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Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： E. Rajendran V. Raji Siddheswar Kar Riyaz A Rahiman Md Mujahid Irfan D. Karthikeyan Department of Electrical and Electronics Engineering SKP Engineering College Tiruvannamalai Tamil Nadu India Department of Computer Science and Engineering SKP Engineering College Tiruvannamalai Tamil Nadu India Department of Electrical Engineering Medi-Caps University Indore MP India School of Electronics and Communication Reva University Bangalore India Department of Electrical and Electronics Engineering SR University Warangal Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

Wind energy is attractive into a highly valuable energy source. To meet the grid connections criteria in light of the growing penetration of wind power, changes are needed. The purpose of this chapter is to learn how to control output and discuss the output regulator of a Permanent Magnet Synchronous Generator (PMSG) that is connected to a utility for wind power generation. The unit powered by wind turbine is regulated by a Sepic converter by PMSG. In order to ensure that PMSG’s power transmission can be commercially successful, their actual and reactive powers are strictly regulated. PI controller is contrasted with the suggested technique. Using MATLAB/Simulink, the effectiveness of the suggested approach and Sepic converter is proven.

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Image Classification for Potato Plant Leaf Disease Detection using Deep Learning

Image Classification for Potato Plant Leaf Disease Detection...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S. Durai T. Sujithra M. Mohamed Iqbal Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of CSE Manipal Institute of Technology Manipal Academy of Higher Education Manipal Senior Grade 1 School of Computer Science and Engineering VIT-AP University Amaravati

Identifying potato leaf diseases at an early stage is a difficult task due to the variability in crop species, crop disease symptoms, and environmental factors. To overcome this challenge, machine learning techniques have been developed. However, current models are limited to specific regions and cannot detect diseases in various crop species. This research proposes a multi-level deep learning model to recognize potato leaf diseases. The model uses a unique convolutional neural network to detect early blight and late blight potato infections from leaf images after extracting potato leaves from plant images using ResNet50 image segmentation. The model is trained and tested using a potato leaf disease dataset, achieving 99.75 percent accuracy. Furthermore, it outperforms state-of-the-art models in terms of accuracy and computational cost.

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IoT Based Environmental Monitoring System using LSTM-WAO-EEMD

IoT Based Environmental Monitoring System using LSTM-WAO-EEM...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Uppara Raghu Babu Neelam Sharma K Senthil Kumar Deshmukh Deepak Madhukar Pavankumar Ravindra Sonawane Neerav Nishant Civil Engineering Department Srinivasa Ramanujan Institute of Technology Ananthapuramu Andhra Pradesh India Department of Physical Education Lovely Professional University Punjab India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Mechanical Engineering Pravara Rural Engineering Collage Loni Maharashtra India Mechanical Engineering Department JSPM's Rajarshi Shahu College of Engineering Tathawade Pune India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow Uttar Pradesh India

In this proposed approach, the system takes a look at the environmental monitoring systems that are based on the Internet of Things. The suggested system's primary function is to supply remote locations with environmental parameters via the internet. The proposed method is a practical and easy way to monitor the surrounding environment and atmosphere. The system is a representation of ambient and environmental parameter monitoring by use of low-power wireless sensors linked to the Internet that report their readings to a centralized server. Finally, data collected from all over the world may be seen on the base station remotely from any Internet-connected device. From the physical level, with its sensors and communication protocol, to the cyber level, with its data management and storage, this work comprises the full solution, a cyber-physical system. It solves the issue of incompatible systems by providing a clear structure for exchanging information between sensors of varying measuring capabilities. This improves the effectiveness of monitoring systems. Using real-time sensors, the suggested system monitors conditions like moisture, temperature, humidity, rainfall, gas concentration, and seismic notification. An open-source platform known as Thing speak checks in on these variables every 2 minutes. The sensor data is then preprocessed, the features are selected, and the model is trained. EEMD-WOA-LSTM is used in the proposed method. When compared to two other models, including LSTM and GRU, the suggested method fares exceptionally well.

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Retraction Note: An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

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Soft Computing 2024年第1期28卷 399-399页

作者： Sreemathy, J. Arun, A. Aruna, M. Vigneshwaran, P. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

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Exploring Innovations in Autonomous Robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach

Empirical Analysis of Prediction of Crime and Forecasting Wi...

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International Conference on Edge Computing and Applications (ICECAA)

作者： R Vasuki Neha Ganvir Rohita Sharma K Senthil Kumar SVHN Krishna Kumari Kamlesh Singh Department of Computer Application Mannar Thirumalai Naicker College Madurai Tamilnadu India KJ College of Engineering and Management Pune Maharashtra India Fine Arts Lovely Professional University Punjab India Department of and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of CSE (Artificial Intelligence and Machine Learning) Vardhaman College of Engineering Telangana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well.

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A Robust and Fast Symmetric Text Encryption Algorithm Based on Fermat's Two Squares Theorem

A Robust and Fast Symmetric Text Encryption Algorithm Based ...

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Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： M Ranjith Kumar R Mani P Revathi S Sabarinathan Vediyappan Govindan Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science and Engineering C Abdul Hakeem College of Engr. & Tech. Melvisharam Tamil Nadu India Department of Mathematics SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mathematics DMI-St. John the Baptist University Mangochi Malawi

In the digital age, the integrity and privacy of information transmitted, communicated over computer networks or stored on computer systems are crucial. This paper suggests a novel and robust symmetric encryption scheme based on Fermat's theorem to facilitate seamless and secure communication while preserving the confidentiality and integrity of data sent over a public channel. The knowledge of the public key does not reveal any valuable information to an intruder for computing the encryption/decryption keys used here. An essential feature of this scheme is that no additional information is passed again through the public channel or a private courier in the computation of the encryption/decryption keys. Also, the keys are dynamic for each data transmission, and the Plaintext and Ciphertext are not the same lengths. Consequently, the proposed encryption-decryption technique is safer against all possible standard attacks and more efficient than the existing encryption algorithms.

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Predictive Maintenance of Electrical Machines using Machine Learning and Condition Monitoring Data

Predictive Maintenance of Electrical Machines using Machine ...

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

作者： S D Prabu Ragavendiran Deepak Shahakar D. Suvarna Kumari Ajay Singh Yadav P.M. Arthi N Rajesha Department of Computer Science and Engineering Builders Engineering College Tirupur Tamil Nadu India Department of Electrical Engineering P.R. Pote Patil College of Engineering & Management Amravati Maharashtra Department of Electronics and Communication Engineering Vignan Institute of Engineering for Women’s Visakhapatnam Andhra Pradesh India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad Uttar Pradesh Department of AIDS Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College(Autonomous) Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Predictive maintenance improves electrical machinery performance and reliability across industries. Machine learning algorithms combined with real-time condition monitoring data reduce unscheduled delays, enable proactive maintenance planning, and predict future *** research describes predictive electrical equipment maintenance using machine learning and condition monitoring data. The suggested method involves data acquisition and preparation, feature extraction and selection, and model construction and *** preparation and gathering involve real-time measurement from electrical equipment sensors and processing to remove noise and outliers. Numerous methods are used to gain insights from raw sensor data. Include time-series analysis, signal processing, and feature *** the feature extraction and selection phase, meaningful features are extracted from the preprocessed data to capture latent patterns that signal machine health. Feature selection methods like RFE, PCA, and correlation analysis can find predictive *** building and deploying predictive maintenance models, SVMs, neural networks, and random forests are trained on the chosen features. This research shows that predictive maintenance solutions that incorporate machine learning and condition monitoring data can benefit electrical equipment. Advanced analytics and real-time monitoring can boost efficiency, reliability, and cost savings in sectors.

关键词： Condition monitoring Time series analysis Predictive models Feature extraction Real-time systems Sensors Maintenance

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K-net+segan-based segmentation with gannet aquila optimization algorithm-enabled deep maxout network for brain tumor classification using mri

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Journal of Mechanics in Medicine and Biology 2023年第5期23卷 2350035-2350035页

作者： Ulaganathan, Sakthi Chen, Thomas M. Sathiyanarayanan, Mithileysh Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India Department of Electrical and Electronic Engineering School of Science and Technology City University of London LondonEC1V 0HB United Kingdom Research and Innovation MIT Square UK City University of London LondonEC1V 0HB United Kingdom

Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-Aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%. © 2023 World Scientific Publishing Company.

关键词： Generative adversarial networks

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Optimization of web-based application to donate blood-on-demand: A machine learning approach

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AIP Conference Proceedings 2024年第1期3075卷

作者： S. Sasikumar R. Pradeep P. V. Gopirajan M. Kanchana N. Velmurugeshkumar Department of Computer Science and Engineering Saveetha Engineering College Chennai 602105 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 India Department of Computer Science and Engineering RMK Engineering College Kavaraipettai Tamil Nadu India

Blood is a necessary element of all human beings. The number of persons who require blood is increasing in significant number day by day. Donors across the globe are three types: Voluntary donors, Professional donors, and Replacement donors. The safe, deliberate blood givers who are non-compensated from hazard populaces. The web structure for fussbudgets with cutoff times empowers quick turn of events and a clean, down-to-earth plan through Django. The current framework is restricted to manual processes. This has a constraint on controlling the processes safely, for announcing the outcomes on schedule, and has an intense usage on assets. Advanced software tools/applications are essential in providing easy access to get/donate blood in this pandemic. ‘Web-based blood donation’ is a machine language-based online portal to facilitate the coordination between the supply and demand of blood. The whole purpose of the project is to bring an online approach for blood givers and blood seekers and to create an interactive way of getting the donors and seekers of blood. The model proposed gives a lot of information about blood groups, blood availability, and blood donation details to create awareness among the people. In addition, extra features in the system, like security protection through a password, generating reports, chat applications, and stock shortage, can enhance management activities even more effectively.

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