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7th International Conference on Information Management and Management Science, IMMS 2024

作者： Salehi, Parsa Chua, Sook-Ling Foo, Lee Kien Faculty of Computing and Informatics Multimedia University Malaysia

ISBN: (纸本)9798400716997

There are many activity recognition systems based on supervised learning that have been proposed over the years. One problem with supervised learning is that it requires sufficient number of labelled data for training. The majority of the labelling tasks are done manually by the user themselves. This process is rather time-consuming and tedious. Although there are studies that attempt to use active learning-based methods to assist in the annotation process, these methods still require some amount of effort from the user and are impractical, especially when implementing a home for the elderly. In this paper, we propose an automated labelling approach using a transformer-based deep learning model to label the daily activities. Our method leverages on spatio-temporal information for class annotation. We evaluated our method on publicly available datasets. © 2024 Owner/Author.

关键词： Self-supervised learning

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Analyzing Cybersecurity Risk with a Phishing Simulation Website 24

Analyzing Cybersecurity Risk with a Phishing Simulation Webs...

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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Ali, Alya Nasuha Binti Mohamed Rahman, Siti Husna Abdul Faculty of Computing & Informatics Multimedia University Selangor Malaysia

ISBN: (纸本)9798400717161

Phishing is a cyberattack that exploits human psychology and technical vulnerabilities. Previous research has revealed inconsistency of relation between age and phishing susceptibility. Hence, this project aims to identify which age group is more susceptible to phishing. Therefore, to achieve these objectives, this project developed and deployed a phishing simulation website to analyse which age group is more vulnerable. Respondents are considered successfully phished if they submitted information to the phishing website. The project adopted previous researcher's phishing process with addition of two phases as a guideline. The phases were Planning, Preparation, Attack, Valuable Acquisition, Knowledge Transfer and Result Analysis. A blog was developed to redirect phishing victims to an e-Learning page to raise their cybersecurity awareness. Persuasion techniques used in this simulation were Reward and Urgency. The phishing website gained 146 clicks and 89 form submissions. Adults aged 18 to 59 years old (group A) has a higher phishing success rate of compared to adults aged 60 years and above (group B). These findings suggest younger adults are more susceptible to phishing. Future research should involve larger sample size and seek broader input from participants. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Phishing

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Transitioning to Online Learning for Continuing Undergraduate Students 24

Transitioning to Online Learning for Continuing Undergraduat...

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7th International Conference on Information Management and Management Science, IMMS 2024

作者： Foo, Lee Kien Chua, Sook-Ling Lim, Siew Ling Faculty of Computing and Informatics Multimedia University Malaysia

ISBN: (纸本)9798400716997

The COVID-19 pandemic has forced the world into lockdown. The usual face-to-face course delivery is not a feasible option for higher education institutions worldwide. Most of the education activities were switched to full online mode during this period. Current research focusses on various aspects of the transition from face-to-face to online learning and the challenges faced by students doing online learning for the first time. There is limited study that examines learning experience on continuing students, i.e., those with prior experience with online learning. The aim of this study is to examine students' experiences and the role of educational technologies in the transition to online learning for continuing undergraduate students. We conducted a survey on 259 students pursuing a computer science degree. The targeted participants are those who are not doing full online learning mode for the first time. The results revealed that many students still feel the challenge in learning via full online mode. Their comfort level in using online learning tools, the internet speed, sharing of devices and disturbance during online classes, are the factors contributing to the challenges when doing online learning. Findings from this study provides insights on the role of educational technologies in supporting online learning. © 2024 Owner/Author.

关键词： Federated learning

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Applied Natural Language Processing: User Review Sentiment Classification Optimized by Modified Metaheuristics 5

Applied Natural Language Processing: User Review Sentiment C...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bacanin, Nebojsa Antonijevic, Milos Perisic, Jasmina Zivkovic, Miodrag Jovanovic, Luka Adamovic, Sasa Singidunum University Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798331518097

E-commerce has become increasingly popular in recent years with increasingly many retailers even conducting business fully online. With the anonymity of the internet, an inability to inspect goods directly, the experiences of other users and seller reputation plays an increasingly important role in purchasing decisions. However, the sentiment expressed in reviews may vary and can be difficult to interpret without manual inspection. This work explores the potential of coupling text processing techniques with machine learning classifiers to handle automatic review sentiment classification. Since ML algorithms are not directly well suited to working with textual data, term frequency inverse document frequency is used to encode data in to a more appropriate format. The AdaBoost classifier is used to determine sentiment expressed in reviews, however, as the AdaBoost classifier is sensitive to parameter selection a modified metaheuristic algorithm is introduced to handle tuning. A comparative analysis of selected relevant optimizers reveals that the proposed optimizer achieves superior results, reaching an objective function score with an overall accuracy of 86.35% for the most effective classification model. © 2024 IEEE.

关键词： Sales

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Stunting Classification Among Toddlers Using Decision Tree C4.5 in Berastagi City

Stunting Classification Among Toddlers Using Decision Tree C...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Saveria, Hygea Gunawan, Putu Harry Informatics School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges, affecting both physical growth and cognitive development. This study focuses on developing a predictive model using the C4.5 Decision Tree algorithm to classify the stunting status of children under five. The dataset comprises nutritional status data of toddlers collected from the Berastagi Health Center. Incorporating attributes such as gender, birth weight and height, age in months, weight, height, and the height-to-age ratio. The model aims to enhance the accuracy of stunting classification to support targeted prevention and treatment strategies. When compared to Naive Bayes, the Decision Tree C4.5 demonstrated superior performance, achieving an F1 score and average accuracy of 86%. This reflects a robust balance between precision and recall, underscoring its reliability in identifying children at risk of stunting. The research highlights the critical role of data-driven methodologies in public health. With its high accuracy, the model serves as a promising tool for healthcare providers, enabling more effective interventions to reduce stunting prevalence and improve nutritional outcomes among children under five years of age. © 2025 IEEE.

关键词： Health risks

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MH-HMR:Human mesh recovery from monocular images via multi-hypothesis learning

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1263-1274页

作者： Haibiao Xuan Jinsong Zhang Yu-Kun Lai Kun Li College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Informatics Cardiff UniversityCardiffUK

Recovering 3D human meshes from monocular images is an inherently ill-posed and challenging task due to depth ambiguity,joint occlusion,and ***,most existing approaches do not model such uncertainties,typically yielding a single reconstruction for one *** contrast,the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions *** address these issues,the authors propose a multi-hypothesis approach,multi-hypothesis human mesh recovery(MH-HMR),to efficiently model the multi-hypothesis representation and build strong relationships among the hypothetical ***,the task is decomposed into three stages:(1)generating a reasonable set of initial recovery results(i.e.,multiple hypotheses)given a single colour image;(2)modelling intra-hypothesis refinement to enhance every single-hypothesis feature;and(3)establishing inter-hypothesis communication and regressing the final human ***,the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated *** with state-of-the-art methods,the MH-HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets,such as Human3.6M and 3DPW,while demonstrating the effectiveness across a variety of *** code will be publicly available at https://***/faculty/likun/projects/MH-HMR.

关键词： 3-D computer vision human reconstraction

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An Integrated Web-Based Approach for Email Analysis Investigation Tool 24

An Integrated Web-Based Approach for Email Analysis Investig...

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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Bari, Fawzul Rahman, Siti Husna Abdul Faculty of Computing & Informatics Multimedia University Selangor Malaysia

ISBN: (纸本)9798400717161

Phishing websites have often been used for online and email fraud, spreading unwanted content. It has been important to find them before any user data was entered. In recent years, numerous attempts have been made to identify these phishing websites. To identify phishing web pages, most current methods trained classification models using manually constructed lexical and statistical features from the textual content of websites. The time-consuming method of extracting hand-crafted features, which required specialized domain knowledge to determine which features were useful for a given platform, was one of the limitations of these phishing detection approaches. To address these issues, this project aimed to develop a website which is an Integrated Web-based Approach for Email Analysis Investigation Tool (IWAEAIT) that used an external API like Virus total to analyses phishing websites and ip-api to get the location of the email. The project's goal was to ensure that anyone could use this website to identify if an email was phishing or not, even if they had no prior IT experience. This project also included features like file attachment analysis and a feature that let users take a screenshot of the website without really visiting it to stop users from visiting phishing websites, URL scan analysis and email artefacts extractor to get the location of the email. Copyright © 2024 held by the owner/author(s).

关键词： Phishing

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Real-Time Recycle Waste Classification Using Deep Learning: A Convolutional Neural Network Model for Automated Waste Management

Real-Time Recycle Waste Classification Using Deep Learning: ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Nasir, Akmal Md Roslan, Rosniza School of Computing College of Computing Informatics and Mathematics Universiti Teknologi MARA Melaka Malaysia

ISBN: (纸本)9798331530303

The advancement of segregation of recycle waste classification applications has been increased by the development of innovative solutions which utilise modern technologies for effective waste management and recycling practices. However, there have problems when most of the country are lack of garbage collection and mechanism facilities, traditional waste management is inefficient and costly, and most people are not aware of how to manage recycle waste correctly. Therefore, this paper presents and studies a smart recycle waste classification of Convolutional Neural Network (CNN) to automate the real-time recyclable waste classification for three types of waste such as metal, paper, plastic. Four phases that applied in this study which are data acquisition, pre-processing, processing (i.e CNN architecture model), and post-processing (i.e test and evaluation). A total of 3000 images have been experimented in this study. All image datasets have been captured in Malacca and collected from the Garbage Classification and Waste Segregation Image Dataset. The CNN architecture model has been applied in this study. To evaluate the quantitative predictive performance of the proposed model, confusion matrix, accuracy, precision, recall, and F1-score are computed with random-splitting and manual-splitting of train-test image dataset with the ratio of 70: 30, 80: 20, and 90: 10. The results of this study showed the highest rate of 97.30%: 93.67%, 97.30%: 93.67%, 97.36%: 96.24%, and 97.26%: 96.18% for accuracy, precision, recall, and F1-score, respectively. Thus, it for is hoped to streamline waste sorting procedures and enable effective recycling techniques to deploy the smart recycle waste classification system, and significantly influence waste management initiatives, fostering sustainability and environmental protection. For the future work, this study may extend to experiment the other various type of recycle waste and experimented for various machine learning algorithm. ©2024

关键词： Convolutional neural networks

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Locational Analysis of Refuelling Stations using p-Median 20

Locational Analysis of Refuelling Stations using p-Median

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Baharom, Nuridawati Shariff, S.Sarifah Radiah Nasir, Noryanti Perlis Branch School Of Computing And Mathematics College Of Computing Informatics And Mathematics Perlis Arau Malaysia Selangor Shah Alam Malaysia School Of Computing And Mathematics College Of Computing Informatics And Mathematics Selangor Shah Alam Malaysia

ISBN: (纸本)9798350382310

This study aims to assess the performance of the existing network of location decisions for refuelling stations in Peninsular Malaysia. Firstly, a questionnaire survey examining users' perceptions and awareness of alternative fuel vehicles (AFV) and the experience of AFV users. The locational analysis was applied using p-median to evaluate the existing locations of refuelling stations regarding the population demand and distance from the population area to refuelling stations. Two new locations of refuelling stations were suggested based on the analysis of the results to accommodate the demand of users. © 2024 IEEE.

关键词： Location

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Addressing the Limitations of News Recommendation Systems: Incorporating User Demographics for Enhanced Personalization

Addressing the Limitations of News Recommendation Systems: I...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Asefa, Zerihun Olana Ergosa, Alemisa Endebu Jimma University Faculty of Computing and Informatics Jimma Ethiopia

ISBN: (纸本)9798331527792

News recommendation schemes use features of the news and user information to suggest relevant news items based on their interests. However, existing schemes are not very effective for new users who have little activity history. To address this issue, we have developed a hybrid news recommender system that combines content-based and collaborative filtering approaches, along with user demographic data. We conducted an extensive experiment using a dataset of news articles, user ratings, and demographic data to evaluate the system's effectiveness. We assessed the system's performance through two experiments. The first experiment focused on individual user similarity and achieved a precision of 68.05%, a recall of 42.46%, and an average F1 score of 52.1%. The second experiment examined user similarity within the same category, resulting in a precision of 93.75%, a recall of 40.25%, and an F1 score of 56.31%. These results indicate that the proposed model performs better in the second experiment compared to the first. © 2024 IEEE.

关键词： Cold start Problem Demographic information Hybrid Approach New Users News Recommendation System

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