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International Conference on Computing and Networking technology (ICCNT)

作者： G. Uganya Shruthi V Kulkarni Vani Valsaraj Anuja Govindrao Gunale d. rajalakshmi B Srividhya Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Bengaluru Karnataka Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Vataria Gujarat Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of ComputerApplication Faculty of Humanities and Sciences SRM Institute Of Science and Technology Chengalpattu Tamil Nadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Now a days, more existing techniques are available to found tumours on brain from Mr images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of “K-Nearest Neighbour - Long Short-Term Memory” (“KNN-LSTM”) with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the Mr images from huge dataset with usage of machine learning approach of “KNN-LSTM”. Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model “KNN-LSTM” for categorizing the tumours with predicting through Mr images. MrI brain imaging collection is used for our experimentation. The “KNN” algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes.

关键词： Machine learning algorithms Accuracy Sensitivity Magnetic resonance imaging Machine learning Nearest neighbor methods Brain modeling Long short term memory Tumors Testing

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Empirical Evaluation of License Plate recognition Using Tensorflow Framework

Empirical Evaluation of License Plate Recognition Using Tens...

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BIT Conference (BITCON), International

作者： S. Sowmyayani V. S. Harilakshmi J. Kavitha r. roselinkiruba Department of Computer (SF) St. Mary’s College (Autonomous) (Affiliated to Manonmaniam Sundaranar University Tirunelveli) Thoothukudi Tamilnadu India Department of Artificial Intelligence and Data Science Holy Cross College (Autonomous) Nagercoil Tamilnadu India Department of Computer Science and Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

due to the tremendous increase in vehicles, more attention is required to Intelligent Transport Systems (ITS). License Plate recognition (LPr) is one such method in ITS. Although there are several researches on LPr, still the issues are partially solved. This is because license plate images suffer from low illumination, side view, etc. This paper developed a method to recognize license plates. It consists of license plate localization, segmentation and recognition. A Convolutional Neural Network (CNN) is designed for recognition. The proposed method is tested on the Chinese City Parking dataset (CCPd), Chinese License Plate dataset (CLPd), Application Oriented License Plate (AOLP) and PKUdata datasets. The performance of the proposed LPr method is evaluated using accuracy. It outperforms other recent methods by achieving an accuracy of 95%

关键词： Location awareness Image segmentation Accuracy Image recognition Codes Urban areas Lighting Convolutional neural networks License plate recognition

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： P. Sathyanathan K. Balachander Pramod Pandey V. Prasanna Srinivasan T. r. GaneshBabu M. Muthulekshmi Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes.

关键词： Pediatrics Machine learning algorithms Surveillance Medical services Transforms Nearest neighbor methods real-time systems Internet of Things Wearable sensors Asthma

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data-driven decision Architectures in Healthcare: The role of AI in Enhancing Predictive Capabilities

Data-Driven Decision Architectures in Healthcare: The Role o...

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International Conference on Inventive Computation Technologies (ICICT)

作者： M. Guru Vimal Kumar revathi K Gulshan Banu A Sivaguru J r. Prasanth Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Information Technology SNS College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamilnadu India Department of Mechatronics Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Department of Catering Science and Hotel Management Dr. N.G.P. Arts and Science College Coimbatore Tamilnadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Breast cancer affects women all around the world, and it can be dangerous. As stated by clinical specialists, early detection of cancer saves deaths. Lot of the machine-learning algorithms have been created to categorize tumors to identify breast cancer, and medical imagery processing is a useful tool for improving classification accuracy. Micro classifications are among the most important indications for early detection of breast cancer. Many investigations have used machine learning methods to address this issue. Numerous classification methods are employed to detect the possibility of breast cancer, either benign or malignant. An image-enhancing technique has been designed to aid in the earlier identification and diagnosis of cancer. The present research attempts to categorize breast cancer using a machine-learning method. The goal of the current study was to use machine learning (ML) techniques to increase the reliability of disease diagnosis. This study assesses and chooses a number of breast cancer prediction methods based on their accuracy, interpretability, and ease of use.

关键词： Support vector machines Accuracy Machine learning Transforms Predictive models Feature extraction Breast cancer Medical diagnosis Medical diagnostic imaging Tumors

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Naga Venkata ramakrishna Guduri r.M. dilip Charaan Kalaiyarasi r Samuthira Pandi V d.Laxma reddy Selvaprabhu. T Faculty of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of CSE Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types.

关键词： Adaptation models Temperature distribution Technological innovation Attention mechanisms Accuracy Predictive models data models real-time systems Convolutional neural networks Predictive maintenance

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Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer rewards in retail Marketing

Constructing a Blockchain-Based Loyalty Program Model for Se...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Ch. Anudeep Kathari Santosh M Sandeep Kumar Jahnavi M Sabareesh r B Kiran Bala GITAM school of business GITAM University Visakhapatnam A.P. Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore Tamil Nadu Department of Artificial Intelligence and Data Science K. Ramakrishnan college of Engineering Trichy TN

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5 .

关键词： Industries data privacy Communication systems Smart contracts Throughput Blockchains Fraud

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and Hr

Blockchain and Machine Learning Integration for Safe Data Sh...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： T. B Sivakumar E. Prathiba Shweta Sharma rashmi Saket Prasad M. S. ramaratnam V. Krishnamoorthy Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Mailam Engineering College Mailam India Department of Business Administration Shambhunath Institute of Engineering & Technology Prayagraj India D Y Patil PGDM Institute Pune Akurdi India Department of Management Studies Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram India Department of Management Studies Kongu Engineering College Erode Perundurai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Modern finance and Hr businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and Hr industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system.

关键词： Industries Training Adaptation models Accuracy Heuristic algorithms data security Computational modeling Finance Blockchains Predictive analytics

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reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry

arXiv

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arXiv 2022年

作者： Lolur, Phalgun Skogh, Mårten Warren, Christopher Biznárová, Janka Osman, Amr Tancredi, Giovanna Wendin, Göran Bylander, Jonas rahm, Martin Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden Department of Microtechnology and Nanoscience MC2 Chalmers University of Technology Gothenburg Sweden

decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (rEM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. rEM can be applied alongside existing mitigation procedures, while requiring minimal post-processing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver (VQE). Two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H2, HeH+ and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to argue for scalability of the method. © 2022, CC BY-SA.

关键词： Ground state

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Experimental Analysis of radioscopy Images based Pneumonia disease detection using Improved Neural Network Methodology

Experimental Analysis of Radioscopy Images based Pneumonia D...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Sajja Suneel B.M.S. rani M. Amina Begum Jayendra Gopal Thatipudi Ajay Singh Yadav d. Muthukumaran Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Vignan’s Nirula Institute of technology and science for women Department of ECE Oxford College of Engineering Venmani Tamilnadu India Dept. of Computer Science University of North Texas Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The bacterium Streptococcus pneumonia is the most prevalent causative agent of pneumonia, a severe and potentially fatal lung infection in humans. According to the World Health Organization, pneumonia accounts for one in three deaths in India. Assessing chest radiographs to detect pneumonia typically requires skilled radiologists, making an automatic detection method crucial for rapid treatment, especially in rural areas. This study introduces an Improved Neural Network (INN) learning method, cross-validated against existing Convolutional Neural Networks (CNNs), reflecting the success of deep-learning algorithms in medical image analysis. The research highlights the utility of these methods for disease classification. The features learned by INN and CNN models on large datasets are particularly valuable for image classification tasks. By using feature extractors programmed on trained INN and CNN frameworks, this study compares the performance of various classifiers in identifying normal and abnormal chest radiographs. Statistical results indicate that pre-trained INN models combined with supervised classifier approaches significantly enhance the evaluation of chest radiographs, particularly in detecting pneumonia. This advancement is pivotal for improving diagnosis and treatment outcomes in resource-limited environment.

关键词： Learning systems Pneumonia Neural networks Lung Organizations Feature extraction Convolutional neural networks

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Contrastive Learning for Improved Abstractive Sentence Summarization

Contrastive Learning for Improved Abstractive Sentence Summa...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Ulligaddala Srinivasarao Karthikeyan r Mohamed Sithik M Faizal Mukthar Hussain S Ohammed Uveise S A Hariharan Shanmugasundaram Department of CSE GITAM (Deemed to be) University Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Mohamed Sathak Engineering College Ramanathapuram India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Text summarizing aims to overview the material while maintaining essential details concisely. Lately, summary tasks have been included in the contrastive learning process in addition to visual representation. To optimize their similarities, contrastive learning summarizing algorithms currently concentrate on modelling and semantic similarity in all documents, targets, and data summaries. They do not, however, consider the impact that sentence semantics have on the text. In this study, we propose an rNN technique to develop the model for detecting the salient information. The sentence salience is based on the semantic similarity between the summary sentences of the original content, taking into account the distance of the loss of contrast in the form of soft weights functions. Consequently, our proposed model minimizes the semantic similarity given the textual summaries and possible noise and maximizes the semantic words between summaries and relevant information. Three popular datasets—multi news, CNN/daily Mail, and Booksum datasets achieved the highest accuracy of 0.69% in the news datasets. The experiment outcomes demonstrate that the suggested strategy can considerably raise baseline performance and outperform other contrastive learning techniques.

关键词： Computers Visualization Semantics Noise reduction Noise Contrastive learning Encoding data models data mining Postal services

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