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

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

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

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.

关键词： Procurement Access control Technological innovation Authentication Fingerprint recognition Liquid crystal displays Protection Informatics Radiofrequency identification

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Evaluation of delays in technical approval of UK Highways Act Section 278 projects

International Journal of Commerce and Contracting

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International Journal of Commerce and Contracting 2024年第3-4期8卷 97 - 120页

作者： Meghdad Bagheri Edward Bate School of Architecture Computing and Engineering University of East London London UK School of Energy Construction and Environment Coventry University Coventry UK

This paper attempts to address the causes of delays to the legal and technical approval processes involved in the creation of agreements authorising works to public highways under s.278 Highways Act (1980) specific to Warwickshire County Council, UK, and whether the type of contract (JCT or NEC) or s.278 agreement (minor or major) has any tangible influence. A series of questionnaires and interviews were carried out on a sample group of individuals including designers, developers, construction lawyers and council engineers with extensive industry experience in relation to s.278 legal, technical, construction and adoption processes. The results revealed the key causes of delays, and therefore, the barriers to prompt and efficient approval processes, as the lack of communication between developer and local authority, inexperienced developers’ engineers, poor quality drawings and insufficient information in the local authority's design guide. These key factors are discussed and recommendations are provided to tackle these issues.

关键词： s.278 agreement Joint Contracts Tribunal New engineering Contract

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Occupancy Detection for HVAC Systems Using IoT Edge computing and Vision-Based Image Processing

Occupancy Detection for HVAC Systems Using IoT Edge Computin...

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IEEE International Conference on Utility and Cloud computing (UCC)

作者： Tariq Akhtar Shaheen Khatoon Azhar Mahmood School of Architecture Computing and Engineering University of East London London United Kingdom

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Energy efficiency, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems, is a critical challenge in modern building management due to the increasing energy demands and environmental impacts. This paper focuses on developing optimized object detection models using machine vision for occupancy detection in office environments, aiming to improve HVAC efficiency. The primary objective is to compare three models—YOLOv8n, YOLOv9c, and YOLOv10n—against the Faster R-CNN baseline, emphasizing detection speed, computational efficiency, and small object detection. Data collection involved creating a custom dataset of 1,728 images from office environments, annotated with eight object classes, including persons and office devices. Preprocessing techniques such as grayscale conversion, image resizing, and augmentation improved the model’s ability to detect objects under various conditions, including occlusion and varied camera angles. The models were evaluated based on mAP@50, mAP@50-95, and detection speed. YOLOv9c outperformed Faster R-CNN in speed and accuracy, achieving a mAP@50 of 88.0% and mAP@50-95 of 59.8%, making it the most balanced model. YOLOv8n demonstrated the fastest detection speed, ideal for real-time applications, while YOLOv10n, though less accurate, provided a strong trade-off between speed and precision. Despite these successes, challenges remain, particularly in small object detection and dataset size. Future work includes expanding the dataset to 100,000 images, improving detection of smaller objects, and integrating the object detection models into real-time HVAC control systems. Moreover, deployment on edge devices, transfer learning, and integration with Building Management Systems (BMS) for dynamic HVAC control represent promising areas for future research.

关键词： YOLO HVAC Accuracy Computational modeling Image edge detection Data augmentation Real-time systems Energy efficiency Data models Optimization

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Coupled THMC model‑based prediction of hydraulic fracture geometry and size under self‑propping phase‑transition fracturing

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International Journal of Coal Science & Technology 2024年第6期11卷 29-53页

作者： Nanlin Zhang Fushen Liu Liangliang Jiang Pinqiang Mo Jingwen Xiao Qi Song Yuhao Luo Computing Center for Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Engineering Research Center of Urban Underground Development of Zhejiang Province Hangzhou 310058China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryAB T2N1N4Canada State Key Laboratory of Intelligence Construction and Healthy Operation and Maintenance of Deep Underground Engineering School of Mechanics and Civil EngineeringChina University of Mining and TechnologyXuzhou 221116JiangsuChina Safety Environment and Technology Supervision Research InstitutePetro China Southwest Oil and Gas Field CompanyChengdu 610041China

The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable *** involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture *** study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case *** results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving *** length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture *** the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to *** study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.

关键词： Sustainable development,Self Propping phase Transition fracturing technology,Thermal Hydraulic Mechanical Chemical coupling,FEM DVIB EPM,Hydraulic fracture propagation

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Examining the Software Developers’ Perception in Open-Source Software of Blockchain Project Using Association Rules Mining 9th

Examining the Software Developers’ Perception in Open-Sourc...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Abd Wahab, Alawiyah Hj. Ibrahim, Huda SarkinTudu, Shehu M. Romo, Bilyaminu A. School of Computing University Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer and Information Technology Sokoto State University P.M.B 2134 Near Airport Road Sokoto State Sokoto Nigeria School of Architecture Computing and Engineering University of East London London United Kingdom

ISBN: (纸本)9789819995882

Developers are vital in enhancing blockchain projects like Bitcoin through feature additions, bug fixes, and performance optimization. However, comprehending developers’ perception of the growing amount of information regarding new features and bug fixes becomes challenging as blockchain projects gain popularity. Data mining, a technique that extracts valuable patterns and information from extensive data sets, assists in decision-making. The Apriori algorithm, widely used in data mining, uncovers association rules among sets of items. Despite its effectiveness, the Apriori algorithm remains relatively underutilized in the field of Open Source Software (OSS) developer turnover. Previous studies have employed approaches like mining software project repositories, social network analysis, quantitative data analysis, and surveys, which shed light on turnover but fail to reveal interesting relationships and patterns related to subjective factors and collaboration. To address this gap, this paper proposes combining survey data with association rule mining. This approach aims to identify co-occurrence patterns between specific personal and project-related variables (e.g., intention to learn or system integration). By analyzing these variables and their associations, the paper intends to provide valuable insights to project leaders, aiding decision-making in developer turnover management. Ultimately, this research contributes to enhancing the quality of blockchain projects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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Machine Learning for Enhanced Underwriting: Predicting Premiums in Health Insurance

Machine Learning for Enhanced Underwriting: Predicting Premi...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

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

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

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.

关键词： Analytical models Service robots Prevention and mitigation Neural networks Insurance Pricing Root mean square Tuning Random forests Optimization

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OvOptix: Optical Based Egg Sorting Technology

OvOptix: Optical Based Egg Sorting Technology

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International Conference on Computer engineering, Network and Intelligent Multimedia (CENIM)

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

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

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.

关键词： Productivity Accuracy Machine vision Reliability engineering Real-time systems Quality assessment Internet of Things Usability Standards Sorting

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

作者： Miracle Agholor Shaheen Khatoon Mhd Saeed Sharif 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: (数字)9798331533137

ISBN: (纸本)9798331533144

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.

关键词： Technological innovation Crowdfunding Scalability Smart contracts Collaboration Medical services Blockchains Security Stakeholders Informatics

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

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

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

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 R 2 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 R 2 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.

关键词： Technological innovation Accuracy Temperature Biological system modeling Stacking Habitats Europe Predictive models Data models Diseases

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Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review 20

Analysis of Machine Learning (ML) and Internet of Things (Io...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Legaspino, Rosie Fe B. Gimeno, Anabella R. Ypanto, Leomar A. Baird, Jeem Clyde C. Adediran, Anthonia Oluwatosin Sunway University School Of Engineering And Technology Department Of Computing And Information Systems Selangor Malaysia Cebu Technological University Cebu Moalboal Philippines Federal University Of Uberlandia Faculty Of Architecture And Urban Design Uberlândia Brazil

ISBN: (纸本)9798350382310

The objective of this paper is to critically examine the publication's trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of Technology (India). However, the top country and research funder are;India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics. © 2024 IEEE.

关键词： Machine learning

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