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Optimizing Quality of Service in Fog Computing using Blockchain Integration: Exploring Approaches, Challenges, and Performance Evaluation

Optimizing Quality of Service in Fog Computing using Blockch...

引用

2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Belsare, Rosahn G. Ambhore, P.B. Government College of Engineering Department of Computer Science & Engineering Amravati India

ISBN: (纸本)9798350374100

In today's computing era, fog computing has emerged as a transformative paradigm, facilitating real-time data processing at the network edge to reduce latency and enhance responsiveness. However, ensuring optimal Quality of Service (QoS) in fog environments, while safeguarding data integrity, presents a tough challenge. This research endeavours to address this critical concern by introducing a comprehensive framework that systematically boosts QoS through the meticulous integration of blockchain technology. Probing into the association of fog computing and blockchain, this study pioneers sophisticated strategies that target the reduction of latency, the amplification of scalability, and the precision management of resources. Through complex process of implementation and systematic evaluation, our proposed strategies manifest demonstrable enhancements across key QoS metrics, prominently including latency and throughput. These enhancements are substantiated through the inclusion of practical case studies that highlight the tangible benefits of our approach in a diverse range of scenarios. This research advances the understanding of the synergistic potential of fog computing and blockchain, while simultaneously empowering practitioners with actionable insights to bind these technologies harmoniously. By combining these innovations, both enhanced QoS and fortified data processing at the network edge become attainable objectives, furthering the evolution of modern computing paradigms. © 2024 IEEE.

关键词： Quality of service

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WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences 8

WRISTVIEW: Augmented Reality and Generative AI Integration f...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Prasanna Kumaar, D. Abisha, D. Ida Christy, J. Navedha Evanjalin, R. Rajesh, P. Mohamed Haris, K. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.

关键词： Virtual environments

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LeNet - IoT Security Aware Intrusion Detection Framework Using Deep Learning

LeNet - IoT Security Aware Intrusion Detection Framework Usi...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ananthajothi, K. Adharshini, C. Amirdha, M. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

The rising complexity of cyberthreats created the demand for compelling cybersecurity solutions that along with expanding Internet of Things (IoT) devices has made them indispensable. In the work our proposal is a Deep Learning-Assisted Intrusion Detection System (IDS), based on the LeNet architecture, that can be used for the identification and prevention of potential intrusions in Internet of Things environments. To increase the detection accuracy, we developed our method based on a properly structured preprocessing step comprising normalisation and data augmentation using image-based and QR-based datasets. We take a look at the architecture of LeNet including the pooling and convolutional layers that help to identify important features of the input dataset. The final parts of the model are fully linked layers and a Softmax classifier that predicts the type of anomaly. Performance metrics measure the efficacy of the IDS including precision, recall, accuracy, and F1 score that reveal our method surpass the classical methods with a high margin. We shall describe in this paper how one can train the LeNet model using IoT-specific attack data so that it could be used to detect intrusion in surrounding network space and inject the walled-off attack, thereby facilitating threat prevention. Since the model is GPU accelerated, it converged close to what was able to protect the IoT network effectively from the evolving cyber-attacks and was quite close to its high accuracy and real time efficient performance in terms of time. © 2024 IEEE.

关键词： Network intrusion

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Customer Segmentation and Retention Strategies for Bank Using Machine Learning

Customer Segmentation and Retention Strategies for Bank Usin...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Beedhi Reddy, Hemanth Kumar Vijay Anand, M. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

Customer churn is a pressing problem faced by banks, affecting their revenues and customer satisfaction. To solve this problem, this study explores the use of machine learning, specifically Naive Bayes, Decision Trees, and AdaBoost classifiers, to predict and reduce customer churn. By leveraging these complex algorithms, banks can gain insight into customer behaviour and take proactive steps to retain valuable products as customers. The process starts with pre- processed data, model design and coding. This optimization can solve many problems such as excessive priority, missing data, categorical features, etc. Additionally, Data Analytics (EDA) and visualizations to gain a deeper understanding of customer segmentation;This allows to uncover important trends and good standards. When evaluating performance in the model, Focused on simple metrics such as recall and ROC- AUC scores. These metrics provide insight into the model's ability to accurately identify past customers while minimizing false positives. To improve the performance of model, hyperparameter tuning was performed to ensure that we achieved the highest accuracy. The Model adopt ROC-AUC curve to see the balance between true positive and negative value. This analysis provides a better understanding of the performance model, allowing to make informed decisions and strategies to manage the customer's quality. Actual and estimated loss status is displayed as an estimate of Customer's final model. This valuable information provides banks with the strategies necessary to identify potential credit losers and take the necessary steps to maintain their trust. © 2024 IEEE.

关键词： Sales

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Integrated Colorization and Styling Approach for Enhancing Image Clarity 3

Integrated Colorization and Styling Approach for Enhancing I...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, Gollapalli Madhu Anand, Rakam Sankeerth, Thalloju Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

Image exploration adopts several dimensionalities with web-based tasks, integrating Streamlit and OpenCV to create an interactive platform for advanced image enhancement. Alongside the transformative tool enabling conversion of black and white images into vibrant compositions, an innovative styling suite offers diverse artistic filters. Notably, the addition of a camera option allows users to capture photos directly within the interface. Through Streamlit's intuitive interface and OpenCV's robust image processing, users experience real-time previews for seamless colorization and filtering. The proposed methodology involves designing a responsive web interface, optimizing Streamlit widgets for file uploads and style selection, and leveraging OpenCV for image processing. Results demonstrate the application's effectiveness, providing immediate insights into style impact. Challenges include processing speed optimization, with future research directions exploring advanced Machine Learning (ML) models for dynamic style transfer. In summary, this research advances interactive image processing, offering a refined platform for creative visual content enhancement. The results obtained is promising leading to effective image colorization and styling, thereby improving the quality of the image. © 2024 IEEE.

关键词： Image enhancement

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Mobile Marker-Based AR Children App for Learning Map 7

Mobile Marker-Based AR Children App for Learning Map

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Nithya Shree, T. Grace Selvarani, A. Sri Ramakrishna Engineering College Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372816

Marker-based Augmented Reality (AR) technology has gained substantial attention in recent years due to its potential to enhance user interactions with the real world by overlaying digital content onto physical objects. This research paper presents a prototype mobile application of marker-based AR technology called MapEdu in the form of an interactive Indian map designed as an assistive technology for children. The primary objective of this study is to explore the feasibility and effectiveness of using marker-based AR within a mobile application to create an interactive and engaging learning experience for children, specifically focusing on the geographical and cultural aspects of India. The proposed mobile application aims to provide an educational platform that seamlessly blends digital text information with real-world geographic maps, enabling children to explore and learn about various states, cities, landmarks, and cultural elements of India. The findings of this paper highlights the benefits of interactive learning through AR, particularly in subjects that involve spatial and geographical concepts. The MapEdu application's effectiveness as an assistive technology for children can pave the way for the integration of AR into educational curricula, fostering a more immersive and engaging learning environment. © 2024 IEEE.

关键词： augmented reality Children map learning marker-based AR mobile app

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Analysis and Prediction of Crimes Against Women 3

Analysis and Prediction of Crimes Against Women

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Julian, Anitha Varshini, K. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The proposed work addresses the escalating concern of crimes against women in contemporary society. This comprehensive endeavor employs a data-driven approach to tackle this critical issue. The primary objectives encompass crime rate analysis, crime rate prediction, and the implementation of an emergency alert system. The crime rate analysis module employs the K-Means clustering algorithm to identify crime-prone areas and high-probability crime occurrences. In the crime rate prediction component, machine learning algorithms, including DBSCAN, Agglomerative Clustering, and Mean Shift, are used to forecast emerging crime trends. The emergency alert system offers women real-time alerts and emergency assistance, fostering a safer environment. The Silhouette Score, an evaluation metric, helps assess the efficiency of these algorithms in capturing inherent data patterns. Proposed work has accessed the performance metrices of K-Means © 2024 IEEE.

关键词： Forecasting

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Ensuring privacy of data in Machine Learning 6

Ensuring privacy of data in Machine Learning

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Reshmi, T. Divakaran, Sumesh College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350381689

Machine Learning is one of the most popular advancements in technology which is being widely used in various domains, including healthcare, avionics, automotive, business, education, etc. A Machine learning approach works by learning the required knowledge from the data to be supplied by a client system. In a typical scenario, a client entrusts a third-party agency to develop a machine learning application and shares the data to the developer to enable the development of a Machine learning application. This is badly affecting the privacy of data as a third-party is getting access to sensitive data from a client system. Therefore, an effective data encoding technique is required to ensure the privacy of sensitive data while enabling a third-party agency to develop a Machine learning application on the encoded data. Existing data masking/encoding techniques such as pseudonymization, anonymization and substitution are badly affecting the machine learning process as the modification they do for masking data is preventing a machine learning approach to learn the required knowledge. Another approach known in the literature is Fully Homomorphic Encryption. But, there is no tool available based on this technique which enables a client system to mask sensitive data before giving it to a third-party Machine Learning developer. We failed to obtain the expected outcome from an off-the-shelf Machine Learning classifier when we tried to classify a benchmark dataset masked using an available implementation of Fully Homomorphic Encryption. Since enough details about the implementation is not available, we could not find and fix the issues which is causing this problem. We propose a technique based on which we implemented a tool which provides an easy-to-use Graphical User Interface enabling the client to mask sensitive data before giving it to a third-party agency for developing a Machine Learning Application. Our tool enables a client to mask sensitive data through few mouse click

关键词： Adversarial machine learning

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Automated Fraud Detection in Online Transaction Using Representation Learning

Automated Fraud Detection in Online Transaction Using Repres...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Farjana, U. Sitaram, M. Sathish Kumar, J. Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

Automated fraud detection in online transactions is a challenging task, as fraudsters are constantly developing new and sophisticated methods. Representation learning is a machine learning technique that can be used to learn patterns in data by transforming the data into a lower-dimensional representation that is more informative and easier to learn from. Two popular representation learning techniques are autoencoders and graph neural networks (GNNs).Auto encoders are a type of neural network that is trained to reconstruct its input. This means that an auto encoder learns to represent the input data in a compressed form, and can then be used to detect anomalies in the data. GNNs are a type of neural network that can be used to learn from data that is represented as graph. A graph is a data structure which in form of nodes and edges. This paper proposes a novel fraud detection system using auto encoders and GNNs. The proposed system is evaluated on a real-world dataset of online transactions, and is shown to achieve state-of-the-art results. We aim to capture complex relationships among accounts, transactions, and entities within the transaction network. The implementation of automated fraud detection using representation learning stands to enhance customer trust, reduce operational costs, and safeguard online transactions from the ever-evolving threat landscape. © 2024 IEEE.

关键词： Metadata

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Road Radar: Integrated Road Monitoring and Maintenance System

Road Radar: Integrated Road Monitoring and Maintenance Syste...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Santosh Kumar, B. Sundaram, Meenakshi Golekar, Malvika Ubana, Prerna Pruthvi Reddy, V. New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372502

Potholes pose a significant threat to road safety in India, contributing to serious traffic accidents. To address this issue, a camera-based system mounted in cars is proposed to detect any road irregularities in real time. The system utilizes You Only Look Once (YOLO) object detection algorithm to identify road irregularities, followed by image processing algorithms to refine detections and improve accuracy. Additionally, Convolutional Neural Networks (CNN) are employed to classify the severity of detected potholes. Real-time notifications are displayed on the dashboard to alert the driver when an obstacle is detected, and a buzzer inside the car sounds when the vehicle approaches an obstruction, in this case, ten meters, prompting the driver to slow down while simultaneously notifying the maintenance team about the pothole's location. A graph will be generated which will include location of the pothole versus area of the pothole. The biggest pothole will be detected, then the maintenance team will try to make this location their priority and will attempt to fix that first in order to prevent major accidents. At the same time, the location that gets the highest number of notifications will be viewed and made a priority. The primary objective is to enhance road safety by alerting drivers to hazards like potholes in real-time, especially prevalent in India. Through YOLO, image processing, and CNN integration, the system aims to decrease accidents, promote cautious driving, and possibly reroute to safer paths, mitigating pothole-related accidents and enhancing overall road safety nationwide. © 2024 IEEE.

关键词： Cameras

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