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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Yang, You Zhi Ramly, Athirah Mohd Sharif, Mhd Saeed Department of Computing and Information Systems Sunway University Selangor Malaysia School of Architecture Computing and Engineering UEL London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

This paper focuses on enhancing the security of a chosen system by implementing multiple layers of protection, including concealing sensitive data on the Liquid Crystal Display (LCD), utilizing hashed Unique Identifiers (UIDs), and deploying biometric dual-factor authentication via fingerprint recognition. The system is considered secure as it prevents unauthorized access attempts, even if the tag appears legitimate. The paper covers various stages, from component procurement to vulnerability assessment, exploitation, and solution development. While this paper significantly improves system security, further investigation remains crucial. Therefore, this paper not only addresses immediate security implementations but also paves the way for ongoing analysis and improvements aimed at defending the system against malicious threats while optimizing its performance. ©2024 IEEE.

关键词： Liquid crystal displays

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Leveraging Artificial Intelligence to Secure Wireless Network: Exploring Threats, Existing Approaches, and Proposed Mitigation Strategies

Leveraging Artificial Intelligence to Secure Wireless Networ...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Nan Xin, Alice Lee Ramly, Athirah Mohd Behjati, Mehran Sharif, Mhd Saeed Department of Computing and Information Systems Sunway University Selangor Malaysia School of Architecture Computing and Engineering UEL London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

The exponential growth of network has introduced new Internet-of-Things (IoT) use cases that has enabling us convenience and comfort. The surge of IoT devices due to the capabilities brought by fifth generation (5G) have given rise to security threats and attacks, particularly malware attacks IoT botnets have been an alarming issue, where smart devices can be manipulated by malicious actors to commence subsequent attacks such as Denial of Service (DoS). Traditional and complex security techniques may not be a viable solution towards these resource-constrained devices with limited processing power. Machine Learning techniques (ML) are the rising trend, and it is often used in Intrusion Detection Systems and Network Anomaly Detection. This paper emphasizes on analyzing and comparing various ML models on the IoT-23 dataset. It aims to predict anomalies and conclude the model with optimal performance and least computational time cost that can be used for network anomaly detection systems with real-time data in future works. The ML models used in this paper are Decision Trees (DT), K-nearest neighbours (KNN), Random Forest (RF), Naïve Bayes (NB) and Histogram Gradient Boosting (HGB). DT displayed the best performance with an accuracy score of 73% and F1 score of 0.49 with a time cost of 28.22 seconds. ©2024 IEEE.

关键词： 5G mobile communication systems

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction 4

Artificial Intelligence Models in Power Generation for Energ...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Tan, Lit Yang Chen Kwan, Charis Shwu Ajibade, Samuel-Soma M. Ramly, Athirah Mohd School of Engineering and Technology Sunway University Department of Computing and Information Systems Selangor Malaysia School of Architecture Computing and Engineering University of East London Department of Computing London United Kingdom

ISBN: (纸本)9798350353266

The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models, often used in different fields, to predict energy-related patterns, providing a unique and specialized viewpoint. The synergy of academicians, AI experts, and industry professionals in the energy sector has resulted in the creation of customized AI models to optimize operational efficiency. By customizing various AI models to suit the distinct attributes of energy scenarios and datasets, these models are positioned to transform energy management methods. This study examines the utilization of AI models to enhance energy efficiency in power generation in Malaysia. The project seeks to predict future power consumption in various sectors, analyze growth rates, and identify sectors with investment potential by developing a Linear Regression model. In addition, a thorough power plan is developed using the estimated energy usage. A comparative analysis is performed to determine the most appropriate model for this particular scenario, which will improve decision-making in the energy sector. The results of this study present promising opportunities for further investigation. By broadening the study's focus to encompass a broader array of AI models and their assessment of performance, it is possible to gain useful insights for predicting power generation. Furthermore, the integration of real-time data streams and the inclusion of feedback loops in the AI models could improve their ability to adapt and increase their accuracy as time progresses. © 2024 IEEE.

关键词： Prediction models

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OvOptix: Optical Based Egg Sorting Technology

OvOptix: Optical Based Egg Sorting Technology

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2024 International Conference on Computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Matias, Jenirose Binayug, Joshua Nikolai Melad, James Carl Ogoy, Romar John Lorenzo, Ariel Decena, Rudylene School of Architecture Computing and Engineering University of Saint Louis Cagayan Tuguegarao City Philippines

ISBN: (纸本)9798350368802

This paper introduces a machine learning and AI-based system for detecting internal and external irregularities in table eggs, addressing the limitations of current egg sorting machines. By leveraging Internet of Things (IoT) technology, the system determines if an egg is defective. YOLOv8 and machine vision were integrated for enhanced detection, using a web camera to capture images and reveal defects not visible through traditional methods. The system's lightweight design is ideal for real-time detection with limited computational resources. A custom egg tray facilitated dataset collection, achieving 96.8% effectiveness, 95.1% reliability, and 91.2% usability. The system demonstrated live detection with an average speed of 450 milliseconds, proving that the OvOptix machine can efficiently meet the high production demands of the egg market. © 2024 IEEE.

关键词： Machine vision

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Securing IoT Devices Against Emerging Security Threats: Challenges and Mitigation Techniques

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Journal of Cyber Security Technology 2023年第4期7卷 199-223页

作者： Aziz Al Kabir, Mohammed Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering UEL London United Kingdom

The increasing prevalence of IoT devices has brought about numerous security challenges due to their relatively simple internal architecture and low-powered hardware warranted by their small footprint requirement. As there are billions of IoT devices in use today, the sheer number of such devices pose a great security challenge as they are often constrained by a number of hardware and software limitations in addition to being designed with a focus on convenience, ease of use, mass production, and low cost, rather than security. The seemingly exponentially increasing number of such devices make it harder to keep track of–and patch–insecure IoT devices. This paper explores the common security threats, attacks, and vulnerabilities relating to IoT devices and highlights the challenges associated with securing them against emerging security threats and cyberattacks. Due to their role as gateways to connected devices and susceptibility to forming botnets or facilitating man-in-the-middle attacks, IoT devices are a lucrative target for cybercriminals. The paper discusses various remediation and mitigation techniques that can be implemented to better secure IoT devices, including access control mechanisms, secure communication protocols, and regular updates and patches. By better understanding the security challenges associated with IoT devices and implementing effective mitigation techniques, individuals and businesses can ensure the safety, security, and privacy of their connected devices and networks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cybersecurity

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Decentralized Healthcare Crowdfunding: Leveraging Smart Contracts for Global Medical Research and Treatment

Decentralized Healthcare Crowdfunding: Leveraging Smart Cont...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Agholor, Miracle Khatoon, Shaheen Sharif, Mhd Saeed Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering University of East London United Kingdom

ISBN: (纸本)9798331533137

This article discusses the creation of a decentralized healthcare crowdfunding platform using Thirdweb technology. It explores the growth of crowdfunding in healthcare emphasizing its role in funding treatments from various sources. Multiple existing fundraising platforms are identified to understand their suitability for medical research and treatment to carry on funding campaigns. The goal is to develop a decentralized medical funding application for research and treatment that enables researchers to create fundraising campaigns on the Ethereum blockchain while ensuring transparent and auditable usage of funds through smart contracts. The study’s main contributions include a development process for healthcare crowdfunding platforms, enhanced security through audited smart contracts, improved scalability for global initiatives and a user-friendly interface to encourage broader usage. As proof-of-concept validity, the fundraising application was deployed to blockchain and can be accessed by the public. ©2024 IEEE.

关键词： Decentralized finance

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Machine Learning for Enhanced Underwriting: Predicting Premiums in Health Insurance 4

Machine Learning for Enhanced Underwriting: Predicting Premi...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Abdelshafy, Nada Abdelshafy, Mohamed Ghazanfar, Mustansar Ali Saglam, Rahime Belen University of East London School of Architecture Computing and Engineering London United Kingdom Coventry University School of Computing Mathematics and DataScience Coventry United Kingdom

ISBN: (纸本)9798331520038

In the evolving field of Insurance, Machine Learning (ML) has emerged as a transformative tool for enhancing the accuracy and efficiency of insurance underwriting, particularly in health insurance, a sector experiencing increased demand post-pandemic. This research develops an ML model aimed at improving premium prediction within this domain. Utilizing a health insurance database from Kaggle, we conducted a comparative analysis of several ML models - XGBoost, Random Forest, Neural Network Regression, and a baseline Linear Regression - to establish performance benchmarks. Our methodology included rigorous data analysis and application of ML model optimization techniques such as feature engineering and selection, hyperparameter tuning through Grid Search, Random Search, and Bayesian Optimization, as well as overfitting prevention strategies like Pruning and Early Stopping. The XGBoost model demonstrated superior performance, achieving a Mean Absolute Error (MAE) of 2582.3, Root Mean Square Error (RMSE) of 4625.0, and an R2 value of 86.59%. This research not only advances the application of ML in predictive premium pricing but also provides a structured approach for future studies in the insurance industry's underwriting processes. © 2024 IEEE.

关键词： Health insurance

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Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends

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SN Computer Science 2024年第6期5卷 717页

作者： Valizadeh, Alireza Amirhosseini, Mohammad Hossein NEX Power Ltd Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London

Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively. © The Author(s) 2024.

关键词： Artificial intelligence Data-driven approach Lithium-ion battery Machine learning

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Innovative Ensemble Approaches for Assessing Critical Factors for European Tick Abundance

Innovative Ensemble Approaches for Assessing Critical Factor...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Zorto, Abin Lansdell, Samantha Seto, Misaki Negera, Edessa Cutler, Sally Sharif, Mhd Saeed Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL United Kingdom Health Sport and Bioscience UEL United Kingdom Leader of Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

This research uses complex machine learning methods to explore the driving factors behind increasing Ixodes ricinus populations in Europe. This tick species plays a key role in transmission of tick-borne diseases, which present a threat to both human and animal health. Tick prevalence rates were examined alongside climatic features (temperature and rainfall) and habitat features (land use category and vegetation levels). The data were analysed using Ensemble modelling approaches (XGBoost, LightGBM, CatBoost, Voting Regressor, Bagging Regressor, Stacking Regressor and AdaBoost Regressor) which were selected for their ability to embrace this complex and multifactorial dataset. The predictive performance of these models was carefully evaluated using Root Mean Square Error (RMSE) and R-squared (R2) (both in the presence and absence of outliers). Our results revealed that land use was the dominant factor for predicting tick occurrences. Stacking Regressor showed the highest performance, achieving an RMSE of 2.34 and an R2 value of 0.99. After removing outliers, the models showed better results with a decrease in average RMSE from 13.14 to 7.92 and R2 increase from 0.21 to 0.81 suggesting that outlier management impacts upon model performance. Our machine learning algorithm accurately predicts tick abundance and provides a key insight into which areas are most likely to be at risk, guiding future location-specific intervention to combat these risks, which will ultimately act as a powerful tool in the fight against tick-borne diseases. ©2024 IEEE.

关键词： Adversarial machine learning

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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