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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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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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An Integrated Web-Based Platform for Enhanced College Placement Management and Student Engagement 10

An Integrated Web-Based Platform for Enhanced College Placem...

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

作者： Prakash, D. Shyam Sarumathi, K.M. Dhanashree, R. Sachin Samuel, R. Preetham Murthy, B. Department of Computer Science and Business systems Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350384369

In the contemporary educational landscape, college placement plays a pivotal role in the transition of students from academia to the professional world. However, this phase is often plagued by challenges such as inefficient communication between students and placement cells, inadequate tracking of job applications, and the need for personalized guidance. This research paper introduces a pioneering web application tailored to reshape this process fundamentally. The proposed system leverages state-of-the-art web technologies, coupled with the intelligence of artificial intelligence (AI) algorithms, to create an interface that redefines the way students and placement cells collaborate. This transformative web application streamlines communication, enhances efficiency, and ultimately optimizes outcomes for both stakeholders. In this paper, we present a comprehensive exploration of the system's architecture, functionality, and potential impact on the higher education ecosystem. © 2024 IEEE.

关键词： Students

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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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Revolutionizing Road Maintenance: YOLO Based Pothole Detection System 10

Revolutionizing Road Maintenance: YOLO Based Pothole Detecti...

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

作者： Saranya, E. Nivetha, R. Abirami, S. Mohaideen Arsath, M. Dharaneesh, S. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Potholes on roadways pose a significant risk to public safety, leading to numerous accidents and fatalities annually. In this research, we present a custom-trained machine learning model for real-time pothole detection using the You Only Look Once (YOLO) version 7 object detection algorithm. Our study aims to address the critical issue of pothole-related accidents by evaluating the effectiveness of YOLO V7 in identifying potholes under various road conditions and lighting scenarios. We compare YOLO V7 with a sequential Convolutional Neural Network (CNN) algorithm and demonstrate that YOLO V7 outperforms in real-time pothole detection. The system incorporates a camera that captures live footage and highlights potholes in real time, enhancing road safety. Our research showcases the versatility of YOLO V7 in detecting potholes in both daylight and low-light conditions. Furthermore, we explore the feasibility of deploying YOLO V7 on edge devices for real-time pothole detection from moving vehicles. The results affirm YOLO V7's high accuracy and fast processing capabilities, making it a suitable solution for improving road safety by alerting drivers to road hazards. This research contributes to enhancing road safety and reducing accidents caused by potholes, ultimately saving lives and minimizing road infrastructure damage. © 2024 IEEE.

关键词： Convolutional neural networks

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M2auth: A multimodal behavioral biometric authentication using feature-level fusion

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Neural Computing and Applications 2024年第34期36卷 21781-21799页

作者： Mahfouz, Ahmed Mostafa, Hebatollah Mahmoud, Tarek M. Sharaf Eldin, Ahmed Faculty of Computer Studies Arab Open University Muscat Oman Computer Science Department Minia University Minya Egypt Faculty of Computers and Information Minia University Minya Egypt Faculty of Computers and Artificial Intelligence Sadat City University Sadat City Egypt Faculty of Information Technology and Computer Science Sinai University Arish Egypt Information Systems Department Helwan University Helwan Egypt

Conventional authentication methods, such as passwords and PINs, are vulnerable to multiple threats, from sophisticated hacking attempts to the inherent weaknesses of human memory. This highlights a critical need for a more secure, convenient, and user-friendly approach to authentication. This paper introduces M2auth, a novel multimodal behavioral biometric authentication framework for smartphones. M2auth leverages a combination of multiple authentication modalities, including touch gestures, keystrokes, and accelerometer data, with a focus on capturing high-quality, intervention-free data. To validate the efficacy of M2auth, we conducted a large-scale field study involving 52 participants over two months, collecting data from touch gestures, keystrokes, and smartphone sensors. The resulting dataset, comprising over 5.5 million action points, serves as a valuable resource for behavioral biometric research. Our evaluation involved two fusion scenarios, feature-level fusion and decision-level fusion, that play a pivotal role in elevating authentication performance. These fusion approaches effectively mitigate challenges associated with noise and variability in behavioral data, enhancing the robustness of the system. We found that the decision-level fusion outperforms the feature level, reaching a 99.98% authentication success rate and an EER reduced to 0.84%, highlighting the robustness of M2auth in real-world scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smartphones

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time systems

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ACM Transactions on Design Automation of Electronic systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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