming to enhance student engagement and foster sustained interest in learning programming by integrating serious game into the learning system.

关键词： Learning systems Education Turning Encoding Real-time systems Serious games Reliability Mentoring Programming profession STEM

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Developing Deep Learning Models to Predict Benign and Malicious Attacks in IoT Networks

Developing Deep Learning Models to Predict Benign and Malici...

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computing (ICOCO), IEEE International Conference on

作者： Nik Muhammad Danial Bin Nik Ram Zaedi Amy Lim Hui Lan Goh Hui-Ngo Faculty of Computing & Informatics Multimedia University Malaysia Cyberjaya Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

The Internet of Things (IoT) has afforded increased connectivity across various domains which increased cybersecurity vulnerabilities. Threats like Distributed Denial of Service (DDoS) attacks, Spoofing, and web-based attacks pose a significant danger to IoT networks, which require more advanced Network Intrusion Detection Systems (NIDS) to effectively counter these threats. The aim of this paper is to investigate the viability of DL-based classifiers for NIDS. The CICIoT2023 dataset is used in this study. Feature reduction is performed using the Pearson’s Correlation Coefficient (PCC) Matrix and features that contain all-zero values are removed. To resolve the class imbalanced issue, Synthetic Minority Oversampling (SMOTE) is applied exclusively on the training set only. For comprehensive comparison studies, a total of nine classifiers have been considered which can be categorized into machine learning (ML)-based classifiers and Deep Learning (DL)-based classifiers. MLbased classifiers consist of Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN) and Logistic Regression (LR), and while DL-based classifiers consist of Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (BiLSTM), 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN). The findings revealed that the DL-based classifiers have consistently recorded high accuracy, precision, recall, F1 score and AUC of more than 0.9. Although ML-based classifiers have recorded high precision and AUC, the accuracy, recall and F1 score have a wider variation in scores. The consistency and high scores across various evaluation metrics indicate the viability of adopting DL-based classifiers for building NIDS.

关键词： Training Deep learning Measurement Accuracy Computational modeling Buildings Predictive models Nearest neighbor methods Internet of Things Testing

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Embodied Narrative: Data Storytelling of Online Artwork Experiences 8th

Embodied Narrative: Data Storytelling of Online Artwork Expe...

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8th International Visual informatics Conference, IVIC 2023

作者： Baharin, Hanif Harun, Afdallyna Fathiyah Mohd Norowi, Noris Institute of Visual Informatics Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Shah Alam40450 Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789819973385

This paper describes a pilot ethnomethodology study of visiting a 3D virtual gallery that exhibits 3D models of physical artworks and of viewing photographs of artworks in a 2D online artist portfolio. Artwork, with its intrinsic information and layers of meaning, is a form of data which narrates a compelling and evocative story to engage audience. The digital landscape is a compelling medium to deliver this storytelling. However, such approach demands a delicate balance between technology incorporation and artistic vision. In an era of converging data and art, this paper explores the aesthetics of data storytelling and immersive online art experiences. Amid CoVID-19 lockdowns, physical art exhibitions migrated online, prompting a study of 3D virtual gallery visits and 2D online artist portfolios. Participants engaged in think-aloud protocols while navigating both formats, revealing insights into rationalizing actions and interpreting artworks online. Challenges emerged, including disrupted responses due to the inability to discern texture in 3D models or artwork photos. Navigational control, viewing distance, detailed artwork descriptions, and stable internet connections further influenced the online viewing experience. This prompts a further exploration on means to curate online exhibitions thoughtfully, where data aesthetics and artistic storytelling is harmonized for realistic interpretation within the digital expanse. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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Movies and Learning: Does Watching Movies Help Students Improve Learn English? 1

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Baharum, Aslina Naim, Nur Faraha Mohd Ismail, Rozita Deris, Farhana Diana Noor, Noorsidi Aizuddin Mat Jasin Campus Melaka Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Bahru Malaysia UTM CRES Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (数字)9789811984068

ISBN: (纸本)9789811984051

Since English is the most widely used language for communication in the world, mastering English has become essential and important in our lives. Many methods are used to improve the English level, including watching movies. This research aims to study whether watching movies helps students improve their English. This study uses a quantitative method to collect the data through an online questionnaire using Google Forms. The online questionnaire consists of 15 questions with the participation of 250 students from one of the universities in Malaysia, of which each faculty has 25 students for random clustering. The results show that most students usually watch action English movies very often. From the findings, there is no result showing that there is a significant relationship between movie genre preference and English learning. However, it can be concluded that watching movies does help students improve learn English. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Application for Selecting Legislative Candidates with Social Media Data that Influences Election Decisions 12

Application for Selecting Legislative Candidates with Social...

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12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Bawono, Marastika Wicaksono Aji Utomo, Stevani Dwi Kristianto, Andika Rozy, Agus Fachrur Utomo, Rio Guntur Nuha, Hilal Hudan School of Computing Telkom University Bandung Indonesia Universitas Kristen Duta Wacana Faculty of Informatics Central Java Indonesia University Airlangga Faculty Science Jakarta Indonesia University Brawijaya Dapertement Statistic Malang Indonesia

ISBN: (纸本)9798350363432

Numerous studies have explored legislative election candidates, yet there remains a significant gap in understanding the impact of social media analysis on election choices. This research investigates the analysis of campaign news disseminated through social media platforms in shaping voter polling trends. A significant challenge encountered in the digital world is the prevalence of fake accounts and bots that can increase the number of followers and distinguish between fake and genuine comments, thereby enhancing the reputation of legislative candidates. Therefore, this requires the implementation of social media analysis reports combined with survey questionnaire results that directly reflect the opinions of supporting respondents. The data obtained from this survey is then visualized in the report. The methodological approach uses analysis from social media news combined with data processing using the pls sem model from questions given to respondents. The primary goal of this research is to provide valuable insights for legislative candidate nominations, particularly in understanding campaign trends and accurately measuring the extent of real-time support on social media platforms. © 2024 IEEE.

关键词： Report for selecting legislative candidates SEM PLS Systematic literature review

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Aspect-Based Subjectivity Analysis Using a BERT-based Approach

Aspect-Based Subjectivity Analysis Using a BERT-based Approa...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Ng, Hu Chong, Wing Kin Chia, Yu Zhang Vun Yap, Timothy Tzen Goh, Vik Tor Wong, Lai Kuan Teck Tan, Ian Kim Cher, Dong Theng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia School of Mathematical & Computer Sciences Heriot-Watt Univeristy Putrajaya Malaysia SIRIM Berhad Shah Alam Malaysia

ISBN: (纸本)9798350353464

Aspect-based subjectivity analysis stands as an important task in natural language processing, seeking to identify the subjectivity of various aspects or features within a text. A new method for aspect-based subjectivity analysis using BERT is introduced in this paper. BERT has demonstrated impressive performance across various NLP tasks, and its capabilities are utilized to accurately ascertain the subjectivity of specific aspects within a given text. The approach involves fine-tuning BERT on a sizable dataset annotated with aspect-level subjectivity labels, enabling the model to grasp the subtleties of aspect-based subjectivity analysis. Extensive experiments on benchmark datasets are conducted to showcase the effectiveness of this approach and compare it with existing methods. The results reveal that this proposed approach surpasses state-of-the-art techniques in aspect-based subjectivity analysis, underscoring the potential of leveraging BERT for such purposes. ©2024 IEEE.

关键词： Aspect-based NLP Subjectivity Analysis

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Supervised Machine Learning Techniques for Power Consumption Usage Level Prediction 6

Supervised Machine Learning Techniques for Power Consumption...

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6th International Conference on Software Engineering and Computer Science, CSECS 2023

作者： Tan, Yi-Fei Zhao, Ge-Zhi Cheeng, Tze-Hang Ooi, Chee-Pun Tan, Wooi-Haw Cheong, Soon-Nyean Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Technology Faculty of of Engineering Chongqing Chongqing China Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Multimedia University Faculty of Creative Multimedia Cyberjaya Malaysia

ISBN: (纸本)9798350306378

Electricity is an essential part of modern life and is crucial for many aspects of society and the economy. It is essential for industries to operate and create products, for transportation systems to function, and for communication and information technology to function. As the world continues to become more connected and technologically advanced, the demand for electricity is likely to increase, making it an even more critical resource in the future. Overusing electricity will lead to higher energy bills, which can put a strain on household or business budgets. In this paper, the authors developed a model to predict the level of electricity consumption usage. The model is developed in 2 stages. First stage is to categorize the electricity usage into several levels by using equally divided method. Second stage is to use supervised machine learning techniques to develop a prediction model for the level of electricity consumption. The experimental results indicated that using the Random Forest classifier leads to better performance in terms of accuracy, precision, recall, F1 score, and kappa score. Besides that, the results show that 2-level prediction performed better than 4-level prediction. © 2023 IEEE.

关键词： Forecasting

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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Computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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