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检索条件"机构=Faculty of computing and informatics"

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M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast Cancer Histopathology Using Contextual Patches and Attention Mechanism

M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast C...

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IEEE International Symposium on Biomedical Imaging

作者： Arbab Sufyan Mohammad Faizal Ahmad Fauzi Wong Lai Kuan Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Computing & Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Histopathological analysis, particularly of nuclear morphology, is critical for identifying malignancies. Accurate nuclei segmentation plays a pivotal role in this process, as it enables detailed assessment of nuclear size, shape, and distribution patterns. Traditional segmentation methods, however, often fail to capture fine details, lack broader context, and struggle with overlapping nuclei of varying sizes and shapes, especially when relying on single-scale approaches. To address these challenges, we propose a multi-scale context-based encoder-decoder model named M3-Net (Multi-Scale, Multi-Level, Multi-Stream Network) that integrates both global and local tissue features. Evaluations demonstrate that M3-Net effectively segments overlapping nuclei and diverse structures, providing a robust solution for automated nuclei segmentation in breast cancer pathology.

关键词： Image segmentation Accuracy Image analysis Histopathology Shape Image color analysis Morphology Breast cancer Context modeling Biomedical imaging

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SODIBOT: A TWO in ONE Real-Time Human Monitoring System using YOLO algorithms 8

SODIBOT: A TWO in ONE Real-Time Human Monitoring System usin...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Aziz, Azim Zaliha Abd Adnan, Nurul Nadzirah Mansor, Nur Farraliza Wan Awang, Wan Suryani Nurhaida, Ida Jaya, Safitri Universiti Sultan Zainal Abidin Faculty of Informatics Computing Terengganu Malaysia Universitas Pembangunan Jaya Department of Informatics Tangerang Selatan Indonesia Universitas Pembangunan Jaya Center for Urban Studies Tangerang Selatan Indonesia

ISBN: (纸本)9798350310931

COVID-19 primarily spreads through direct physical contact. As a precaution, it is recommended that each individual to keep at distance of at least one meter between one another. This study proposes a realtime system named as Social Distancing and Body Temperature (SODIBOT) for monitoring physical distancing compliance and body temperature measurement in indoor spaces during this endemic using computer vision and deep learning techniques. The method suggested in the study utilizes the You Only Look Once (YOLO) object detection algorithm to detect individuals and promptly estimate their physical distance in realtime using a high-end thermal camera. Every identified human is assigned a color-coded bounding box with a distinct meaning. Simultaneously, body temperature is also recorded and displayed at the top of each bounding box. The effectiveness of the proposed system was measured by the number of people detected in frame per second. Furthermore, the system's ability to measure and display individual body temperatures at the top of the bounding boxes adds additional value to SODIBOT. The outcomes illustrate the potential of SODIBOT in effectively monitoring compliance with physical distancing in indoor environments, offering valuable insights for potential application in other public health scenarios. © 2023 IEEE.

关键词： COVID-19

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

引用

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Dynamic SDN Multiple Nodes Migration Using SARSA Reinforcement Learning 20th

Dynamic SDN Multiple Nodes Migration Using SARSA Reinforceme...

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

作者： Sue Ling, Jenniffer Teh 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 addresses the problem of software-defined Networking (SDN) migration process under dynamic network conditions and present a SARSA-based approach to tackle the challenges associated with it. Traditional SDN migration techniques often fail to adapt effectively to dynamic network environments, leading to suboptimal performance. To overcome these limitations, this article explores the design of State–Action–Reward–State–Action (SARSA) reinforcement learning in SDN migration process. The article presents a comprehensive analysis of the existing literature on SDN migration process and the design of reinforcement learning, highlighting the limitations of current approaches in dynamic network scenarios. It then presents a SARSA-based SDN migration system that utilizes reinforcement learning to adaptively migrate multiple network nodes in response to changing network conditions. Overall, this article gives insightful information into the design of SARSA reinforcement learning in SDN migration process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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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...

引用

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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