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HighTech and Innovation Journal 2023年第4期4卷 854-869页

作者： Abdul Razak, Siti Fatimah Fang, Ku Yee Kamis, Noor Hisham Muhammad Amin, Anang Hudaya Yogarayan, Sumendra Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia Computer and Information Science Department Higher Colleges of Technology Dubai Men's College United Arab Emirates

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. © 2023, Ital Publication. All rights reserved.

关键词： Denial-of-service attack

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IoT-Based Monitoring of Chili Plant Growth 6

IoT-Based Monitoring of Chili Plant Growth

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Mustaffa, Mas Rina Rizal, Norul Safinaz Norul Department of Multimedia Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Darul Ehsan 43400 Malaysia

ISBN: (纸本)9798331539603

The Internet of Things (IoT) has the potential to revolutionize agriculture by enabling precise monitoring and control of environmental conditions. However, in certain areas, the application of IoT in agriculture is still limited, and beginner growers often lack the necessary knowledge to optimize chili plant growth. This project aims to address these issues by developing an IoT-based smart monitoring system for chili plants. The objectives are to assist users in real-time monitoring of plant growth and provide guidance for optimal cultivation practices. The methodology involves the integration of sensors (temperature, humidity, and soil moisture) connected to an Arduino (NodeMCU esp8266) to collect data, which is then transmitted to ThingSpeak and Firebase databases for real-time monitoring via a mobile application. User testing was conducted to evaluate the system's effectiveness. Results indicated that the system significantly improved users' ability to monitor and manage chili plant growth. The educational content within the application was found to be highly beneficial, and the interactive game feature enhanced user engagement. In conclusion, the IoT-based system successfully assists growers in optimizing chili plant cultivation and has the potential to be expanded to other crops, contributing to smarter agricultural practices. © 2024 IEEE.

关键词： Smart agriculture

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technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian);The analysis for Malaysian farmers 5

Technology acceptance model (TAM) for mobile durian plantati...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Kamarudin, Noraziahtulhidayu Yusoff, Aiman Basari, Abd Samad Hasan Safar, Noor Zuraidin Bin Mohd Hussin, Mohd Hatta Universiti Tun Hussein Onn Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Technology Department of Software Engineering Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Information Security and Web Technology Malaysia Pertubuhan Peladang Senggarang Johor Darul Takzim Malaysia

ISBN: (纸本)9798350337525

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where;a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers. © 2023 IEEE.

关键词： Image recognition

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Integrating Business Intelligence and Recommendation Marketplace System for Hawker Using Content Based Filtering 4

Integrating Business Intelligence and Recommendation Marketp...

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Mishan, Mohd Taufik Amir, Aimi Liyana Bin Mohd Supir, Mohd Hafifi Kushan, Albin Lemuel Zulkifli, Norlindamalia Rahmat, Mohammad Hafidz Department of Computer Sciences Melaka Malaysia Melaka International College of Science and Technology Faculty of Information Technology and Multimedia Melaka Malaysia Department of Computer Technology Networking Melaka Malaysia

ISBN: (纸本)9798350318432

The hawker industry is known for its diverse food options and affordable prices, but customers often struggle to select their preferred hawker stalls due to the abundance of choices. To address this challenge, this research proposes the integration of business intelligence techniques with a recommendation marketplace system using content-based filtering. This research aims to enhance the hawker dining experience by providing personalised recommendations based on customer preferences and leveraging data-driven insights. The objectives of this study are to collect and integrate data from various sources, including hawker stall information, customer preferences, and feedback. This research will employ content-based filtering techniques to analyse this data and develop a recommendation engine that suggests hawker stalls and food options aligned with users' preferences and past interactions. Through this integration, this project seeks to derive valuable insights from the collected data, such as identifying popular food categories, analysing customer behaviour, and understanding customer satisfaction levels. These insights will enable us to optimise the recommendation marketplace system, improve operational efficiency, and enhance the overall customer experience. This paper presents a mobile-based recommendation system that allows customers to explore personalised hawker stall recommendations based on their preferences. By integrating business intelligence with a recommendation marketplace system for hawker food, this research contributes to improving the decision-making process for customers, promoting hawker stall visibility and revenue, and creating a personalised and enjoyable dining experience. © 2023 IEEE.

关键词： Recommender systems

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Low-error Reconstruction of Directional Functions with Spherical Harmonics

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IEEE Transactions on Visualization and computer graphics 2025年 PP卷 PP页

作者： Vlnas, Michal Milet, Tomas Zemcik, Pavel Brno University of Technology Department of Computer Graphics and Multimedia Faculty of Information Technology Czech Republic Lappeenranta-Lahti University of Technology School of Engineering Science Lappeenranta53850 Finland

This paper proposes a novel approach for the low-error reconstruction of directional functions with spherical harmonics. We introduce a modified version of Spherical Gaussians with adaptive narrowness and amplitude to represent the input data in an intermediate form. This representation is then projected into spherical harmonics using a closed-form analytical solution. Because of the spectral properties of the proposed representation, the amount of ringing artifacts is reduced, and the overall precision of the reconstructed function is improved. The proposed method is more precise comparing to existing methods. The presented solution can be used in several graphical applications, as discussed in this paper. For example, the method is suitable for sparse models such as indirect illumination or reflectance functions. © 1995-2012 IEEE.

关键词： Visualization

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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A Machine Learning Approach to Predict Autism Spectrum Disorder 2

A Machine Learning Approach to Predict Autism Spectrum Disor...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Baqer, Fatema Subramanian, Suresh Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia College of Information Technology Multimedia Science Department Ahlia University Manama Bahrain

ISBN: (纸本)9798350355468

Autism spectrum disorder, often shortened to ASD, is a developmental disability caused by differences in the brain. Individuals with ASD display deficits in social communication and interaction as well as repetitive behaviors or interests among a list of other symptoms [1]. If left untreated, individuals with autism may observe no progression or a worsening of their symptoms as untreated ASD can increase the risk of the development of depression and anxiety. The best outcomes are expected when the individual is diagnosed as early as possible allowing for early intervention and the provision of therapy and treatments. ASD can be detected in individuals as young as toddlers. Much research has been conducted into the application of machine learning in order to predict ASD in many different age groups, with the category of toddlers having the least dedicated research. This paper aims to explore the application of different machine learning techniques in order to predict ASD in toddlers. Two different toddler datasets were used to train and test the models. Many different traditional machine learning models, ensemble models, and deep learning models were considered in the scope of this research and compared on the basis of performance. K-fold cross validation was applied, and the models were tested. The most positive results in terms of the highest accuracy recorded were seen in ensemble learning which near consistent positive results being seen for all the different experimented techniques. Further favorable results were seen in particular with random forest (100%), decision trees (100%), C4.5 (100%) and logistic regression (100%). In terms of deep learning the most positive results were seen in multilayer perceptron (100%), recurrent neural networks (98.6%), and multilayer kernel machines (100%). © 2025 IEEE.

关键词： ASD Autism Autism Spectrum Disorder Deep Learning Ensemble Learning Feature Selection Machine Learning

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Handling Imbalanced Data for Improved Classification Performance: Methods and Challenges 3

Handling Imbalanced Data for Improved Classification Perform...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Abokadr, Somiya Azman, Azreen Hamdan, Hazlina Amelina, Nurul University Putra Malaysia Faculty of Computer Science & Information Technology Department of Intelligent Systems Seri Kembangan43400 Malaysia Al Zintan University Faculty of Science Libya University Putra Malaysia Faculty of Computer Science & Information Technology Seri Kembangan43400 Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Multimedia Selangor Darul Ehsan Serdang Malaysia

ISBN: (纸本)9798350305333

Imbalanced data significantly impacts the efficacy of machine learning models. In cases where one class greatly outweighs the other in terms of sample count, models might develop a bias towards the majority class, thereby undermining the performance of the minority class. Imbalanced data act to increase the risk of overfitting, as the model may memorize the majority of class samples instead of learning underlying patterns. This paper addresses these challenges in the classification field by exploring various solutions, including under-sampling, oversampling, SMOTE, cost-sensitive learning, and ensemble deep learning methods. The evaluate the performance of these methods on different datasets and provide insights into their strengths and limitations. The paper presents a taxonomy of strategies for imbalanced binary and multi-class classification problems, including resampling, algorithmic, and hybrid methods. Ultimately, the paper furnishes guidelines to facilitate the selection of the most pertinent method for mitigating imbalanced data challenges within a specific classification context. © 2023 IEEE.

关键词： Classification (of information)

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