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computer, Communication and Control (IC4), International Conference on

作者： m N Rekha mudarakola Lakshmi Prasad Saptarshi mukherjee Sudam Vasant Nikam Swati Sharma Pundru Chandra Shaker Reddy Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysore Karnataka India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India School of Computer Science Engineering and Information Science Presidency University Bangalore Karnataka India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network–based machine-translation(mT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for mT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model’s translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner’s corpus is corrected using the grammatical fault alteration approach, and “error-correct” sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model’s error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB.

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Performance Analysis of Various machine Learning techniques for mental Health Tracking 2nd

Performance Analysis of Various Machine Learning Techniques ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Poongothai Kasthuri mariammal Yeturu, Jahnavi Department of Computational Intelligence S.R.M Institute of Science and Technology TamilNadu Chennai India Department of Computer Science Arulmigu Subramania Swamy Arts and Science College Thoothukudi628 907 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Avadi Chennai India Department of Computer Science Dr. V. S. Krishna Govt. Degree and PG College Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9783031689048

Artificial intelligence technologies called "machine learning" automatically build models from data in order to improve decision-making. An examination of mL that can be utilized for mental illness treatment has been prompted by the upsurge in mental health diseases and the directive for efficient medical sustenance. The quality of life for patients is improved and treatment outcomes are increased when mental health issues are identified early. Therefore, it is critical to address basic mental health issues that, if left unattended, could develop into more serious issues. The analysis and diagnosis of medical data can today be accomplished with the help of machine learning algorithms. The conventional (CCTV) system for tracking patient activity is ineffective and expensive, and employing a sensor-based system is particularly challenging because of depleted battery life. The real goal is to provide a critical review, and literature review, about all the machine learning techniques which are supervised-based. These techniques include Random Forest Classification, KNN Classifier, Logistic Regression, Decision Tree Classifier, Bagging, Boosting, & Stacking. And to determine the most effective machine-learning technique for monitoring someone’s mental health. It has been found that Accuracy and ROC Curve plays an important role for comparing the mL models. The outcomes clearly show that stacking generated more precise outcomes by showing the accuracy of 82%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Synthetic Data Generator for an E-Learning Platform in a Big Data Environment

Synthetic Data Generator for an E-Learning Platform in a...

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International Conference in Information technology and Education, ICITED 23

作者： moreno, Yaneth montero, Anthony Hidrobo, Francisco Infante, Saba CEMISID Faculty of Engineering University of Los Andes Mérida Venezuela Department of Computer Science Faculty of Engineering University of Los Andes Mérida Venezuela School of Mathematical and Computational Sciences Yachay Tech University Imbabura San Miguel de Urcuquí Ecuador

ISBN: (纸本)9789819954131

In this paper, we present the implementation of a big data environment to extract, filter, and classify data to use tools that anticipate its growth and that allow scaling resources in order for results to be used as a basis for future analysis or, as in this case study, for the generation of synthetic data. Specifically, we focus on the information available in a learning management system, concretely moodle, where numerous interactions occur between users and the learning platform. All actions of users, mainly students, are recorded and can be used to reproduce behaviors. Then, we create a request simulator to emulate the behavioral pattern of students, using data from a database taken from an operative learning management system for training. Results were quite similar concerning student behavior, as reflected in the statistics on the operational learning management system database and the data synthetically generated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Big data

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Social Distance monitoring method with Deep Learning to prevent Contamination Spread of Coronavirus Disease 6

Social Distance Monitoring Method with Deep Learning to prev...

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6th International Conference on Computing methodologies and Communication, ICCmC 2022

作者： Thylashri, S. Femi, D. Devi, C. Thamizh Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India M.A.M College of Engineering and Technology Department of Information Technology Tiruchirappalli India

ISBN: (纸本)9781665410281

The ongoing COVID-19 virus pandemic has resulted in a global tragedy due to its lethal spread. The population's vulnerability grows as a result of a lack of effective helping agents and vaccines against the virus. The spread of viruses can be mitigated by minimizing close connections between people. Social distancing is a critical containment tool for COVID-19 prevention. In this paper, the social distancing violations that are being made by the people when they are in public places are detected. As per CDC (Centers for Disease Control and Prevention) minimum distance that should be maintained by people is 2-3 meters to prevent the spread of COVID- 19, the proposed tool will be used to detect the people who are maintaining less than 2-3 meters of distance between themselves and record them as a violation. As a result, the goal of this work is to develop a deep learning-based system for object detection and tracking models in social distancing detection. For object detection models, You Only Look Once, Version 3 (YOLO v3) is used in conjunction with deep sort algorithms to balance speed and accuracy. To recognize persons in video segments, the approach applies the YOLOv3 object recognition paradigm. An efficient computer vision-based approach centered on legitimate continuous tracking of individuals is presented to determine supportive social distancing in public locations by creating a model to generate a supportive climate that contributes to public safety and detect violations through camera. © 2022 IEEE.

关键词： COVID-19

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning 4

Intelligent Deep Learning Framework for Breast Cancer Predic...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep Learning KNN machine learning RNN SVm

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Enhancing Speech Recognition for Hearing-Impaired Individuals Using Deep Learning models 7

Enhancing Speech Recognition for Hearing-Impaired Individual...

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

作者： Kalaiarasi, K. medidhi, Winson Niresh Kumar, S. Thenappan, S. Sathiyaseelan, R. mohana Sundaram, K. Saveetha Institute of Medical and Technical Sciences Department of ECE Saveetha School of Engineering Chennai India Vijayawada Andhra Pradesh Kanuru India St. Joseph's College of Engineering OMR Department of CSE Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Arunai Engineering College Department of Computer Science Engineering Tamil Nadu Thiruvannamalai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India

ISBN: (纸本)9798350372816

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies. © 2024 IEEE.

关键词： Self-supervised learning

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OpenCV Based Real-Time Traffic Analyzer

OpenCV Based Real-Time Traffic Analyzer

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Computing and Data science (ICCDS), International Conference on

作者： C Pandi N.P Ponnuviji R. Srinivasan C. Parthasarathy P. Karthick Rohith Kumar V Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal India Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The report focuses on the development and implementation of a real-time traffic analysis system using OpenCV (Open Source computer Vision Library). The project aims to leverage computer vision techniques to analyze and monitor traffic patterns, providing valuable insights for traffic management and urban planning. The system utilizes video streams from surveillance camerasplaced at strategic locations such as traffic intersections or highways. These video streams are processed in real-time usingOpenCV algorithms to extract relevant information about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide meaningful insights for traffic management authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step employs specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various components and methodologies used in developing the system. We will explore the algorithms and techniques employed for vehicle detection and tracking, as well as the methods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancements for the system. The findings of this paper demonstrate the feasibility and effectiveness of using OpenCV-based systems for real-time traffic analysis. The insights gained from this research have the potential to contribute to more efficient traffic management strategies and informed decision-making in urban planning.

关键词： computer vision Surveillance Roads Urban planning Data visualization Traffic control Streaming media

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Design of Proficient Fused mAC Unit for PID Controllers 3

Design of Proficient Fused MAC Unit for PID Controllers

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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Abinaya, A. maheswari, m. Haridoss, G. Sherin Shibi, C. Babitha Lincy, R. Jency Rubia, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India K.Ramakrishnan College of Engineering Department of Ece Tiruchirappalli India M.A.M College of Engineering and Technology Department of Ece Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Sir Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The implementation of PID controllers in ASICs demands a strong grasp of system and hardware design. Despite the straightforward control structure and efficiency of industrial components regulated by PID controllers, they still incur significant power consumption. These control systems incorporate VLSI components like adders, multipliers, and accumulators to operate effectively. In this study, the integration of a fused mAC (multiply-Accumulate) unit is employed for designing PID controllers, aiming to enhance efficiency across power consumption, path delay, and the utilization of LUTs. © 2024 IEEE.

关键词： Three term control systems

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An Accurate Prediction of Coronary Heart Disease Using Ensemble Algorithms 1

An Accurate Prediction of Coronary Heart Disease Using Ensem...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Joshi, Kambham Pratap Prasad, m Lakshmi Natchadalingam, R. Reddy, Pundru Chandra Shaker mukherjee, Saptarshi Babu, G Charles Hyderabad Dundigal India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Maulana Abul Kalam Azad University of Technology M. Tech-Bioinformatics West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350316469

Coronary Heart-disease (CHD) is one the foremost causes of death globally, making the accurate recognition of it vital. machine-learning (mL) and deep-learning (DL) are two of the newest technologies being put to use in the healthcare industry. This paper uses a risk factor method to examine how CHD can be predicted. K-Nearest-Neighbors, Binary-Logistic Classification, and Naïve-Bayes are only few of the predictive methods that are measured by accuracy, recall, and receiver operating characteristic (ROC) curves for evaluation. Ensemble modeling strategies including bagging, boosting, and stacking are contrasted to these foundational classifiers. The efficacy of using ensemble approaches to enhance coronary heart disease prediction was investigated using a comparative analytical methodology. The 'Cardiovascular-Disease Dataset,' which includes 80,000 records of patient data for CHD, is used to evaluate the modeling techniques. It is demonstrated that bagged models, on average, are more accurate than their non-bagged counterparts by the margin of 1.99 percentage points. The highest accuracy of all models was 76.5%, but the AUC for the boosted models was 0.78. Accuracy of 75.1% was achieved by the stacked model consisting of KNN, random-forest classifier, and SVm. In addition, data-analytic methods and K-Folds cross-validation were used to verify the tested models' performance. © 2023 IEEE.

关键词： Artificial intelligence CHD Ensemble techniques Heart-disease Hybrid modeling machine-learning

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monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced marine and Polar Dynamics

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTm) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from mODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTm-RNN model achieves high predictive accuracy, with root mean square error (RmSE) values as low as 0.08 m for ice thickness and a mean absolute error (mAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTm (RmSE 0.12 m, mAE 0.09 m) and CNN-LSTm hybrid (RmSE 0.10 m, mAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTm-RNN machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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