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

作者： Gebre, Hambisa Mitiku Wegari, Getachew Mamo Jimma University Faculty of Computing and Informatics Department of Information Science Jimma Ethiopia Jimma University Faculty of Computing and Informatics Department of Information Technology Jimma Ethiopia

ISBN: (纸本)9798331527792

Background:- Breast cancer is one of the most common and lethal diseases in the world. Traditional breast cancer diagnostic and prognosis procedures usually involve significant human talent and can be time-consuming and subjective, leading to potential errors and treatment delays. Two recent technical advancements, deep learning, and big data analytics are promising to improve breast cancer diagnosis and therapy. As a result, this systematic review aims to examine specific papers that describe deep learning techniques and big data analytics in the context of breast cancer diagnosis and ***: Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), peer-reviewed articles published in the English language from January 2020 to August 2023 were selected from electronic databases such as PubMed, ACM, Digital Library, and Science Direct, as well as citations and manual searches. This review paper takes into account papers about deep learning algorithms, big data analytics, the efficacy of deep learning and big data analytics, as well as the problems and limitations of merging deep learning methods with big data analytics. Articles that were not original or in English were not ***: - Ten articles were identified for this review. The finding showed that deep-learning techniques play a great role in analyzing vast datasets to identify malignant cells or tumors, aiding radiologists in accurate diagnoses and improving patient outcomes. Big data analytics in breast cancer diagnosis and treatment can improve accuracy, efficiency, and patient-centered care. Deep learning techniques are utilized with big data, enhancing screening test accuracy and guiding diagnostic procedures, especially for image-based scanning that leads to early breast cancer identification, improving patient outcomes, and potentially enhancing diagnostics. Obtaining huge volumes of high-quality data for training deep learning models is a challenge and limita

关键词： big data analytics breast cancer diagnosis and treatment Deep learning algorithms

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Movie Recommender System Based on Generative AI 1

Movie Recommender System Based on Generative AI

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1st International Symposium on Parallel computing and Distributed Systems, PCDS 2024

作者： Ling Tung, Carmen Pei Haw, Su-Cheng Kong, Wan-Er Naveen, Palanichamy Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350349658

Movie recommender systems have revolutionized how people discover and experience movies using sophisticated algorithms, such a system generates personalized recommendations to assist users in reaching the movies that precisely fit their tastes. The traditional movie recommender systems lacks of diversity, unable to capture complex user preferences, and suffers from the cold-start issues. In this paper, we utilize the generative AI, specifically with Variational Autoencoders (VAE), to solve these problems. By combining VAEs, the system aims to produce more rich and individualized recommendations. VAE mitigates these issues by enabling the generation of meaningful item representations, promoting diversity in recommendations, and capturing the complexity of user preferences in a continuous latent space. The implementation is structured as follows: first, review the generative AI in movie recommender systems;second, implement the chosen generative AI approach;and finally, conduct the performance metrics evaluation of the selected generative AI approach. Ultimately, this research will enable the system to offer an achievement of an interactive, changing, and more personalized movie-discovering experience through the amazing power of generative AI. © 2024 IEEE.

关键词： Generative adversarial networks

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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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2024 IEEE International Conference on computing, ICOCO 2024

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

ISBN: (纸本)9798331530303

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. ML-based 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. ©2024 IEEE.

关键词： Network intrusion

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Imbalanced Data Classification Using Oversampling and Automatic Feature Selection Methods for Undergraduate Student Career Prediction 13

Imbalanced Data Classification Using Oversampling and Automa...

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13th International Conference on Educational and Information Technology, ICEIT 2024

作者： Haque, Radiah Goh, Hui-Ngo Ting, Choo-Yee Quek, Albert Hasan, Md Rakibul Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350372663

The application of machine learning techniques for predicting the career trajectories of fresh undergraduate students has become a crucial strategy for evaluating their potential to secure employment post-graduation or pursue further education. However, for such applications, imbalanced data is a vital issue that needs to be addressed with proper methods. In this paper, the combination of oversampling, using Synthetic Minority Overs amp ling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and feature selection, using Recursive Feature Elimination (RFE) and the Boruta algorithm, is applied. The results show that the SMOTE-based Boruta approach is effective to improve the performance of machine learning classification models for undergraduate student career prediction. © 2024 IEEE.

关键词： Forecasting

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Intrusion Detection System for Cyber-attacks in the Internet of Vehicles (IoV) Environment 1

Intrusion Detection System for Cyber-attacks in the Internet...

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1st International Conference on Cyber Security and computing, CyberComp 2024

作者： Hanifa, Mueez Zubair, H.T. Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350387728

This report presents the development of an Intrusion Detection System (IDS) designed to detect cyber-attacks in an Internet of Vehicles (IoV) environment. The IDS employs machine learning techniques, specifically Support Vector Machines (SVM), and is implemented on two low-memory, low-speed microcontroller systems connected to a CAN bus. The model is trained on real-world data, sourced from a publicly available dataset collected from a Hyundai Avante CN7, using Python-based tools on a computer. The resulting coefficients and biases are extracted and embedded into a C program, which runs on the microcontrollers. The final implementation achieved an accuracy of 83%, with a precision of 97% and a recall of 68%. © 2024 IEEE.

关键词： Microcontrollers

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Data Leakage Detection in Cloud computing Environment 1

Data Leakage Detection in Cloud Computing Environment

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1st International Conference on Cyber Security and computing, CyberComp 2024

作者： Bin Suhaimi, Hurin Haziq Zubair, H.T. Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350387728

Insider threats pose critical security threats through the possession of privileged and access to valuable information and resources. Data leakage is one of the significant challenges faced in cloud computing environments, emphasising the criticality of data protection across various sectors such as education, finance, healthcare, and more. Cloud computing offers numerous advantages, including reliable data storage and transfer, yet it also presents significant risks, including data breaches, server failures, and malicious insider threats. This paper's primary objective is to evaluate the effectiveness of data leakage detection methods, particularly focusing on insider threats. Through the exploration of machine learning models and anomaly detection techniques, the study seeks to identify the most effective strategies for mitigating unauthorised data exposure. The research encom-passes a comprehensive analysis of current security measures, identification of vulnerabilities, and the proposition of enhanced detection methods. By examining real-world data leakage incidents and employing advanced detection tools, the findings aim to contribute to the development of more robust data protection frameworks. This research is poised to offer valuable insights and recommendations that will aid organisations in leveraging cloud services securely and efficiently, thereby enhancing their overall data security posture. The study not only underscores the importance of the CIA triad-Confidentiality, Integrity, and Availability-but also highlights the significance of innovative detection methods such as machine learning algorithms and behavioural analytics in safeguarding sensitive information in the ever-evolving landscape of cloud computing. © 2024 IEEE.

关键词： Information leakage

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Superficial Wound Classification Using CNN Image Classifier for Initial Wound Care 6

Superficial Wound Classification Using CNN Image Classifier ...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

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

ISBN: (纸本)9798350389692

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). © 2024 IEEE.

关键词： Convolutional neural networks

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Interactive Data Visualization to Optimize Decision-Making Process 1

Interactive Data Visualization to Optimize Decision-Making P...

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1st International Symposium on Parallel computing and Distributed Systems, PCDS 2024

作者： Phang, Zheng-Bin Haw, Su-Cheng Tai, Tong-Ern Ng, Kok-Why Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350349658

The increasing volume and complexity of data in today's business environment present significant challenges for traditional data analysis methods. Static reports and spreadsheets often fall short in providing comprehensive insights necessary for strategic decision-making. This paper addresses the critical issue of enhancing data comprehension and utilization through interactive data visualization. The primary objectives of this paper are to explore various visualization techniques, design and implement an interactive dashboard with a content-based recommender system, and evaluate its effectiveness in optimizing business processes. The content-based recommender system utilizes TF-IDF vectorization and cosine similarity to offer personalized product recommendations. The process includes data cleaning, preprocessing, and integrating the recommender system into the dashboard. Comprehensive evaluation through user testing and performance analysis ensures the vizualisations and recommender system's accuracy, responsiveness, and effectiveness. In addition, by leveraging descriptive analytics, the paper aims to create a user-friendly tool to explore data, uncover hidden patterns, and facilitate decision-making. The expected outcome of this paper is an interactive dashboard that significantly enhances data comprehension and strategic decision-making for businesses. © 2024 IEEE.

关键词： chart dashboard data visualization decision-making interactive recommender system

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Integrating Serious Game in Personalized Learning Environment

Integrating Serious Game in Personalized Learning Environmen...

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2024 IEEE International Conference on computing, ICOCO 2024

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

ISBN: (纸本)9798331530303

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. ©2024 IEEE.

关键词： Contrastive Learning

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