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检索条件"机构=Dept. of Computer Science and Engineering with specialization in Blockchain Technology"

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Smart Vehicle Procurement System using blockchain technology 3

Smart Vehicle Procurement System using Blockchain Technology

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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Smart Vehicle Procurement System Using blockchain technology

Smart Vehicle Procurement System Using Blockchain Technology

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Boddu Manoj Kumar G. Rajeswari Kavitha Duraipandian Shiela David Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Chennai Tamil Nadu Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

关键词： Industries Costs Smart contracts blockchains Fraud Security History Reliability Standards Automotive engineering

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

引用

2023 IEEE Conference on engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory Data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory Data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： Data visualization

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using Data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

引用

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

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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Combating Deepfakes: Multi-LSTM and blockchain as Proof of Authenticity for Digital Media

Combating Deepfakes: Multi-LSTM and Blockchain as Proof of A...

引用

2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020

作者： Ki Chan, Christopher Chun Kumar, Vimal Delaney, Steven Gochoo, Munkhjargal Chaoyang University of Technology Dept. of Information Management Taichung Taiwan Ryerson University and Capital Blockchain Inc. Dept. of Computer Science Toronto Canada Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781728170312

Malicious use of deep learning algorithms has allowed the proliferation of high realism fake digital content such as text, images, and videos, to exist on the internet as readily available and accessible consumable content. False information provided through algorithmically modified footage, images, audios, and videos (known as deepfakes), coupled with the virality of social networks, may cause major social unrest. The emergence of misinformation from fabricated digital content suggests the necessity for anti-disinformation methods such as deepfake detection algorithms or immutable metadata in order to verify the validity of digital content. Permissioned blockchain, notably Hyperledger Fabric 2.0, coupled with LSTMs for audio/video/descriptive captioning is a step towards providing a feasible tool for combating deepfake media. Original content would require the original artist attestation of untampered data. The smart contract combines a varied multiple LSTM networks into a process that allows for the tracing and tracking of a digital content's historical provenance. The result is a theoretical framework that enables proof of authenticity (PoA) for digital media using a decentralized blockchain using multiple LSTMs as a deep encoder for creating unique discriminative features;which is then compressed and hashed into a transaction. Our work assumes we trust the video at the point of reception. Our contribution is a decentralized blockchain framework of deep discriminative digital media to combat deepfakes. © 2020 IEEE.

关键词： Smart contract

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The Influence of Kanban Agile Methodology on Software Project Management: A Survey Method 10

The Influence of Kanban Agile Methodology on Software Projec...

引用

10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Munir, Fatima Nafees, Fouzia Mahmood, Jabar Chaudhry, Shehzad Ashraf Javaid, Maryam Batool, Khadija University of Sialkot Dept. of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates International Institute of Science Arts and Technology Dept. of Computer Sciences Gujranwala Pakistan

ISBN: (纸本)9798331532895

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods. © 2024 IEEE.

关键词： Agile DSDM Kanban Quality Enhancement Scrum SPM XP

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

引用

engineering Informatics, IEEE

作者： S. Aarthi Dharmita A Pragathi G S Elakkiya Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India Dept. Computer Science Engineering specialization with Big Data Analytics SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

关键词： Technological innovation Renewable energy sources Data analysis Machine learning algorithms Wind energy Machine learning Feature extraction

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using Data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： M. S. Minu Rohit Suryaa S Jovan Titus J Sharan S Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India Dept. Computer Science Engineering Specialization With Big Data Analytics SRM Institute of Science and Technology Chennai India

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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