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Multivariate Analysis of Urban Air Pollution: Clustering and Patterns Across Major Asian Cities

Multivariate Analysis of Urban Air Pollution: Clustering and...

引用

Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Anika Rahman Mst. Taskia Khatun Dept. of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis and evaluate the effectiveness of six clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, and Spectral Clustering. Performance was assessed using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, WCSS, Cohesion, and Separation. The novelty of this work lies in the comparative analysis of these clustering methods on air pollution data, providing new insights into pollution dynamics across Asian cities. The analysis identified four distinct clusters- ‘High Pollution’, ‘Moderate Pollution’, ‘Ozone-Dominated Pollution’, and ‘Low Pollution’- with K-Means proving to be the most effective. Significant disparities were found, particularly in South and East Asia, where countries like India, China, and Pakistan exhibited the highest pollution levels. Additionally, an examination of capital cities revealed specific pollution patterns and the primary pollutants-PM2.5, NO2, CO, and Ozone-aiding in identifying sources and affected regions. These findings underscore the need for targeted regional pollution control strategies.

关键词： Gases Environmentally friendly manufacturing techniques Asia Urban areas Pollution control Clustering algorithms Transportation Air pollution Indexes Public healthcare

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A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks

A Performance Analysis Modeling Framework for Extended Reali...

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International Conference on Distributed Computing Systems

作者： Anik Mallik Jiang Xie Zhu Ran The Dept. of Electrical and Computer Engineering The University of North Carolina Charlotte NC USA The Dept. of Electrical and Computer Engineering University of Houston TX USA Dept. of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models.

关键词： Performance evaluation Analytical models Solid modeling Energy consumption Extended reality Computational modeling Wireless networks

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Generation of Correlated Random Numbers with Gaussian Distribution Using Box-Muller's Method and Central Limit Theorem

Generation of Correlated Random Numbers with Gaussian Distri...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Muhammad Ahmad Abdulfattah Akio Tsuneda Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Generation of high quality Gaussian random numbers is important for simulations across a wide range of disciplines. We investigated the auto-correlation functions of correlated random numbers that follows a Gaussian distribution generated by applying well-known transformation algorithms to uniformly distributed chaotic random numbers generated by 1-dimensional chaotic maps.

关键词： computers Chaotic communication Gaussian distribution

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An Intensive Review on Deep Learning Approaches in Non-Invasive Fetal Healthcare Monitoring

An Intensive Review on Deep Learning Approaches in Non-Invas...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Harismithaa. LR G. Sudha Sadasivam Dept. of Computer Science of Engineering PSG College of Technology Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and healthy life. Monitoring and diagnosing abnormalities at the right gestational age is very significant. There are remarkable technical advancements in fetal well-being evaluation, bridging the gap between prenatal medicine and Artificial intelligence. This study provides an intensive review of various modes of non-invasive data that can be used for fetal health monitoring and decision support. Also, existing studies on fetal health care that use deep learning techniques for diagnosis and monitoring are explored. Also, a generic framework designed for an Automated Fetal healthcare Monitoring system using AI approaches is presented.

关键词： Deep learning Reviews Medical services Fetus Expert systems Biomedical monitoring Monitoring Medical diagnostic imaging

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DubasAlert: AI-Powered Detection System for Dubas Infestation on Palm Leaves

DubasAlert: AI-Powered Detection System for Dubas Infestatio...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Y Nagaraju Venkatesh Dept. of Computer Science and Engineering UVCE Bangalore University Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different stages of their growth. Existing models perform ineffectively and have large trained weights, making integration with edge devices difficult. To address these issues, we propose a pruned YOLOv7 model which is lighter and has fewer parameters, resulting in 75% lower FLOPs. Pruned Yolov7 outperforms baseline models with a mAP@0.5 of 80.1%. Our approach enables real-time detection and seamless integration into edge devices, resulting in practical solutions to combat Dubas insect infestations.

关键词： Performance evaluation Knowledge engineering Communication systems Insects Image edge detection Production Real-time systems

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Meta-Adaptation Goals: Leveraging Feedback Loop Requirements for Effective Self-Adaptation

Meta-Adaptation Goals: Leveraging Feedback Loop Requirements...

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), IEEE International Conference on

作者： Raffaela Groner Ricardo Diniz Caldas Rebekka Wohlrab Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

Self-* systems focus on optimizing systems regarding adaptation goals, e.g. availability, that relate to properties of the managed system. While these adaptation goals are important, the feedback loop must also be considered. It is problematic if systems adapt too often, make too time-consuming changes that involve insecure transition states, or react too slowly when planning adaptations. Current approaches do not allow to enforce requirements on the adaptation loop—including sensitivity, sta-bility, and transition states. When planning adaptations, those meta-adaptation goals need to be considered and potentially traded off against traditional adaptation goals. Such a tradeoff might entail, for instance, that it may be worth it to adapt a system less frequently, although the overall availability of the system could be higher with more adaptations. We propose to explicitly consider feedback loop requirements for self-adaptive systems. Concretely, we propose using machine learning to predict quality measures based on the state of the feedback loop and the managed system. This information is used to plan for better adaptations that consider the adaptation goals and the meta-adaptation goals of the feedback loop. The explicit representation of meta-adaptation goals and adaptation goals enables the optimization of adaptation strategies, tradeoff analysis, and evaluation of adaptations.

关键词： Feedback loop Adaptation models Sensitivity Machine learning Planning Stakeholders Requirements engineering Distributed computing Optimization Monitoring

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ImportChainBD: Blockchain Solutions for Transparent and Accountable Imported Cosmetic Products

ImportChainBD: Blockchain Solutions for Transparent and Acco...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Md. Adib Khan Sagar Karmoker Ummay Khadiza Rumpa Habibur Rahman Md. Sabbir Hossain Nur Adnan Chowdhury Anik Mahamudul Hasan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand’s reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax.

关键词： Printing Supply chain management Government Finance QR codes Documentation Blockchains Stakeholders Security Internet of Things

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Revolutionizing Agricultural Finance: Simplifying Farmer Access to Financial Tools with an Innovative Fintech Platform

Revolutionizing Agricultural Finance: Simplifying Farmer Acc...

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Communication & Computing (WCONF), World Conference on

作者： Ibna Soud Joy Faria Basher Nusrat Sultana Md. Ahnaf Tahmid Sayeda Rahnuma Akthar Mahady Hasan Sadita Ahmed Dept. of Computer Science & Engineering Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Agriculture stands as the cornerstone of global sustenance and economic prosperity, yet smallholder farmers face significant financial barriers. Despite advancements in agricultural technology and markets, access to financial services remains limited for smallholders. We've thoroughly examined 25 research papers in this field to assess the extent of existing research. Subsequently, we conducted a comprehensive market analysis and competitive review to shape the design of our platform. This study introduces an innovative fintech platform aimed at bridging this gap by facilitating connections between farmers and financial service providers. By leveraging precision risk analysis, inclusive microloans, comprehensive insurance coverage and democratized investment opportunities, our platform seeks to enhance sustainable farming practices and boost productivity for smallholder farmers. Our platform utilizes data analytics to assess risk factors specific to agricultural activities, thereby providing personalized financial solutions and mitigating risks inherent in farming operations.

关键词： Productivity Economics Data analysis Shape Reviews Insurance Risk analysis Faces Farming Investment

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Automated Grading of Bananas Using Advanced YOLO Models: A Machine Learning and computer Vision Approach for Ripeness Classification

Automated Grading of Bananas Using Advanced YOLO Models: A M...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Md. Sariful Islam Robaaed Afred Tanik Noor Hossain Md Abdur Rahman Tamim Mohammed Imran Jubayer Ahmed Shawon Wahidur Rahman Samia Yasmin Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Among all fruits, bananas are consumed in high quantity around the world, and their health and industrial impact is enormous. The work presented here elaborates on how advanced machine learning and computer vision techniques can automate banana grading, particularly in its different versions of the YOLO model. In that respect, a dataset of 1899 images, representing five stages of ripeness, was manually created and pre-processed. Precision, recall, mean Average Precision at different IoU thresholds, and speed are applied to measure the performances of YOLOv8-n, YOLOv9-s, YOLOv10n, and YOLO11s. The experimental results demonstrated the high value of precision with 0.992 and a recall of 1.0 for the YOLOv8-n model, suitable for real-time applications, whereas YOLO11s attained the highest overall performance in various IoU thresholds with an mAP50-95 of 0.936. This work points out how automated grading systems can play an important role in improving both efficiency and the quality assessment of bananas, promoting consumer satisfaction while reducing post-harvest losses. Further research will be directed toward dataset enlargement, greater application of advanced imaging techniques, and overcoming practical challenges to implementation to improve the accuracy of grading and applicability in real situations, besides establishing a standardized framework for evaluation to gain more widespread acceptance of automated grading technologies in the agricultural sector.

关键词： YOLO computer vision Accuracy Computational modeling Customer satisfaction Imaging Machine learning Real-time systems Quality assessment Information technology

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks

Enhanced Predictive Modeling Techniques for Early Detection ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sneha Jha Prakash Sahu Santosh Kumar Dept. of Data Science CSVTU Chhattisgarh Computer Science & Engineering IIIT-Naya Raipur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of ${9 4 \%}$ and a validation accuracy of ${9 0 \%}$. In comparison, the RMSprop optimizer yielded a training accuracy of ${9 2 \%}$ and a validation accuracy of ${8 8 \%}$. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence

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