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Elevating Quantum-Enhanced Security: Pioneering Advances for Cloud computing Environments 1

Elevating Quantum-Enhanced Security: Pioneering Advances for...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Swetha, Devulapally Mohiddin, Shaik Khaja Koneru Lakshmaiah Education Foundation Department of Cse Vaddeswaram Guntur India

ISBN: (纸本)9798331529246

Robust security is vital in cloud computing due to the proliferation of sensitive data. This paper presents an innovative quantum-based security framework to address emerging threats. Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) enhances data transmission security by establishing unhackable channels through quantum entanglement, ensuring data integrity. Quantum Homomorphic Encryption (QHE) enables secure computations on encrypted data, ensuring confidentiality during storage. The Quantum Secure Storage and Management Communication (QSSMC) protocol ensures end-to-end privacy and prevents unauthorized access throughout the cloud data lifecycle. Additionally, Quantum Random Number Generation (QRNG) leverages quantum uncertainty to generate truly random numbers, mitigating the risks of pseudo-randomness and strengthening cryptographic applications. By integrating MDI-QKD, QHE, QSSMC, and QRNG, this framework provides robust security, confidentiality, and data integrity. Implemented in Python with a scalability rate of 95%, it offers a comprehensive solution for modern cloud computing challenges. © 2025 ieee.

关键词： data integrity

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Blockchain-Enhanced data Integrity and Authentication in WSN-IoT systems for Dynamic Spectrum Access 3

Blockchain-Enhanced Data Integrity and Authentication in WSN...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Tuteja, Gaurav Rani, Shalli Sharma, Ankita Nasra, Parul Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798331515911

Wireless Sensor Networks (WSN) that work hand in hand with IoT are crucial for contemporary smart city solutions. However, as these are dynamic and decentralized, they pose additional issues related to network data and authentication. Therefore, the deep analysis of blockchain technologies in WSN-IoT to improve the data integrity and authentication of the systems is proposed in this paper. Based on the consensus mechanisms and the tamper-proof nature of the blockchain ledger, the proposed method is effective in managing the cognitive radio spectrum access. This paper also seeks to discuss previous methods, their inefficiencies, and potential enhancement through the application of blockchain based on various performance metrics. © 2025 ieee.

关键词： data integrity

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Research on Smart Leisure Venue Management systems Driven by the Internet of Things 1

Research on Smart Leisure Venue Management Systems Driven by...

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1st international conference on intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhang, Lu Institute of Physical Education South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9781643685779

The rapid development of Internet of Things (IoT) technology has provided new opportunities for constructing smart leisure venue management systems. This paper proposes an IoT-based intelligent leisure venue management system, employing a layered architectural design that includes data acquisition, data analysis, intelligent decision-making, and application service layers. The data acquisition layer collects environmental and user behavior data through distributed sensor networks and RFID technology. The data analysis layer utilizes big data processing techniques to achieve real-time data fusion and cleansing. The intelligent decision-making layer optimizes venue operations using reinforcement learning algorithms, while the application service layer offers personalized services and visual interfaces to users and managers. Experimental results demonstrate that the system excels in data acquisition accuracy, real-time processing capability, visitor flow prediction accuracy, and energy consumption optimization, effectively enhancing venue operational efficiency and user satisfaction. The conclusion highlights that the system has room for improvement in handling peak period visitor statistics. Future work will focus on optimizing algorithms and enhancing system scalability to further improve overall performance. © 2025 The Authors.

关键词： data collection

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Precision Calibration of Drone Camera Intrinsic Parameters using YOLOv5 Algorithm 1

Precision Calibration of Drone Camera Intrinsic Parameters u...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Zhou, Guangyong Yue, Chonglun Zhang, Guangzhen Guangdong Construction Polytechnic School of Civil Engineering Guangzhou China Guangdong Polytechnic Normal University School of Innovation and Entrepreneurship Guangzhou China

ISBN: (纸本)9798331529246

This study developed a drone camera intrinsic calibration system using YOLOv5 to address challenges such as motion blur, vibration, and illumination changes. The system consists of three modules: data acquisition, data processing, and calibration. In the data acquisition module, a high-resolution camera captures images in real time. The YOLOv5 algorithm processes these images to quickly detect and locate calibration plate feature points, while the CIoU loss function improves detection accuracy. The calibration module then computes the camera's intrinsic parameters. Experimental results show a reprojection error of 0.85 pixels and a processing time of just 35 milliseconds, outperforming traditional methods. This system offers significant potential for applications in precision agriculture, environmental monitoring, and other drone-based fields. © 2025 ieee.

关键词： data acquisition

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AK-Medoids-based Framework for Traffic Monitoring in Low Illumination Conditions 3

AK-Medoids-based Framework for Traffic Monitoring in Low Ill...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ganesh Ram, R. Joshua Samuel, P. Vigneshwaran, V. Rajagopal, S. National Engineering College Kovilpatti India

ISBN: (纸本)9798331527549

A novel unsupervised framework employs YOLOv4 (You Only Look Once, Version 4) to enable real-time object detection on roadways, significantly enhancing intelligent traffic monitoring. The system efficiently identifies vehicles, pedestrians, and other road objects in various challenging conditions, such as low-light environments, varying weather, and high-traffic density. By adopting an unsupervised approach, the framework eliminates the need for large labeled datasets, ensuring scalable deployment across diverse road networks. With integrated difference image analysis and K-medoids clustering, the framework boosts detection accuracy, achieving an impressive 92% detection accuracy—representing a 7% improvement over YOLOv4 and a 12% improvement over YOLOv3. Difference images highlight significant frame-to-frame changes, essential in low-illumination scenarios, while K-medoids clustering refines object grouping and reduces detection noise. These techniques complement YOLOv4’s object detection capabilities, providing a robust solution for real-time monitoring. With edge computing for low-latency performance, the system is well-suited for large-scale deployment in intelligent Transportation systems. By combining unsupervised methods with advanced object detection, this framework delivers a scalable, efficient, and adaptive solution for continuous traffic monitoring, contributing to improved road safety. © 2025 ieee.

关键词： intelligent systems

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Optimized data Processing for Hard Drive Failure Prediction 5

Optimized Data Processing for Hard Drive Failure Prediction

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5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Sivakumar, M. Ragavendra, R. Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798331522100

With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research paper introduces a data analysis and pre-processing approach for hard drive failure prediction based on machine learning techniques. A large dataset encompassing hard drive operational data, metadata, and error rates was collected. Extensive pre-processing was performed, including data cleaning and transformation. Multiple Machine Learning algorithms, including random forests, were employed to train predictive models for hard drive failure. The results demonstrate the effectiveness of our approach in achieving high accuracy for hard drive failure prediction, highlighting the significance of our pre-processing methods. Additionally, this paper compares existing machine learning algorithms and explores the role of big data in enhancing hard drive failure prediction. The utilization of big data and advanced data handling techniques has led to improved accuracy and predictive capabilities, making it an indispensable factor in this field. © 2025 ieee.

关键词： data curation

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An intelligent Disaster Management System: Integrating Technology for Effective Response and Recovery

An Intelligent Disaster Management System: Integrating Techn...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Pandey, Nandini Rani, Priya Kundu, Sumana Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798331523893

This proposed Disaster Management System (DMS) is an advanced application aimed to improve disaster preparedness, response, and recovery efforts through real-time data integration, predictive analytics, and efficient communication. It leverages Artificial Intelligence to predict disasters like earthquakes and floods, providing early alerts and resource management to minimize impact. Key features include role-based access, geolocation tracking, incident reporting, and multilingual support. DMS facilitates coordination between government agencies, NGOs, and volunteers, ensuring rapid response and effective resource allocation. With future integration of IoT and blockchain technologies, the system aims to enhance transparency, optimize resource distribution, and support international disaster management efforts. By integrating real-time data, predictive analytics, and communication tools, this system supports efficient coordination during emergencies. © 2025 ieee.

关键词： Resource allocation

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Empowering Energy systems: Exploring the Intersection of Artificial Intelligence and Predictive Maintenance 6

Empowering Energy Systems: Exploring the Intersection of Art...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Natarajan, Karthika Tekkali, Chandana Gouri Radhika, Vankayalapati School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Raghu Engineering College Andhra Pradesh Visakhaptnam India and AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

This article aims to improving and refining energy processes by applying diverse technologies, strategies, or methodologies of Artificial Intelligence (AI). AI plays an essential role in transforming and optimizing energy systems across various sectors. This article provides how advanced techniques in AI to analyze data, predict necessary issues, and enact proactive maintenance protocols, ultimately contributing to a more resilient and intelligent energy infrastructure. We have used various Machine Learning (ML) models to optimize energy consumption by learning from benchmark data i.e., energy consumption of 2020, and adjusting system parameters accordingly, leading to improved efficiency. This article contributed to sustainability, cost-effectiveness, and improved reliability in the generation, distribution, and consumption of energy and also helps in proactive maintenance to minimizing downtime, and improving overall system reliability. By harnessing the power of AI technologies, industries can proactively address challenges and optimize energy resources to meet the growing demands of the future. © 2025 ieee.

关键词： Predictive maintenance

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Real-Time Object Detection for an intelligent Retail Checkout System 3

Real-Time Object Detection for an Intelligent Retail Checkou...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Mugada, Swathi Patil, Prachi Bhausaheb Preethi, V.J. Deeksha, K. M.S.Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India

ISBN: (纸本)9798331515911

Customers are made to wait for extended hours in busy shopping settings as traditional limits of barcode and RFID-based billing systems have a predisposition towards errors and inefficiencies in scanning untagged or weighted items. The methods that are in use today can't keep up with the demand and lead to queues at check-out counters causing customer discontentment. This paper presents an idea of a novel Smart Billing Counter, based on Object Detection using advanced deep learning, YOLOv8, and OCR technologies for smooth billing processes. It captures the image of items placed at the counter, identifies what it is with minimal error, extracts necessary information from labels concerning weighted items, and generates a bill automatically. The unique capability of this solution is to recognize identical items in one frame with total cost by quantity to optimize checkout times and minimize the possibility of errors or fraud. At an inference speed of 37.2ms per image with 93.6% accuracy, this significantly enhances operational efficiency and ensures that grocery retailers provide their customers with smooth, error-free, and swift checkout processes. This innovation is likely, therefore, to bring about a transformation in retail billing systems. It will enhance customer satisfaction and operations efficiency quite highly. © 2025 ieee.

关键词： Sales

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Internet of Things Cloud-based System for Continuous Heart Disease Management and Diagnosis using intelligent Neuro-Fuzzy systems and Improved Imperialist Competitive Algorithm 3

Internet of Things Cloud-based System for Continuous Heart D...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Shalini, K. Rani, M. Pushpa Mother Teresa Women's Tamilnadu Kodaikanal India Mother Teresa Women's University Department of Computer Science Kodaikanal India

ISBN: (纸本)9798331509675

This research aims to develop an Internet of Things (IoT) cloud-based system that integrates intelligent neuro-fuzzy systems and the IICA for continuous health monitoring and timely medical interventions. The objective is to provide personalized care for elderly patients and improve the accuracy and efficiency of heart disease diagnosis. The research problem addresses the limitations of traditional healthcare systems in managing heart disease. Many systems cannot provide continuous monitoring and timely medical responses. This gap leads to ineffective management of heart disease, especially among the elderly. Existing systems often fail to optimize feature selection and parameter tuning, which are crucial for accurate diagnosis. Manual methods for these tasks result in suboptimal performance and reduce the reliability of diagnosis. The precision of the proposed system can be enhanced by improving data quality, optimizing the neuro-fuzzy system, and refining the IICA. The proposed method integrates several advanced technologies, including IoT devices, cloud computing, intelligent neuro-fuzzy systems, and IICA. IoT devices collect real-time health data from patients, which is transmitted to the cloud for storage and processing. The intelligent neuro-fuzzy system analyzes the health data using neural networks and fuzzy logic. IICA optimizes the system's parameters to enhance accuracy and reliability. This integrated approach ensures continuous health monitoring and quick response to health changes. The results show that the proposed system significantly improves the accuracy of heart disease diagnosis. The system achieves an accuracy of 97.0%, precision of 96.2%, and recall of 96.8%. These metrics demonstrate the effectiveness of the system in providing reliable health monitoring and accurate diagnosis. © 2025 ieee.

关键词： Electronic health record

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