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

作者： MhD Saeed Sharif Madhav Theeng Tamang Anup Nepal Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model's data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making.

关键词： Training Accuracy Machine learning algorithms Biological system modeling Computational modeling Data visualization Predictive models Data models Forecasting Random forests

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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ICT Conference (AICTC), Arab

作者： Aysha Alfaw Wael Elmedany Mhd Saeed Sharif College of Information Technology University of Bahrain Sakir Bahrain Department Computer Science and Digital Technologies School of Architecture Computing and Engineering London UEL UK

ISBN: (数字)9798350342086

ISBN: (纸本)9798350342093

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Surveys Machine learning algorithms 5G mobile communication Insurance Machine learning Organizations Internet of Things Security Risk management Intelligent systems

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Quantifying the Impact of Large Language Models on Collective Opinion Dynamics

arXiv

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arXiv 2023年

作者： Li, Chao Su, Xing Fan, Chao Han, Haoying Xue, Cong Zheng, Chunmo College of Civil Engineering and Architecture Zhejiang University Zhejiang Hangzhou310000 China College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29631 United States

The process of opinion expression and exchange is a critical component of democratic societies. As people interact with large language models (LLMs) in the opinion shaping process different from traditional media, the impacts of LLMs are increasingly recognized and being concerned. However, the knowledge about how LLMs affect the process of opinion expression and exchange of social opinion networks is very limited. Here, we create an opinion network dynamics model to encode the opinions of LLMs, cognitive acceptability and usage strategies of individuals, and simulate the impact of LLMs on opinion dynamics in a variety of scenarios. The outcomes of the simulations inform about effective demand-oriented opinion network interventions. The results from this study suggested that the output opinion of LLMs has a unique and positive effect on the collective opinion difference. The marginal effect of cognitive acceptability on collective opinion formation is nonlinear and shows a decreasing trend. When people partially rely on LLMs, the exchange process of opinion becomes more intense and the diversity of opinion becomes more favorable. In fact, there is 38.6% more opinion diversity when people all partially rely on LLMs, compared to prohibiting the use of LLMs entirely. The optimal diversity of opinion was found when the fractions of people who do not use, partially rely on, and fully rely on LLMs reached roughly 4:12:1. Our experiments also find that introducing extra agents with opposite/neutral/random opinions, we can effectively mitigate the impact of biased/toxic output from LLMs. Our findings provide valuable insights into opinion dynamics in the age of LLMs, highlighting the need for customized interventions tailored to specific scenarios to address the drawbacks of improper output and use of LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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Climate change and wind energy potential in South America

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Science of the Total Environment 2024年 957卷 177675页

作者： Martinez, A. Iglesias, G. School of Engineering & Architecture & MaREI Environmental Research Institute University College Cork College Road Cork Ireland University of Plymouth School of Engineering Computing & Mathematics Marine Building Drake Circus PlymouthPL4 8AA United Kingdom

Wind energy is crucial in mitigating greenhouse gas emissions and combating global warming. However, the economic viability of wind energy projects is tied to wind resources, which may be affected by climate change. The objective of this work is to investigate the evolution of wind energy resources in South America using the most up-to-date climate-change scenarios: the Shared Socioeconomic Pathways. Three scenarios are considered, corresponding to low, medium and high emissions pathways. A multi-model ensemble is constructed with Global Climate Models selected for their accuracy in the study region in a historical period. Results indicate that east of the Andes, in Brazil, Paraguay and Venezuela, substantial increases in average wind power density are expected, surpassing 100 % in certain areas and in the most pessimistic scenario (the fossil-fuelled development pathway). By contrast, significant reductions (up to ~50 %) are projected for certain areas west of the Andes: South Chile, Peru and the waters off West Colombia. South Chile has the windiest climate in the continent, which makes this reduction due to climate change all the more important. Even under the low-emissions scenario, the projected evolution in wind resources is relevant, which highlights the necessity of considering climate change in wind energy planning. © 2024 The Authors

关键词： Renewable energy Sustainability Wind energy Wind power SSP Climate change

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Architectural licensing in Nigeria, a pathway to professionalism or an obstacle to practice?

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Journal of Building Pathology and Rehabilitation 2025年第2期10卷 1-18页

作者： Okonta, Ebere Donatus Okeke, Francis Ogochuwku Oluigbo, Chinedu Uchechukwu Mgbemena, Emeka Ebuz School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UK School of Engineering Technology and Design Canterbury Christ Church University Canterbury UK Design Surveying and Planning East Kent College Canterbury UK Department of Architecture Niger Delta University Bayelsa Nigeria Department of Architecture Obafemi Awolowo University Ile-Ife Nigeria

Licensure is the ultimate milestone in the journey towards becoming an architect. However, anecdotal evidence suggests many architecture graduates lack interest in getting a practice license. Numerous investigations have yet to undertake a qualitative assessment to ascertain the advantages arising from the professional licensing of architects in Nigeria. By employing a combination of narrative literature reviews and 37 semi-structured interviews with architects, this study qualitatively investigates how architectural licensing in Nigeria impacts the profession and the broader built environment considering the unique challenges and opportunities in the country's dynamic construction landscape. The data were categorised according to emerging themes and qualitatively analysed using thematic analysis. Findings underscore the critical role of licensing in upholding professionalism, ethical standards, legal accountability in the architectural community and the quality of architectural practice within the built environment. It reveals that the registration trend in the last 47 years has been inconsistent, resulting in a shortage of licensed architects, highlighting barriers to obtaining and maintaining licenses to include regulatory inefficiencies and socio-economic challenges. Recommendations include policy interventions, streamlined licensing processes, and mentorship programs to increase the number of licensed architects. This work contributes to understanding licensing’s role in improving architectural performance and addressing national development needs.

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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Cognitive computing Applications in Real-Time Financial Risk Management

Cognitive Computing Applications in Real-Time Financial Risk...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Hari Prasad Josyula Sunil Rajaram Landge Piyush Ranjan Kishan D. Gajjar P. Anand Kumar Mohammad Jamal Bdair Management Sciences (BBA Information System) PSG College of Arts & Science Civil Aerodrome Post Sitra Coimbatore School of Architecture Computing and Engineering University of East London

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The incorporation of cognitive computing into realtime financial threat operations, to highlight the revolutionary potential of this technology for the financial sector. Cognitive computing, which is a combination of artificial intelligence, machine literacy, and natural language processing, improves the capability to reuse enormous quantities of financial data quickly and directly. The purpose of this study is to investigate how cognitive systems can recognise, evaluate, and mitigate financial risks in real-time. These systems offer greater prophetic analytics and decision-making support in comparison to traditional methods. Cognitive computing makes it possible to conduct more accurate threat assessments, detect implicit dangers earlier, and improve nonsupervisory compliance. This is accomplished through the use of sophisticated algorithms and data analytics. Case studies illustrate the practical applications of these technologies through the use of colourful financial scripts, demonstrating how beneficial they are in improving threat operating methods. In the final section of the study, the author discusses the counterarguments that cognitive computing poses for the future of fiscal threat operation, as well as the implicit issues that are associated with its widespread abandonment.

关键词： Threat modeling Technological innovation Prevention and mitigation Decision making Real-time systems Natural language processing Stability analysis Regulation Cognitive systems Predictive analytics

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ruyi Qian Bojun Cao Mengjuan Gao Qinwen Shi Yida Wang Yuanchao Xu Qirun Huo Keni Qiu College of Information Engineering Capital Normal University Beijing China SKL of Computer Architecture Institute of Computing Technology CAS Beijing China

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm—EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead.

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification 1

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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1st International Conference on computing, Sciences and Communications, ICCSC 2024

作者： Bdair, Mohammad Ruby, E D Kanmani Juyal, Prachi Taseen, Rakheeba Abirami, A. Soujanya, A. School of Architecture Computing & Engineering University of East London London United Kingdom Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Uttarakhand Dehradun India School of computer science and Engineering Presidency university Bangalore India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Ibrahimpatnam India

ISBN: (纸本)9798350353648

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study. © 2024 IEEE.

关键词： Lung cancer

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yuanchao Xu Ruyi Qian Yida Wang Qirun Huo College of Information Engineering Capital Normal University Beijing China SKL of Computer Architecture Institute of Computing Technology CAS Beijing China

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time.

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