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Blockchain Based Digital Forensic Data for Unbreakable Trust and Security

Blockchain Based Digital Forensic Data for Unbreakable Trust...

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2024 international conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Imrankhan, M. Viji, M. Sathishkumar, R. Gnanaprakash, T. Roshan, R. Karthik Saravanan, G. Manakula Vinayagar Institute of Technology Dept. of Cse Puducherry India Sri Manakula Vinayagar Engineering College Dept. of Computer Science and Business Systems Puducherry India

ISBN: (纸本)9798331510022

This can be one of the most magnificent advantages realized from applying blockchain technology in a forensic investigation context, namely, a transformative approach to address challenges embedded within legal and criminal justice systems. For example, smart contracts, being self-executing agreements with the automated inclusion of predefined rules, may even automate and secure the process of tracking evidence, managing chain of custody, and access control. This smart contract will ensure that forensic operations are transparent and of sound integrity. Every transaction and action performed on the blockchain is recorded there, instantaneously bringing down tampering to the lowest possibility and any chance for human mistakes. Regarding decentralized nature and cryptography-based security combined with immutability, blockchain is a technology known to have safeguards over sensitive data. Once recorded on the blockchain, the information thereupon becomes completely un-changeable, assuring the reliability and authenticity of evidence. This will be especially important in the management of an unforgeable chain of custody and in ensuring that forensic evidence remains unchanged and can be relied on with integrity throughout the court process.[6] Critical data can then be maintained tamper-proof for management and storage by developing Ethereum-based smart contracts on Remix IDE using Solidity.[15]Each transaction will be time-stamped and safely logged on the Ethereum blockchain, thus developing an unchallenged ledger that makes any investigation that much more trustworthy. In a nutshell, the application of blockchain in forensic activities builds greater control and efficiencies, reduces possible errors and conflicts, and provides transparency, integrity, and an unchangeable, permanent record for forensic processes. The invention thereby significantly upgrades the general integrity and confidence in legal and criminal justice inquiries. © 2024 IEEE.

关键词： Ethereum

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Optimizing Autonomous Area Search: A Synergistic Approach Using Voronoi Graphs and the Boustrophedon Algorithm

Optimizing Autonomous Area Search: A Synergistic Approach Us...

引用

international conference on computer and automation engineering, ICCAE

作者： Shashi Shekhar Ankita Kumari Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Efficient area search is critical in autonomous systems for applications like agricultural automation and disaster response. This paper introduces a novel method combining Voronoi graph creation with the Boustrophedon algorithm to optimize path planning and coverage. Voronoi graphs segment the search area into manageable regions, while the Boustrophedon algorithm ensures thorough coverage with a sweeping pattern. Our integrated approach, evaluated through simulations and experiments, shows significant improvements in coverage efficiency and path optimization. This research advances autonomous navigation and offers a robust solution for efficient area search, with future work focusing on dynamic environments and real-time applications.

关键词： automation Heuristic algorithms Disasters Surveillance Space missions Path planning Robustness Real-time systems Partitioning algorithms Optimization

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Blockchain-Based Lightweight Authentication and Key Exchange Protocol For Unmanned Aerial Vehicle

Blockchain-Based Lightweight Authentication and Key Exchange...

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Kumar, Pramod Mohanraj, B. Munuswamy, E. Soni, Mukesh Maranan, Ramya Byeon, Haewon Ganga Institute of Technology and Management KablanaHaryana Jhaajji124104 India Sona College of Technology Department of Information Technology SALEM India Computer Science and Engineering Kings Engineering College Sriperumbudur Kanchipuram India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Inje University Department of Digital Anti-Aging Healthcare Gimhae50834 Korea Republic of

ISBN: (纸本)9798350317060

The proposed authentication and key exchange protocol for unmanned aerial vehicles (UAVs)are based on a combination of elliptic curve cryptography, symmetric cryptography, and blockchain technology. The protocol, called Blockchain-based Lightweight Authentication and Key exchange Protocol, aims to provide secure and efficient communication between the UAV and the ground station while minimizing computational costs and reducing the risk of cyber attacks. The elliptic curve cryptography is used for key exchange, symmetric cryptography is used for message encryption and decryption, and blockchain technology is used to store and distribute the public keys. The proposed protocol is evaluated through simulations and equated with current protocols. The outcomes show that the protocol has lower computational overhead, better security, and is highly resistant to attacks due to the use of blockchain technology. This makes the proposed protocol a suitable solution for secure communication in UAV applications. The paper builds on previous work such as DroneSecand DroneSec - lite, which is also based on elliptic curve cryptography and symmetric cryptography, but with a focus on lightweight authentication and key exchange protocols for UAV systems with varying computational capabilities. © 2023 IEEE.

关键词： Authentication

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An Improved Multiscale Faster R-CNN Based Lung Cancer Detection System

An Improved Multiscale Faster R-CNN Based Lung Cancer Detect...

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2024 international conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Arikaran, N. Sathishkumar, R. Poojitha, B. Dharanya, S. Kanchana, M. Mohamed Appa, M.A.Y Peer Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510022

Lung cancer is a leading cause of cancer-related deaths, making early and accurate detection crucial. This project develops an automated detection system using Convolutional Neural Networks (CNN) to improve the identification of malignant nodules in lung CT scans. The process involves collecting a large dataset from public repositories, followed by preprocessing steps like normalization and augmentation to enhance input quality. The CNN architecture is designed to capture complex features of lung nodules, with training on expertly annotated data. Techniques such as dropout and batch normalization are used to prevent overfitting. The model's performance is evaluated using metrics like accuracy and ROC curve analysis, showing significant improvement over traditional methods. The findings suggest that CNNs offer a promising approach to early and accurate lung cancer detection, with potential for clinical application and future integration with other diagnostic tools. At the stage of diagnosis our major aim is to reach the higher level accuracy and efficiency. The improved model, based on lung cancer increases the performance, by combining the properties of various network structures. The proposed model achieved 96.2 % ACC, 94.7% Spec, 93.5% Recall, 98% Prec, and 98.5 % F1-Score. In addition to helping medical professionals with patient care and monitoring, the enhanced model may more accurately detect lung cancer, lower the possibility of incorrect and delayed diagnoses resulting from physician personal reasons, and enhance the overall evaluation and treatment process. © 2024 IEEE.

关键词： Lung cancer

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Web Scraping and Job Recommender System 2

Web Scraping and Job Recommender System

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2nd international conference on Informatics, ICI 2023

作者： Sinha, Koustubh Sharma, Priyansh Sharma, Harshit Asawa, Krishna Jaypee Institute of Information Technology Computer Science Engineering Delhi India Jaypee Institute of Information Technology Computer Science Engineering Noida India

ISBN: (纸本)9798350343830

Web scraping is the process of autonomously obtaining data from webpages using computer software or tools. It has grown in popularity in recent years because of the volume of data available on the internet and the necessity for organizations and individuals to analyze and utilize this data to get the necessary and important information out of it for further analysis and application. Web scraping, together with automation tools, can create wonders. In this paper, we have focused on the latest web scraping techniques and their applications. One of the ideas is to assist those looking for employment on different firm job portals, which is discussed in this paper. Job tracking and scraping collect data from multiple platforms and are web-based tools that help job searchers locate the ideal job for them based on their categories and organize their job search more effectively. People are always on the lookout for a place where they can get their desired information so that they can easily apply for a job. After the collection of data based on user profile attributes, we recommend their success rate based on previous records and an appropriate machine learning model, and we also suggest what skills they can improve to land that job. Web scraping tools allow us to extract the necessary data from different websites in real time and present it to the user. We have discussed various ways to implement scraping and propose a job algorithm to process the extracted data and make it into meaningful information to help users land the best job. © 2023 IEEE.

关键词： Recommender systems

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Cognitive Cardiovascular Disease Prediction Using ECG-Crown Auto Encoder Neural Network for ECG Time Series Signals 8

Cognitive Cardiovascular Disease Prediction Using ECG-Crown ...

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8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Shah, Ameet Singh, Dhanpratap Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350391770

Cognitive computing research in cardiovascular disease prediction is needed in the post-COVID-19 era. Recently, post-vaccination anomalies have been declared at certain levels throughout the world, and in rare cases, the vaccine causes Thrombosis Thrombocytopenia Syndrome, which causes blood clot formation. The core threat is that if such clots travel to heart arteries, there is a possibility of sudden cardiac arrest, cardiac arrhythmia diseases, and other heart diseases. The heart's function can identify such threats, and the primary tool for cardiac function tests is an electrocardiogram (ECG). So, analyzing the heart rate and heartbeat status more accurately is necessary. The manual analysis for cardiac arrhythmia using ECG requires manual QRS-complex identification, which may mislead the precise disease prediction. Hence, the proposed research focuses on fast decision by enhancing the cognitive computing accuracy of ECG signals through the proposed ECG-Crown autoencoder with an LSTM sequential model. The ECG-Crown auto-encoder neural network gives fast classification results as the downsampling is executed concurrently, making the arrhythmia prediction possible even for a single heartbeat by ECG waveform R-R peak analysis. Hence, the proposed model performance is very impressive compared to existing techniques. The proposed system is tested with ECG time series signal data, and the results are very promising, with 99.7% accuracy. The model loss recorded minimum, which lowers the classification error and subsequently helps to predict a cardiovascular disease very accurately. © 2024 IEEE.

关键词： Vaccines

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Understanding URDF: A Survey Based on User Experience 19

Understanding URDF: A Survey Based on User Experience

引用

19th IEEE international conference on automation Science and engineering, CASE 2023

作者： Tola, Daniella Corke, Peter Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark Centre for Robotics Qut Brisbane Australia

ISBN: (纸本)9798350320695

With the increasing complexity of robot systems, it is necessary to simulate them before deployment. To do this, a model of the robot's kinematics or dynamics is required. One of the most commonly used formats for modeling robots is the Unified Robot Description Format (URDF). The goal of this article is to understand how URDF is currently used, what challenges people face when working with it, and how the community sees the future of URDF. The outcome can potentially be used to guide future research. This article presents the results from a survey based on 510 anonymous responses from robotic developers of different backgrounds and levels of experience. We find that 96.8% of the participants have simulated robots before, and of them 95.5% have used URDF. We identify a number of challenges that complicate the use of URDF, such as the inability to model parallel linkages and closed-chain systems, no real standard, lack of documentation, and a limited number of dynamic parameters to model the robot. Future perspectives for URDF are also determined, where 53.5% believe URDF will be more commonly used in the future, 12.2% believe other standards or tools will make URDF obsolete, and 34.4% are not sure what the future of URDF will be. Most participants agree there is a need for better tools to ensure URDF's future use. © 2023 IEEE.

关键词： Robots

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An Advanced Strategy for Efficient Agricultural Production using Machine Learning 2

An Advanced Strategy for Efficient Agricultural Production u...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Athinarayanan, S. Selvi, R. Rajakumari, K. Arunadevi, R. Robinson Joel, M. Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Department of Information Technology Dr MGR Educational and Research Institute Chennai India Chennai India Department of Computer Science and Engineering Prince Shri Venkateshwara padmavathy Engineering College Tamil Nadu Chennai India Department of Information Technology Kings Engineering College Chennai India

ISBN: (纸本)9798350340235

Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solutions, it is the most effective method for solving problems. Crop yield prediction involves estimating the crop yield based on historical data and a variety of other factors, including the climate, sometimes the environment, the water source, and warmth. This study analyses and discusses the use of the linear regression technique for evaluating agricultural production based on data from the earlier study. This study uses geographic and meteorological data to anticipate winter wheat production. It is motivated by this data science issue. The project's goal is to find a solution to the cost loss issue. Actual agricultural data is used to build the models, which are then evaluated on samples. Using the vegetable yield forecasting model, end users (agriculturalist) may foresee crop production as they plant crops on agricultural land. The Linear Regression Algorithm method is used to forecast results with accuracy. The decision-making model's creation will be aided by the availability of a sizable dataset. © 2023 IEEE.

关键词： agriculturalist Decision-Making Linear Regression Machine Learning meteorological data

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Cylindrical CGH Wavefront Calibration Method based on Transmission Holography

Cylindrical CGH Wavefront Calibration Method based on Transm...

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2022 international conference on Optical and Photonic engineering, icOPEN 2022

作者： Ying, Zhijun Lin, Xingyu Chai, Xuanyu Yu, Yingjie School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9781510661905

computer-generated holograms(CGH) can generate the ideal wavefront required for any aspheric surface to be measured for phase compensation. It is usually used to create a cylindrical wavefront to replace the null compensator in cylindrical interferometry. Compared with the null compensator, the error components introduced by CGH are more complex, including substrate error, etching error, coding error, etc. The error calibration of the wavefront generated by the CGH can effectively improve the accuracy of the measurement results. Since CGH will generate multi-order diffraction wavefronts, there is frequency aliasing in multiple groups of fringes, so its wavefront cannot be measured directly. A physical model of the multi-order diffracted wavefronts emitted by CGH is established, and the interference of multiple sets of wavefronts is simulated numerically. To solve the frequency aliasing problem, a tilted carrier beam is introduced by using transmission holography to separate the frequency of the target wavefront from those of the other orders. Thus, the frequency of the target order is extracted, and the cylindrical wavefront is recovered according to its frequency. In this paper, the distribution of experimental fringes is consistent with the simulated fringes, which verifies the accuracy of the physical model. The simulation results show that the proposed method can theoretically separate and recover the cylindrical wavefront well, proving the method's theoretical feasibility. © 2022 SPIE.

关键词： Wavefronts

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Fecal-based Health Status Prediction in Poultry Birds: A Blended Approach using Handcrafted and Deep Features 2

Fecal-based Health Status Prediction in Poultry Birds: A Ble...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Agarwal, Ambuj Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350340235

The well-being of animals and the profitability of poultry farming are both directly affected by the condition of the birds raised for food. Assessing the health of chickens has traditionally included a lot of physical effort and time-consuming procedures. In this study, a novel method is proposed for determining the health of chickens using fecal samples. Extraction of deep features from Convolutional Neural Networks (CNNs) is combined with the strengths of handcrafted features such as textual feature extraction and the Bag-of-Visual-Word (BoVW) proposed technique. The proposed hybrid model is meant to leverage the complementing traits of handmade and deep features to boost the accuracy of health status prediction. The proposed approach intends to provide a strong and efficient answer for poultry health evaluation by integrating information retrieved from the texture and visual content of fecal samples. Extensive tests were run, and hybrid model was compared to well-known deep architectures like Inception, ResNet50, and EfficientNet, to analyze the efficiency of the proposed model. The findings show that the proposed approach is superior to the others, with a remarkable 96 percent accuracy. This outstanding performance demonstrates the promise of proposed blended approach to greatly enhance the precision and efficiency of health status prediction in poultry birds, making it a useful resource for the poultry industry and animal health specialists. © 2023 IEEE.

关键词： Animal welfare Bag-of-Visual-Word (BoVW) Deep features Fecal analysis Handcrafted features Health status prediction Hybrid model Poultry farming Poultry health Textural feature extraction

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