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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Siva, K.P. Shri Suriya, P. Praveen, R. Nimesh Rajan, D. Santhosh Kumar, G. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The goal of this project is to create a web-based application for smart farming that makes use of IoT technology to enable real-time monitoring, data analysis, and remote control for improving crop productivity. The solution connects numerous IoT sensors, offers data visualization, applies machine learning for predictive analytics, and assures data security. A sustainable and fruitful future for agriculture is made possible by the application, which equips farmers with useful knowledge. The MQTT protocol collects the data from a variety of IoT sensors, including temperature, humidity, soil moisture, and weather sensors. Using the HTTPS and TLS protocols, data transfer and communication are done using a security-focused approach. An efficient alert and notification system is built up utilizing Twilio for SMS and email services to inform farmers of important occurrences. The project's accomplishments demonstrate the value of incorporating contemporary technology into agriculture and pave the way for more research and development in the area of smart farming. Future improvements may involve extending the selection of IoT sensors that are supported, including drone technology for aerial monitoring, and working with agricultural specialists to adapt the program for various crop kinds and geographical locations. © 2024 IEEE.

关键词： Smart agriculture

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Enhancing URL Security and Access Control Using Hash-Based Shortening Algorithm 2

Enhancing URL Security and Access Control Using Hash-Based S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Haripriya, K. Preethi, C. Hameed, S. Shahul Pavalarajan, S. Shushmmitha, T. Haripriya, V. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331534950

Shortening URLs has become an integral part of modern digital communication, as it simplifies long and complex internet addresses, making them more user-friendly. However, conventional URL shortening methods often lack adequate security provisions, leaving shortened links vulnerable to various risks. This project aims to develop a secure URL-shortening service by incorporating a hash-based access control mechanism. By using a hash-based approach, we can significantly enhance the security of shortened URLs. In this method, a unique hash value is generated for each URL, which serves as a link to the shortened URL. The generated hash is highly resistant to decryption, making it difficult for unauthorized users to retrieve the original URL, thereby ensuring that only the intended recipient can access the link. The goal of this project is to develop a protected and secure URL-shortening service that utilizes a hash-based access control mechanism. By generating a unique hash for each shortened URL, the system will enhance both security and user privacy. Furthermore, the implementation of granular access control will be a key factor in maintaining the system's overall security. SHA-256 can be utilized to generate unique, unpredictable shortened URLs by creating secure hash values. Integrating OAuth2 and JWT provides robust access control, enabling seamless user authentication and authorization across multiple systems. Additionally, AES encryption ensures the protection of sensitive data by offering strong encryption, safeguarding both the original URL and user information. © 2024 IEEE.

关键词： Cryptography

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Fitness Certificate Validation Using Automatic Number Plate Recognition 10

Fitness Certificate Validation Using Automatic Number Plate ...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Ramya, D. Sakthi Maheswari, S. Balaji, S. Shri Kishore, J.C. Deepanchakaravarthy, K. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As traffic congestion escalates, the need for precise and prompt vehicle regulation becomes increasingly critical. Ensuring vehicles comply with fitness certificate requirements is pivotal for road safety and legal conformity. However, traditional manual methods of verifying fitness certificates are prone to errors and time-consuming. This paper introduces an innovative solution: an Automated Number Plate Recognition (ANPR) system seamlessly integrated with a backend database for efficient and reliable verification of vehicle fitness certificates. Our proposed ANPR system utilizes computer vision to capture and analyze number plates, matching them with fitness certificate status stored in the database. This technology not only expedites the verification process but also significantly enhances accuracy compared to manual approaches. By merging ANPR technology with fitness certificate databases, this project has the potential to modernize vehicle regulation practices. This paper offers a detailed exploration of the system's design and functionality, showcasing its ability to revolutionize regulatory procedures and promote road safety. The project places a strong emphasis on data security, ensuring the safe storage and retrieval of sensitive vehicle information. Through this project, we aim to contribute to discussions surrounding intelligent transportation systems and their role in optimizing regulatory processes. By reducing errors, minimizing manual intervention, and enhancing overall efficiency, the ANPR-based approach offers a path towards safer roads and more streamlined regulation. This abstract provides an overview of the methodology, system architecture, implementation details, results, and conclusions, offering a comprehensive perspective on the proposed solution's effectiveness in automating fitness certificate verification. © 2024 IEEE.

关键词： Traffic congestion

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Justrent (A House Rental Management System) 10

Justrent (A House Rental Management System)

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Cholaraja, K. Vignesh, M. Srinidhi, B. Raghu Muthu Vishal, S. Rishin Nethaji, S. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

The new digital platform called the House Rental Management System is revolutionizing the management of rental properties. It provides a quick and convenient alternative for landlords and tenants to manage a variety of rental property issues. Tenant administration, tracking of rental listings and availability, automatic rent collection, handling of maintenance requests, financial tracking and reporting, and secure document management are some of the key features. This approach improves communication between landlords and tenants, streamlines procedures, and decreases paperwork. It guarantees timely rent collection, prompt maintenance response, and precise upkeep of financial records. The House Rental Management System is a useful tool for streamlining and improving the experience of managing rental properties, benefiting both property owners and tenants with its user-friendly design and robust features The House Rental Management System offers benefits that go beyond simple convenience. It encourages responsibility and openness in property management. Through thorough financial reports, property owners may easily track rental revenue, expenses, and property performance, enabling well-informed decision-making. This system's transparency benefits tenants as well because it makes it simple for them to view lease agreements, payment histories, and maintenance records, assuring a fair and reliable rental experience. In the end, the House Rental Management System is a potent instrument that not only makes managing rental properties easier on a daily basis, but also promotes efficiency, trust, and financial stability for both landlords and tenants. It establishes a new benchmark for contemporary, trouble-free rental property administration. © 2024 IEEE.

关键词： Efficiency

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Reach - A Chat Application 10

Reach - A Chat Application

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Rajarajeswari, P.L. Gowshick, H. Sasi Prakash, M.K. Anuvidh, M. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

In an era dominated by the ubiquity of digital communication, Reach emerges as a pioneering mobile-first chat application, engineered to facilitate seamless in-person messaging and empower users for real-time connections. Developed on the foundation of JavaScript, React Native, Peer JS, Firebase, and ***, Reach revolutionizes the messaging experience by seamlessly integrating critical features like text messaging, voice and video calling, and group chats accommodating up to 100 users. Reach is designed to transcend the limitations of conventional messaging applications by enabling users to send messages in-person, fostering richer interpersonal connections. This innovative platform leverages the familiarity of mobile devices while providing a robust and user-friendly interface, ensuring a superior user experience. Reach's distinguishing feature lies in its capacity to break free from the digital shackles that encase modern communication. By facilitating in-person messaging, it nourishes profound and meaningful connections between users. All this is presented through an intuitive, user- friendly interface that complements the mobile-first ethos, ensuring a superlative user experience. In a landscape crowded with messaging applications, Reach emerges as a trailblazer, bridging the gap between the virtual and the tangible, ushering in a new era of communication that places the user's need for authentic and real-time connection at the forefront. © 2024 IEEE.

关键词： Smart city

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Forecasting Power Prices for Cloud Computing using an Enhanced Machine Learning 15

Forecasting Power Prices for Cloud Computing using an Enhanc...

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

作者： Nithisha, C. Tejaswini, Chillara Pavana Samyuktha, Busireddy Harika, Banda Chatrapathi, Gantala Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

The use of several machine learning regression models for the prediction of power costs is explored in this work. The following models are taken into consideration: Gradient Boosting, Adaboosting, Lgbmregressor, Cataboost regressor, Random Forest Regressor, XGBoost Regressor, Support Vector Regressor, and Stacking Regressor. Historical power pricing data with variables like time, demand, and weather make up the training and assessment dataset. A portion of the dataset is used to train each model, which is then adjusted to maximize prediction performance. To evaluate each model's precision, resilience, and computational effectiveness, a comparative study is done. Insights into the applicability of various regression models for the forecast of power prices are provided by the study's findings, which may help decision-makers optimize the planning and management of energy resources. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain systems

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computer Modeling in engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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An Insight of Deep Neural Networks Based on Demand Forecasting in Using: Ann Algorithm 10

An Insight of Deep Neural Networks Based on Demand Forecasti...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Ananthi, K. Sownthariya, M. Ranjana, E. Moulieshwaran, K. Babu, B. Santhosh Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

As the development of the smart grid is increasing, accurate electric demand forecasting is becoming more important for power systems, because it plays a vital role in improving the performance of power companies in terms of lower operating costs and reliable operation. This study presented an integrated approach by applying neural networks with ANN machine learning algorithms and mathematical programming to predict energy demands. The data relating to electricity generation and fuel consumption in power plants electricity imports and exports and energy-consuming sectors are collected. Then the neural networks for prediction accuracy improvement are introduced. To have an efficient power system a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in forecasting, especially in the short-term using a neural network, which is crictical for demand response. The ANN algorithm minds and analysis the data to optimize electricity user information. The electricity prediction model to verify the effectiveness of the model proposed. The results demonstrate that the ANN algorithm can significantly improve electricity user accuracy. The electricity demand model constructed can significantly improve the prediction accuracy and it can provide a theoretical and practical basis for electricity demand prediction, as well as development Planning for the electricity sector. © 2024 IEEE.

关键词： Demand response

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Skin Disease Detection Using Convolutional Neural Network 10

Skin Disease Detection Using Convolutional Neural Network

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Dhanabal, D. Divya, D. Hemanth, M. Karthika, V. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

Skin conditions are common everywhere, and early and precise diagnosis is essential for successful treatment. This work gives a thorough investigation of the use of Python programming and machine learning techniques for the prediction of skin disorders. In the suggested method, several machine learning algorithms are trained and assessed using a wide dataset of dermatological photos. The study starts with preprocessing methods to get the data ready for modeling, such as picture improvement and feature extraction. Then, using well-known Python libraries like scikit-learn and TensorFlow, a variety of classification methods are constructed, including Support Vector Machines, Random Forests, and Convolutional Neural Networks. To maximize the models' performance, cross-validation techniques are used for training and fine-tuning. The findings demonstrate the usefulness of the created models in properly predicting skin disease classes. Performance parameters such as accuracy, precision, recall, and F1-score are presented and compared between algorithms. The suggested Python-based system has considerable potential for supporting dermatologists and healthcare practitioners in the early detection and management of skin problems. © 2024 IEEE.

关键词： Convolutional neural networks

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