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The Role of Blockchain Technology in Supply chain Management for data Security 3

The Role of Blockchain Technology in Supply chain Management...

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3rd IEEE International Conference on Blockchain and distributed Systems Security, ICBdS 2024

作者： Birari, dipika R. Rathod, Vijay U. dandavate, Aarti Shelke, Nilesh Kumar, Ranjit Kulkarni, Masira M. S Army Institute of Technology Department of Information Technology Pune India G H Raisoni College of Engineering and Management Department of Artificial Intelligent and Machine Learning Wagholi Pune India Dhole Patil College of Engineering Department of Computer Engineering Pune India Symbiosis International Deemed University Symbiosis Institute of Technology Department of Computer Science and Engineering Nagpur Campus Pune India Ajeenkya D Y Patil University Department of Computer Engineering Pune India G H Raisoni College of Engineering and Management Department of Cyber Security and Data Science Wagholi Pune India

ISBN: (纸本)9798350354348

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well. © 2024 IEEE.

关键词： Smart contract

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Analysis of Intrusion detection in Cyber Attacks using Machine learning Neural Networks

Analysis of Intrusion Detection in Cyber Attacks using Machi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Amutha, S. Maheswari, G. Uma Nandhini, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India P.S.R Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India

ISBN: (纸本)9798350313987

Three machine learning algorithms-Support Vector Machines (SVM), k-nearest Neighbours (KNN), and Random Forest (RF)-are analyzed in this research study for their potential use in the field of cybersecurity intrusion detection. With the increasing complexity of cyber threats, robust intrusion detection systems are essential to safeguard digital systems. leveraging a comprehensive dataset of network activity, this research assesses the effectiveness of these algorithms in accurately identifying intrusions while minimizing false positives. The results showcase the strengths and limitations of each algorithm in terms of accuracy, speed, and adaptability. The results of this study show that machine learning may greatly improve cyber defenses. The necessity of cyber security has grown as our society has gotten more interconnected, as cyberattacks continue to grow in sophistication and variety. Traditional rule-based intrusion detection systems are often insufficient in the face of evolving threats. This research builds upon the foundation of intrusion detection by applying three machine learning algorithms: SVM, KNN, and RF. These algorithms offer diverse approaches to identify and respond to cyber intrusions. A dataset containing a mixture of normal network traffic and known attack patterns is utilized for training and evaluation. The study aims to assess the accuracy, speed, and adaptability of each algorithm for intrusion detection. The study's results reveal the distinct strengths and limitations of SVM, KNN, and RF in the context of intrusion detection. SVM, known for its accuracy, excelled at correctly classifying network activities, minimizing false positives, and reducing the risk of overlooking intrusions. KNN, leveraging proximity-based classification, demonstrated adaptability and simplicity. RF, with its ensemble approach, showcased efficiency and robustness in handling complex data. Each algorithm displayed varying degrees of adaptability, accuracy, and compu

关键词： Cybersecurity k-Nearest Neighbors Machine learning Random Forest Support Vector Machines

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Advanced development on Real Time Monitoring of Bridges Using Wireless Fidelity 7

Advanced Development on Real Time Monitoring of Bridges Usin...

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

作者： Ferlin deva Shahila, d. Stephen, Valantina Padma Suresh, l. Ashwini, A. Shally, S.P. Anushia, R.M. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India Saveetha School of Engineering Department of EEE Chennai Thandalam India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India DMI College of Engineering Department of ECE Chennai India DMI College of Engineering Department of CSE Chennai India

ISBN: (纸本)9798350372816

Wireless fidelity technology enables communication without the use of wires or connections between two or more people across large distances. Wireless technology encompasses communications that utilize both radio frequencies (RF) and infrared waves. The origin of wireless technology can be traced back to the invention of electromagnetic waves by the German physicist, geophysicist, and engineer, Heinrich Hertz. In the late nineteenth century, the commercial wireless telegraph service was established by the Italian entrepreneur Gugliello Marconi, more than half a century after the invention of the first commercially available wired telegraph in 1832. In the early twentieth century, Marconi was one of the pioneers of radio communications, transmitting radio signals to mobile receivers on ships. Wireless has always been more advanced than wired technology, and it's usually more expensive too, but it's also given us the added benefit of being able to move around and get and send *** this project, we'll be using MATlAB software to design and test the full model. We'll also be looking at wireless bridge health monitoring systems. ZigBee acts as the wireless network for short-range IEEE 802.11 communication between sensors in the bridge. © 2024 IEEE.

关键词： Zigbee

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Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and learning Techniques 5th

Enhancing Weed Recognition in Cotton Fields Through Advanced...

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5th International Conference on Recent Trends in Machine learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, dinesh Prasad Tiwari, Mohit Haque, Mustafizul Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Dr. D. Y. Patil Vidyapeeth’s Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Ghaziabad India

ISBN: (纸本)9789819788606

This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models’ ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Cotton

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Real-Time detection of Rice Bacterial Blight using Remote Sensing and deep learning 3

Real-Time Detection of Rice Bacterial Blight using Remote Se...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rakesh, Nitin Thirumurugan, R. Mohankumar, N. Kasinathan, Prabakaran Prasad, S. N. Sheela Evangelin Srinivasan, C. Maharashtra Nagpur India Adhi College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Symbiosis Institute of Technology Nagpur Campus Pune India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Krishna Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research introduces an innovative method for the real-time identification of bacterial blight in rice crops via remote sensing and deep learning (dl) technologies. Bacterial blight represents a significant threat to rice production, requiring quick detection for efficient control. To address this challenge, remote sensing data from drones and satellites is analyzed to detect visual signs connected to the diseases. A convolutional neural network (CNN) model is used to classify and identify sick rice plants using high-resolution images. The results demonstrate that the model attained an accurate rate of 98.2%, significantly enhancing the speed and precision of illness identification relative to conventional approaches. The combination of advanced dl algorithms with remote sensing technologies enables quick responses and improves sustainable agriculture practices. It illustrates the capability of using technology for crop monitoring, enhancing food security, and optimizing resource management in agriculture. © 2025 IEEE.

关键词： Convolutional neural networks

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Secured dynamic Opportunistic Routing in Ad-hoc Wireless Network 2

Secured Dynamic Opportunistic Routing in Ad-hoc Wireless Net...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Maheswari, S. Mishra, Nilamadhab Shadaksharappa, B. Sivanesan, T.M. Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computer Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

In ad-hoc wireless network, the transmission efficiency decreases due to the restriction of channel interference, packet loss, and less bandwidth. Opportunistic routing (OR) is a new routing method which increases the network's efficiency. Though, most of the traditional OR solutions do not resolve the forwarder selection and routing overhead. To solve these issues, Opportunistic Routing for dynamic Routing (ORdR) with security enhances the OR efficiency. This mechanism uses the mobile node authentication procedure to isolate the malicious nodes. The next transmitter node is selected energetically based on the link strength and the energy to the destination. Based on link quality, this approach discovers the best transmitter from the forwarder list. The proposed ORdR system measures link strength, received signal strength indication, and received packet rate. Experimental results illustrate that the ORdR mechanism provides the best efficiency compared to the baseline mechanisms in terms of delay and improves security. © 2023 IEEE.

关键词： Authentication

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Tuberculosis detection, an effective approach using convolutional neural network

Tuberculosis detection, an effective approach using convolut...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Balaji, l. dhanalakshmi, A. Brindha, J. Pasupulati, Baburao Santhosh Krishna, B.V. Institute of Science and Technology Electronics & Communication Vel Tech Rangarajan Dr Sagunthala R & D TN Chennai India Panimalar Engineering College Electronics and Communication Engineering TN Chennai India Institute of Science and Technology Electronics & Electronics Engineering Vel Tech Rangarajan Dr Sagunthala R & D TN Chennai India New Horizon College of Engineering Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350381887

In most regions of the world, tuberculosis (TB) poses a major health risk and is spread through the air. A majority of diagnostic techniques takes a long time, are not always accurate, and were mostly created in the last century. The most popular technique for early tuberculosis detection in big populations is chest X-rays. The radiologist's experience and ability to interpret images will determine whether or not this procedure is successful. Because convolutional neural networks (CNNs) can learn both medium-level and high-level picture representations, they have become a popular deep learning technique. Several CNN models were employed in this work as Google Net models to categorize chest radiographs into TB-positive and TB-negative groups. A comparison of deep learning methods that can be used to process chest X-rays and identify tuberculosis is presented in this article. A publicly accessible dataset called the Tuberculosis (TB) Chest X ray database is used to gauge the system's performance. © 2024 IEEE.

关键词： X ray radiography

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Adaptive Silo Networks with Cloud Computing and Reinforcement learning for Responsive Grain Storage 7

Adaptive Silo Networks with Cloud Computing and Reinforcemen...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jehan, C. Kumaresh, P.S. Suguna, M. Raja Rosaline, R. Anto Arockia Suguna, M. Murugan, S. Vel Tech Multitech Dr. Rangarajan Dr. Sagunthala Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Avadi India K.L.N.College of Engineering Department of Electronics And Communication Engineering Tamil Nadu Sivagangai India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Department of Computer Science And Engineering Tamil Nadu Salem India Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

This research study presents a novel method for managing grain storage facilities by combining adaptive silo networks, cloud computing, and reinforcement learning (Rl). The suggested system's goal is to improve grain storage efficiency by automatically modifying storage parameters in response to changes in the surrounding environment and real-time data. The adaptive silo network uses cloud computing capabilities to keep track of things like temperature, humidity, and grain quality in real-time. The proposed system is able to learn and adapt to new circumstances due to the use of Rl algorithms. Interacting with environmental elements such as weather patterns, grain kinds, and demand changes, the model learns optimum storage solutions. The proposed approach improves storage efficiency with the use of advanced algorithms, adaptive control mechanisms, and real-time monitoring. Contributing to the long-term viability and financial success of agricultural supply chains, it optimizes grain storage systems to reduce losses and provide responsive management. With cloud computing, scalability and dispersed data processing, analysis, and decision-making are improved. The device enhances storage efficiency and gives advanced warning of impending problems, allowing for preventative maintenance and protection. To increase the responsiveness and efficiency of grain storage facilities, suggest using cloud computing and Rl to create adaptive silo networks. By integrating advanced technology to overcome challenges in grain storage and management it advances the development of smart agriculture practices. © 2024 IEEE.

关键词： Cloud computing

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Innovative Solar Ironing System: Combining Renewable Energy with Advanced deep learning Techniques 4

Innovative Solar Ironing System: Combining Renewable Energy ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, P. Presskila, X. Arogya Krishnan, R. Santhana Rajkumar, G. Vinoth Kumar, V. Vinoth Rekha, G. Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Jp College of Engineering Department of Electronics and Communication Engineering Tenkasi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331540364

This research study introduces an innovative solar-based ironing system that integrates renewable energy with advanced deep-learning techniques to optimize energy consumption in household ironing operations. The system harnesses solar panels to collect solar energy, ensuring efficient ironing during daylight hours. In cases of insufficient solar generation, the system automatically switches to a conventional power supply, guaranteeing uninterrupted functionality. A key feature of this system is incorporating a long Short-Term Memory (lSTM) model, which predicts solar power output based on historic data and real-time climate information sourced from the OpenWeatherMap API. This predictive capability enhances energy management by providing timely insights into expected solar energy availability, allowing users to plan ironing tasks effectively. data preprocessing techniques, including imputation of missing values using the median and normalization, ensure the quality and reliability of the input data for training the lSTM model. The model is assessed using various performance metrics and compared to traditional forecasting methods, including ARIMA, Gated Recurrent Units (GRU), and Feedforward Neural Networks (FNN). The results demonstrate that the lSTM model significantly outperforms these alternatives in predicting solar energy generation. This research contributes to sustainable living by promoting renewable energy usage in everyday tasks and illustrates the potential of combining deep learning with solar technologies for improved energy efficiency in household applications. © 2024 IEEE.

关键词： Clean energy

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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization 15

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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

作者： dhas, John T. Mesia Saikia, Utpal Arun, K.K. Josphine, C.Vimala Kingslin, M.Theodore Padmavathi, N. School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Silapathar College Department of Mathematics Dhemaji Assam Silapathar India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai India R.M.K College of Engineering & Technology Department of Electronics & Communication Engineering Tamilnadu Puduvoyal India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India

ISBN: (纸本)9798350370249

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data. © 2024 IEEE.

关键词： mathematical modeling oil rig welding optimization Residual VGGNet welding parameters

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