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A Novel Deep Learning based IoT Enabled computerized Vehicle Number Plate Recognition System using OCR Principles

A Novel Deep Learning based IoT Enabled Computerized Vehicle...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： J. Bhuvaneswari N. Gireesh G. Srimathy Subrahmanyam Nandigam V. Nanammal Department of AI & DS R.M.K. College of Engineering and Technology Puduvoyal Chennai Department of ECE Mohan Babu University (Erstwhile Sree Vidya Nikethan Engineering College Tirupati Electrical and Electronics Engineering S.A Engineering College Chennai Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai Department of ECE Jeppiaar Engineering College Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A kind of intelligent transportation and detecting method is the license plate recognition system. The system can automatically recognize and read the license id of a vehicle’s number plate from digitally taken photographs thanks to this mix of technologies. The term “automatically capturing the license plate” refers to a technique that detects license plates and converts their pixel data into plain text or ASCII text. Modified Learning based Character Recognition (MLCR) is an analytical operation-based approach to vehicle number plate recognition that is part of this system. For the purpose of evaluating the suggested scheme’s efficiency, it is cross-validated with the traditional learning model known as Convolution Neural Network (CNN). The primary goal is to properly recognize and translate a specific vehicle’s license plate by using and combining several morphological techniques. Image enhancement, grayscale transformation, filtering, edge detection, and number plate extraction from vehicle images are some of the operations upon which this is built. Following the completion of the aforementioned processes, the segmentation process is being utilized to detect the text on the number plate. This is achieved by utilizing template matching and optical character recognition. From a photograph of the car, our method can swiftly and reliably deduce the license plate.

关键词： Vibrations Statistical analysis Neural networks Transportation Cameras Character recognition Reliability

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands Masters in Computer Science United States Jawaharlal Technological University Bachelors in Ece Hyderbad India University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350306088

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration's success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace. © 2023 IEEE.

关键词： Integration

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Advanced Fake News and Hate Speech Detection Leveraging Spectral Graph Theory

Advanced Fake News and Hate Speech Detection Leveraging Spec...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sowjanya, M. Devendraiah, K.M. Kalpana, V. Ramana, K. Venkata Hazela, Bramah Vaigandla, Karthik Kumar Geetanjali College of Engineering and Technology Department of Ece Cheeryal India V. V. Sangha's Independent P U College Ballari India R. M. K. College of Engineering and Technology Department of English Puduvoyal India Vardhaman College of Engineering Kacharam India Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Noida India Balaji Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350375442

Despite the proliferation of the Internet, prejudice and misinformation have persisted throughout history. False news and hate speech have long been a part of human nature, as has lying and slander. The proliferation of social media, however, has changed the timing, location, and nature of disinformation and hate speech. False news and hate speech are no longer confined to offline forums thanks to the *** suggested method consists of preprocessing, feature extraction, and training the model. The impact of pre-processing was examined by analyzing various pre-processing procedures, which were subsequently followed by the suggestion of a combination of these strategies. We used Word2Vec for feature extraction;it looks at a word's context to try to figure out what it means and how it's related to other words. Throughout the training process, Spectral Graph Theory was regularly utilized. This cutting-edge method outperforms CNN and LSTM with an average accuracy of 94.95%. © 2024 IEEE.

关键词： fake news detection hate speech. spectral graph theory

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Ramya A. Yazhini T. Geetha R. Sahila Devi Kavin K. S. S. Dhanalakshmi Department of Nano Electronics Materials and Sensors ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computer Science & Engineering College of Engineering Anna University Chennai India Department of Artificial Intelligence and Data Science Agni College of Engineering and Technology Chennai India Department of Computer Science and Engineering Rohini College of Engineering and Technology Palkulam India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress.

关键词： TCP Protocols Vehicular ad hoc networks Spread spectrum communication Routing Throughput Search problems

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A multidimensional time series data on wireless network to improve the detection performance of unsupervised learning algorithms

A multidimensional time series data on wireless network to i...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Paul, Chayan Ranjith, S. Vijayashanthi, R.S. Begam, R. Benazir Sabitha, R. KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science AP Vaddeswaram India Jeppiaar Engineering College Semmenchery Department of Electronics and Communication Engineering Chennai600119 India S.A. Engineering College Department of Electronics and Communication Engineering Poonamallee Avadi Road Tamil Nadu Thiruverkadu600077 India Rajalakshmi Engineering College Department of ECE Tamil Nadu Chennai602105 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In recent years, only with developments of cellular networking technologies as well as the implementation of 4G LTE networks, the skills and options provided to end-users had expanded to a level that seems to be limitless. Smart phone users who travel by public transportation rely on high-speed internet infrastructure and expect to have a stable, greater connectivity for the length of their journey. Because of the high requirements placed on wireless networks and the regular changes in the fundamental radio channel, customers frequently encounter rapid and surprising alterations in the connectivity reliability of their connections. The old traditional process of evaluating and inspecting infrastructure cellular technology should be no longer sufficient to fulfill the demands of the manufacturing industry. Consequently, an accurate mobile network cellular irregularity diagnostic method is required to monitor for problems that lead and to improving the effectiveness of device construction and operation. It is estimated that there are thousands of mistakes and exclusions in the current single-dimensional abnormality identification technique, and also that the present multifunctional abnormality diagnostic technique has a poor detection accuracy on multifunctional time series analysis. Multidimensional statistics depending on network service device abnormality classification optimization approach described in this work is predicated on machine learning that use limited of information to support in the instruction of tremendous unmonitored techniques, thereby enhancing the detection accuracy of unsupervised classification techniques. This paper conducts experimental studies to validate the functionality of the optimization techniques, and it shows a significant progress over the four generally used unmonitored techniques, that can greatly enhance the abnormality detection performance of the existing techniques, according to the authors. © 2022 IEEE.

关键词： Wireless networks

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An Intensive Review on 6G and its Future Technological Developments using New Technology Integration

An Intensive Review on 6G and its Future Technological Devel...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Anil Sharma Dr R Deepa S Sowmiya Rakesh Kumar T. Kuppuraj R. Pavaiyarkarasi Department of Electronics & Communication Engineering (ECE) IES College Of Technology Bhopal M. P India Department of Science and Humanities (English) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Engineering & Applications (CE&A) GLA University Mathura Department of Computer Applications Karpagam Academy of Higher Education Coimbatore TN Department of ECE R.M.K. Engineering College RSM Nagar Kavaraipettai Tamilnadu

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

As 5G deployment picks up steam, there’s been a noticeable change in the field of wireless research toward imagining what 6G will look like for communication in the future. The purpose of this article is to present the anticipated communication requirements and technology for the 6G future. The creation of digital twin realms that accurately replicate the biological and physical worlds in real time is the key to the future of connectedness as it is currently envisioned. The goal of this integration is to bring experiences from the digital, biological, and physical realms together. The development of novel man-machine layouts through productive local devices, the ubiquitous distribution of general computing across individual devices and the cloud, as well as multi-sensory integration of data for the creation of multi-verse corresponds and exciting mixed-reality experiences, and accurate sensing and actuation for efficient control of the physical world are emerging themes that are shaping 6G system specifications and technologies. The 6G air link and network will be supported by artificial intelligence, which will use data, computation, and energy as key resources to achieve higher performance. This paper also explores other revolutionary technologies that will likely shape 6G: cognitive spectrum sharing, integrating localization and sensing into system design, meeting strict latency and reliability performance requirements, new network architecture concepts involving sub-networks while RAN-Core convergence, and creative security and privacy strategies.

关键词： 6G mobile communication Location awareness Privacy Wireless sensor networks 5G mobile communication Layout Communication channels

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Intelligent and Smart Health Care Monitoring System based on Deep Convolutional Neural Network Model

Intelligent and Smart Health Care Monitoring System based on...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G. Anitha R. Sankar A. Vimala Juliet V. Dhivya B. Thiyaneswaran Ata Kishore Kumar Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Department of Electronics & Instrumentation Engineering SRM Institute of Science & Technology Chennai Department of ECE Vel Tech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Sona College of Technology Salem Department of ECE School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh

Remote health monitoring apps have revolutionized the healthcare industry as a whole with the advent of Internet of Things (IoT) technology. Strategies used to get insights through the analysis of lifestyle patterns as well as behaviors are also crucial to tailored healthcare and preventative services. Opportunities for both the investigation of illnesses and the forecasting of abnormal health situations are made possible by the use of intelligent data retrieval as well as categorization methods. The broad use of IoT has improved telemedicine capabilities and introduced more precise remote patient monitoring, both of which have had a favorable effect on the world at large. The impact of the Internet of Things (IoT) becomes deeper as it enhances healthcare infrastructure for patients, doctors, and hospitals. Through the usage of secure patient portals, healthcare providers have easier access to up-to-date patient information, allowing for more precise diagnosis and treatment. The prototype's potential for efficient healthcare monitoring is reinforced by the success of similar systems. In this research, we automated the AlexNet Model building process by experimenting with several approaches. In addition, a whole new method of diagnosis has been created. The experimental data shows that the suggested algorithm is superior to the state-of-the-art methods. The total accuracy of the suggested model is 97.13%, which is significantly higher than the accuracy of any of the other models compared. This demonstrates the model's efficiency and places it as a potentially game-changing breakthrough in healthcare.

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Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach 3

Interpreting the Evidence on Life Cycle to Improve Education...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Madumidha, S. Tripathi, Anju Srinivasan, P. Wategaonkar, Somnath Raghunath Chauhan, Amit Suganthi, D. Sri Krishna College of Technology Department of Information Technology Coimbatore India Mangalmay Institute of Management and Technology Dehi NCR Gr.Noida India Sona College of Technology Department of Ece Tamilnadu Salem India Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Navi Mumbai India School of Sciences Department of Life Sciences Karnataka Bengaluru India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India

ISBN: (纸本)9798350381931

Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. © 2024 IEEE.

关键词： Feature extraction

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An Innovative Method to Predict Real Estate Prices Using Convolutional Block Attention Module 4

An Innovative Method to Predict Real Estate Prices Using Con...

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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Babu, G.N.P.V. Vijayalakshmi, N.S. Yadav, Sameer Manigandan, E. Gulati, Chahat Pundir, Aditya Kumar Singh Rudraram Telangana Hyderabad India Nehru Memorial College Department of Economics Karnataka Sullia India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Saveetha College of Liberal Arts and Sciences Thandalam Department of Computer Applications Tamilnadu Chennai India Bits Pilani Pilani Campus Department of ECE Rajasthan India Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350300888

Both real estate brokers and owners can benefit strategically from foresight into rising or falling property values. This study intends to develop a novel method to accurately forecast real estate prices using machine learning models and actual transaction data. In machine learning models, the attributes of the real estate and the transaction prices are the independent variables and the dependent variables, respectively. Preprocessing, feature selection, and model training are used in the proposed method. Preprocessing involves the use of normalization. The selection of features is based on PCA and sequential analysis. CBAM is the model that is utilized for training. When compared to GRU and LSTM, two additional models, the proposed technique performs well. © 2023 IEEE.

关键词： Principal component analysis

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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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