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Organising unstructured data using Double Net Self-Organising Map (DNSOM) model

Soft computing 2025年第7期29卷 3533-3554页

作者： You, Cheng Chun Lim, Seng Poh Lee, Chen Kang Tan, Joi San Lim, Seng Chee Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan University Bandar Barat Perak Kampar31900 Malaysia Department of Computing Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sungai Long Bandar Sungai Long Cheras Selangor Kajang43000 Malaysia School of Computing and Informatics ViTrox College 746 Persiaran Cassia Selatan 3 Batu Kawan Industrial Park Penang Bandar Cassia14110 Malaysia

Surface reconstruction is the process of representing surfaces using the data obtained from scanning devices. When the data obtained is unstructured during data collection, it can cause problems in presenting the surface due to the lack of connectivity information for the data. It shows that data collection is a crucial task in surface reconstruction. Previous works have proved that Self-Organising Map (SOM) models can be used to organise unstructured data. However, the 2-D SOM model will generate a surface with holes for the closed surfaces. Besides, the 3-D SOM model will generate an incorrect surface due to the connectivity of the internal neurons. In addition, the Cube Kohonen SOM (CKSOM) model is limited to the same grid size. Hence, a SOM model known as Double Net SOM (DNSOM) is proposed via the merging of two 2-D SOMs to overcome the issues. Three data sets (cube, sphere, and talus bone) with different grid sizes are applied to test the models. When the grid size of all the models increases, the surface becomes smoother. The DNSOM contains a lower quantisation error than the 2-D SOM and the lowest topographic error among the other SOM models. It performs faster than the 3-D SOM and CKSOM. It presents the correct closed surface with a smaller number of neurons and different grid sizes. Microsoft Visual Studio 2022 with the C++ programming language is used to develop the models, while GNUPlot is used to visualise the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Visualization

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The Internet of Things in the Rearing of Giant Freshwater Prawn: A Pilot Study 6

The Internet of Things in the Rearing of Giant Freshwater Pr...

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Aziz, Musyaffa' Suhaili, Wida Haji Kumar Patchmuthu, Ravi Shams, Shahriar Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Brunei Faculty of Engineering Bandar Seri Begawan Brunei

ISBN: (纸本)9798350317428

The Internet of Things is an advanced technology that connects devices worldwide, making them smart, aware of their surroundings, and able to offer sophisticated services and applications to improve daily activities. This paper reviews the possible sensors and automation needed for giant freshwater prawn rearing and issues that can arise. The Internet of Things encompasses the hardware, software, and communication infrastructure that enables real-time monitoring and automation in the rearing of giant freshwater prawn. The features of the system include measuring water quality parameters using sensors, notifying users when the water quality parameter falls below or rises above the optimum value, a historical data set of the water parameters, and manual and conditional actuators. A pilot study was conducted to test the capabilities of the Internet of Things on giant freshwater prawn post-larvae. The results of this study suggest that the sensors' readings are 97.6% similar to the manual tests. Despite the noticeable limitations, this study suggests that the potential of the Internet of Things in rearing giant freshwater prawn is limitless. The system can effectively monitor water quality parameters, feed consumption, and other environmental parameters. © 2023 IEEE.

关键词： Internet of things

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Blockchain-based Data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Azmi, Azman Yahya, Farashazillah Moung, Ervin Gubin Sallehudin, Hasimi Utomo, Rio Guntur Azman, Nur Afrina Universiti Malaysia Sabah Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Data Technologies and Application Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Kebangsaan Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Malaysia Telkom University School of Computing Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Johor Iskandar Puteri Malaysia

ISBN: (纸本)9798350310689

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem. © 2023 IEEE.

关键词： Blockchain

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Simulation of SARSA-Based Reinforcement- Learning Dynamic SDN Migration Process 20th

Simulation of SARSA-Based Reinforcement- Learning Dynamic SD...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Ling, Jenniffer Teh Sue Tan, Saw Chin Wei, Siew Hong Zaki, Muhammad Faiz M. Omar, Nazaruddin Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Telekom Malaysia Kuala Lumpur. Selangor Malaysia Persiaran Multimedia Cyberjaya63100 Malaysia

ISBN: (纸本)9783031680045

This article presents a SARSA-RL-based dynamic SDN migration process, designed to tackle the dynamicity of Software-Defined Networking (SDN) migration process. Existing migration techniques often struggle with the dynamic nature of network conditions, resulting in suboptimal performance and disruptions. This work proposes a reinforcement learning (RL) approach using the SARSA algorithm, to create an intelligent agent capable of adapting the migration process based on changing network conditions, traffic demands, and budget constraints. The primary objective is to optimize the migration process under varying network conditions, improving network efficiency. Experimental results demonstrate the scheme's effectiveness in reducing network congestion and enhancing overall performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Reinforcement learning

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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Leveraging the Power of Object Detection Models in Identifying Litter for a Significant Reduction in Environmental Pollution 13

Leveraging the Power of Object Detection Models in Identifyi...

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13th International Conference on Computer and Knowledge Engineering, ICCKE 2023

作者： Xian, Lim Zhen Moung, Ervin Gubin Wi, Jason Teo Tze Saad, Nordin Yahya, Farashazillah Rui, Tiong Lin Farzamnia, Ali University Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia University Malaysia Sabah Faculty of Engineering Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350330151

The growing concern of litter pollution in natural environments has escalated into a significant issue that demands immediate and efficient resolution. Recent studies have used deep learning models to solve the problem of litter pollution, but these approaches have faced challenges in accurately detecting litter in real-world environments. Therefore, this paper has proposed a litter detection model and analyze its performance on the TACO dataset, which contains real-world outdoor environment images. The paper evaluates three distinct deep learning models (YOLOv4, YOLOv5, Faster R-CNN) and identifies the best performing model. The performance of the selected model is then enhanced through adjustments of hyperparameters, use of several preprocessing techniques and data augmentation techniques. The experimental results showed that YOLOv5x achieved 88% mAP@.5 and 71.4% mAP@.75 on testing dataset which outperformed the state-of-art studies. The findings of this paper provide valuable insights into the solution of litter pollution and can inform future research in this area. © 2023 IEEE.

关键词： Object detection

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HeartInspect: Heart Disease Prediction of an Individual Using Naïve Bayes Algorithm 11

HeartInspect: Heart Disease Prediction of an Individual Usin...

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11th IEEE Conference on Systems, Process and Control, ICSPC 2023

作者： Sabri, Nurbaity Shari, Anis Amilah Samah, Khyrina Airin Fariza Abu Noordin, Mohd Rahmat Mohd Shari, Asma Shazwani Mohd Ishak, Fadhilah Mustapha, Wan Masnieza Wan Affendi, Muhammad Fudhail Afiq Nor Rozaini College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Melaka Malaysia Universiti Teknologi Mara Cawangan Faculty of Bussiness Management Kelantan Malaysia

ISBN: (纸本)9798350340860

Heart disease is a serious health issue that contributes significantly to the high death worldwide. Therefore, the creation of a reliable system for heart disease prediction is essential for early intervention and better results. Such technologies can help identify at-risk persons and enable prompt preventive interventions by utilizing cutting-edge algorithms and analysing pertinent data. Nonetheless, predicting heart disease is a difficult endeavour, especially in underdeveloped regions with few diagnostic tools and a shortage of trained medical workers. Moreover, the healthcare sector produces a tremendous quantity of data on cardiac disease, yet these important resources are frequently underutilized when it comes to making well-informed decisions. Additionally, pricey heart diagnostics like electrocardiograms are now out of reach for the typical person due to increased living expenses. This present work suggests a Naive Bayes-based cardiac disease prediction system as a solution to these problems. The system makes use of a dataset that includes information about a person's heart disease status, height, weight, physical health, difficulties walking, age group, physical activity, general health, and sleep duration. For training and testing purposes, the dataset is partitioned 80/20. The dataset is analysed using the Naive Bayes technique to determine the chance of cardiac disease. Despite assuming independence among the characteristics, the system shows promising performance, reaching about 71-73% precision in heart disease prediction. Even though this falls short of higher standards, it is nevertheless a noteworthy accomplishment in light of the difficulties mentioned in the problem statements. In summary, the Naive Bayes algorithm-based heart disease prediction system reported in this work shows promise for predictions that are 71-73% accurate. This method helps address the problems involved with cardiac disease prediction, particularly in environments with limit

关键词： Python

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Natural Language Processing Approach for Fake News Detection Using Metaheuristics Optimized Extreme Gradient Boosting

Natural Language Processing Approach for Fake News Detection...

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Applied Intelligence and computing (AIC), IEEE World Conference on

作者： Aleksandar Petrovic Jasmina Perisic Luka Jovanovic Miodrag Zivkovic Milos Antonijevic Nebojsa Bacanin Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Informatics and Computer Science Union - Nikola Tesla University Belgrade Serbia

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The problem of fake news can be dangerous, as widely spread misinformation can cause serious physical and psychological harm. In the age of digitization and artificial intelligence (AI), particularly deep learning methods, which are able to generated synthetic text, being able to distinguish between real and fake news has become even greater challenge. Therefore, this research proposes a metaheuristics optimized machine learning (ML) approach with natural language processing (NLP) application for detecting fake news. The main classification model that was used is the eXtreme gradient boosting (XGBoost), which was tuned by the modified variable neighborhood search (VNS) algorithm. The NLP technique that was applied to transform text into the meaningful form for ML is the term frequency-inverse document frequency (TF-IDF). The performance of the VNS-tuned XGBoost was evaluated and compared with other cutting edge metaheuristics, where the proposed method obtained superior performance.

关键词： Machine learning algorithms Image edge detection Metaheuristics Psychology Transforms Boosting Natural language processing Classification algorithms Fraud Fake news

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Predicting Mortality in Heart Failure Patients: A Comparative Study of Decision Tree C4.5 and Naïve Bayes Algorithms 9

Predicting Mortality in Heart Failure Patients: A Comparativ...

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9th International Conference on informatics and computing, ICIC 2024

作者： Prasetya, Didik Dwi Arviansyah, Muhammad Busthomi Hidayat, Muhammad Taufiq Sari, Heni Vidia Zain, Azlan Mohd State University of Malang Department of Electrical Engineering and Informatics Malang Indonesia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia

ISBN: (纸本)9798331517601

The heart is one of many vital organs of humans that is used to pump blood to the entire part of the body. One of many diseases that attack the heart is heart failure. Heart failure arises from either structural or functional abnormalities that hinder the ventricle's capability to effectively pump blood throughout the body. Based on the data released by the Health Research Department, the prevalence of heart failure in Indonesia is 1.5%, which means 1 million Indonesians or more have heart problems. The mortality rate of heart failure patients can be reduced by data mining classification. The classification process uses decision tree C4.5 and the naïve Bayes algorithm. From 299 data of heart failure patients that were classified using a decision tree, the C4.5 algorithm got an 83.26% accuracy rate, and the naïve Bayes algorithm got a 79.26% accuracy rate. From the classification process that has been done, it can be seen that the decision tree C4.5 algorithm has better performance than the naïve Bayes algorithm in the classification process of heart failure patient data. © 2024 IEEE.

关键词： classification decision tree C4.5 heart failure naïve Bayes

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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