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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform 18

Maximizing Profit of the Service Providers through Efficient...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Gandhi, Preet Mahanta, Abhishriya Ghose, Manojit Sahu, Aryabartta Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach. © 2024 IEEE.

关键词： Cloud platforms

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MEC-EnergySaver: Unleashing Efficiency Through D2D and Data Compression 16

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Ghosh, Anindita Mukherjee, Poulomi De, Tanmay National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350383119

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption. © 2024 IEEE.

关键词： D2D communication data compression energy efficiency MEC

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Flame Frenzy Fighter

Flame Frenzy Fighter

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Unpane, Nikita Jewani, Gunjan Umarvaishya, Manish Kokatey, Akshay Gupta, Ashutosh Nikhare, Amita Jhulelal Institute of Technology Computer Science and Engineering Department Nagpur India

ISBN: (纸本)9798350348460

Our research project aims to develop and implement a revolutionary fire extinguishing robot carefully adapted for the detection and autonomous extinguishing of fires in enclosed spaces. The lack of immediate human intervention in fire alarm systems often causes significant damage, highlighting the need for automated fire protection solutions. Our robot, equipped with flame detection and control sensors, has the ability to quickly detect fire sources and approach them autonomously with unmatched precision. This remotecontrolled marvel ensures a safe and effective response to fires, eliminating the need for human intervention in dangerous situations. This research represents a significant step forward in improving safety, preventing disasters, and saving lives from home fires. This is an important milestone in the field of autonomous robotics and could revolutionize our approach to residential fire safety and disaster management in private homes as well as commercial establishment. © 2024 IEEE.

关键词： Arduino IOT real time robot Sensors snapshot

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IndiSentiment140: Sentiment Analysis Dataset for Indian Languages with Emphasis on Low-Resource Languages using Machine Translation

IndiSentiment140: Sentiment Analysis Dataset for Indian Lang...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Kumar, Saurabh Singh, Sanasam Ranbir Nandi, Sukumar Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891761148

Sentiment analysis, a fundamental aspect of Natural Language Processing (NLP), involves the classification of emotions, opinions, and attitudes in text data. In the context of India, with its vast linguistic diversity and low-resource languages, the challenge is to support sentiment analysis in numerous Indian languages. This study explores the use of machine translation to bridge this gap. The investigation examines the feasibility of machine translation for creating sentiment analysis datasets in 22 Indian languages. Google Translate, with its extensive language support, is employed for this purpose in translating the Sentiment140 dataset. The study aims to provide insights into the practicality of using machine translation in the context of India’s linguistic diversity for sentiment analysis datasets. Our findings indicate that a dataset generated using Google Translate has the potential to serve as a foundational framework for tackling the low-resource challenges commonly encountered in sentiment analysis for Indian languages. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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Suspicious Activity Monitoring System Using YOLOv5 15

Suspicious Activity Monitoring System Using YOLOv5

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kushwaha, Piyush Murmu, Mahendra Kumar National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350370249

In today's digital age, effective surveillance and anomaly detection systems are crucial. YOLOv5, a cutting-edge real-time object detection model that stands out for its remarkable speed and accuracy, making it ideal for these applications. This paper outlines the essential libraries and dependencies for the project, highlighting the importance of setting up the correct environment. It details the process of importing datasets in the YOLO format and provides insights on effective dataset preparation and curation. The exploratory data analysis (EDA) section covers visualizing sample images with bounding boxes, analyzing class distribution, and assessing image size and resolution for YOLOv5 compatibility. The core discusses applying the YOLOv5 algorithm, including model loading, training, and fine-tuning, along with evaluating model performance. Finally, practical applications of the trained model, such as real-time and batch inference, are explored, demonstrating its utility in detecting suspicious activities for security and surveillance. This paper serves as a comprehensive resource for implementing YOLOv5, ensuring the successful deployment of a robust object detection system. © 2024 IEEE.

关键词： Anomaly Detection computer Vision Deep Learning Exploratory Data Analysis (EDA) Security Systems YOLOv5

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BlasT: Blood Clot Classification Model Using Transfer Learning Based Convolutional Neural Network 16

BlasT: Blood Clot Classification Model Using Transfer Learni...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mishra, Shruti Pal, Sujata Dey, Sweta Indian Institute of Technology Department of Computer Science & Engineering Ropar India

ISBN: (纸本)9798350383119

Blood cells contain a plethora of clotting factors vital for sustaining human life. Blood coagulation manages excessive blood loss from the blood vessels of an injured individual. Blood clots can appear due to various medical reasons in humans, such as platelet aggregation and converting fibrinogen into fibrin. Consequently, blood clots generate multiple disorders and even elevate the risk of death when not promptly diagnosed. Therefore, the prompt identification and treatment of blood clotting are paramount. In this study, we introduced an analytical approach for detecting blood clots. Also, we introduced a blood clot classification model: Blood Clot Classification Model using Transfer Learning and Convolutional Neural Network (BlasT). Our evaluation results demonstrate an impressive accuracy rate of 93.99% for the proposed model: BlasT. The primary objective of this study is to establish an early clot classification model using transfer learning in conjunction with a Convolutional Neural Network (CNN) layer. Our proposed BlasT model can identify blood clots swiftly and accurately. Furthermore, it allows timely notifications to medical professionals. © 2024 IEEE.

关键词： Blood-clot CNN Machine Learning (ML) Red Blood Cells (RBCs) White Blood Cells (WBCs)

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Revolutionizing Transportation Infrastructure and Communication technology through Blockchain and 5G Simulation 2

Revolutionizing Transportation Infrastructure and Communicat...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pallavi, N. Naik, Darshana A. Soumya, C.S. Mahadesh, Jayanth Braganza, Joseph A. Singh, Prakhar Nanda, Rohith Ramaiah Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306415

This paper presents the outcomes of two key projects dedicated to the enhancement of modern transportation infrastructure and communication technology. The first project introduces a web-based application for toll tax collection, with a particular focus on bolstering security and transparency through the implementation of blockchain technology. By leveraging blockchain, this system successfully mitigates fraud and enhances accountability. Simultaneously, our second project utilizes the Open Air Interface for simulating 5G network node *** toll tax collection project yields substantial results by establishing a secure, decentralized platform through blockchain integration. This, in turn, leads to a notable reduction in fraud and a significant improvement in accountability. The system utilizes an immutable ledger and smart contracts to streamline toll collection processes, ultimately enhancing operational efficiency. In tandem, the 5G network simulation project contributes to the advancement of network technology, delivering improvements in efficiency, transparency, and user experience within toll collection systems, while also catalyzing the evolution of state-of-the-art communication infrastructure. © 2024 IEEE.

关键词： 5G Networks 5G OpenAirInterface (OAI) Blockchain technology Cash-Based Transactions Environmental Impacts Fraudulent Activities High Operational Costs Manual Toll Booths Operational Inefficiencies Toll Collection Method Traffic Congestion User Convenience Vehicular Ad-Hoc Networks (VANETs)

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A Novel Video Encryption Scheme Using Chaotic Map Based Simultaneous Permutation-Diffusion

A Novel Video Encryption Scheme Using Chaotic Map Based Simu...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Dhingra, Deepti Dua, Mohit National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350388534

The need for robust video encryption is driven by the increasing demand for secure multimedia transmission and storage. Traditional encryption methods often fail to account for the high data volume and real-time constraints of video content. Chaos-based cryptography have intrinsic features like extreme sensitivity to initial conditions, deterministic, random, topological mixing, unpredictability. This paper presents a novel video encryption scheme using our earlier presented 2D Cosine-Sine chaotic map. The scheme comprises mainly five steps: (i) Retrieve each frame from the video separately and encrypts them one by one. (ii) The frame is converted into grayscale for simplicity. (iii) The Dynamic version of 2D Cosine-Sine map is created. (iv) The map is used to generate chaos matrix and chaotic sequence that is used for permutation and diffusion respectively. (v) We have introduced a simultaneous permutation and diffusion method in which as each pixel is permuted, it is immediately diffused before moving on to the next pixel. It reduces the need for multiple passes over the data, potentially speeding up the encryption process and enhance security. (vi)The grayscale frame is converted into BGR form. The efficiency of the scheme has been tested using various parameters. © 2024 IEEE.

关键词： Chaos theory

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Machine learning based Expert System for Online Admissions 15

Machine learning based Expert System for Online Admissions

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Sasane, Sanika Igade, Aditi Shet, Anish Kandalgaonkar, Vighnesh Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331300579

The web application permits on-line admissions saving the time of geographically scattered college *** permits lowering time in activities, centralized information dealing with and paperless admission with decreased *** improves operational performance and decreases the fee. The college admission device has been advanced so as to automate admission device beginning from the notification to admission *** device used a selection tree primarily based totally category method. Prior to device improvement, six fashions have been created and examined to discover the maximum green version which might later be carried out for improvement of internet-primarily based totally pupil admission screening device. Online Admission screening device is an internet device that may be accessed at some stage in the company and out of doors as nicely with right login furnished. The purpose of this studies paintings is to significantly verify the gathering and processing of pupil admission shape procedure so as to become aware of troubles encountered with inside the device, a good way to layout a brand new device a good way to assist lessen or absolutely remove the diagnosed issues internet. The automatic admission processing device will assist lessen a number of the onerous paintings contain in pupil admission processing, maximized returns for the group offer a pleasant consumer interface users. © Grenze Scientific Society, 2024.

关键词： Expert systems

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Software Fault Localization using CNN-LSTM Model 15

Software Fault Localization using CNN-LSTM Model

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Vishwank Vikram Bisi, Manjubala National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350370249

Software fault localization is a critical task which identify the faulty statements in a program. Traditional fault localization techniques are unable to capture complex relationships within program execution data. In recent years, Convolutional Neural Networks (CNNs) has shown promise in enhancing fault localization performance. In this paper, we propose a CNN-LSTM model for software fault localization. We perform extensive experiments on four programs and observe that proposed CNN-LSTM model consistently outperforms existing methods, achieving higher accuracy and reliability in localizing software faults. © 2024 IEEE.

关键词： CNN LSTM MBFL SBFL

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