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International Conference on Smart technologies For Smart Nation (SmarttechCon)

作者： M. Sahaya Sheela Vijayakumari G Ahmed H. R. Abbas I. Parvin begum Anil Pratap Singh R J Anandhi Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Applications B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Department of Computer Science & Engineering IES University Bhopal MP India Department of Information Science & Engineering New Horizon College of Engineering Bengaluru

bPNN ensemble can significantly improve the generalization ability of knowledge system by training multiple bPNNs and synthesizing their conclusions. It not only helps scientists to study machinery knowledge and neural computing in depth, but also helps ordinary engineers and technicians to use bPNN technology to solve real-world problems. Ensemble knowledge has become one of the hot spots in the territory of machinery knowledge in recent years, and selective integration method has become an important direction of ensemble knowledge because of its advantages in adaptability, generalization and combination. In this paper, the transcription of multi-tone piano is studied based on bPNN. The corresponding study methods are used in the research. Through the establishment of data graph and algorithm formula, the corresponding study is carried out. From the research, it can be found that the piano transcription efficiency based on bPNN is very high, up to about 90.43%. In the future, people may pay more Focus to the piano study of bPNN.

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DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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Advancement in Computation & computer technologies (InCACCT), International Conference on

作者： P. Abirami D. Antony Joseph Rajan T. Vivekanandan V. Saillaja N. Mohankumar M. Rajmohan Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual’s performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system’s influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues.

关键词： Training Psychology Medical treatment Games Software Real-time systems Complexity theory

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AI-Assist Tool to Enhance the Reliability and Accuracy of Automated Speech and Writing Support for Students with Disabilities in Learning Languages

AI-Assist Tool to Enhance the Reliability and Accuracy of Au...

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IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： D. Amruta Surana K. Devika Prema S Nilesh H. Dhannaseth N. Gopinath P. Tamilarasan Department of Computer Engineering Sinhgad Institute of Technology lonavala Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Innovative educational technology have made it possible to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-based approaches have been among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the establishment of systems for both diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic backgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasible to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-based strategies have been among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for both diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and be successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in both text and audio versions after that. This makes it simple to assist any student with a disability in having good communication in a foreign language.

关键词： Cloud computing Navigation Educational technology Writing Speech enhancement Assistive technologies Reliability Artificial intelligence Socioeconomics Faces

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Text Transmission Through Li-Fi in WSN Using Smart based Secure Morse Code Algorithm

Text Transmission Through Li-Fi in WSN Using Smart Based Sec...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： I. Vasudevan P. Malathi S. Siddik P.R Therasa M.Mohamed Nasurudeen R.M.Dilip Charaan Dept.of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Dept of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Dept.of EEE Pavaai Engineering College Namakkal India Dept.of CSE R.M.K Engineering College Kavaraipettai India Dept.of CSE V.S.B Engineering College Karur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cyber security is one the most challenging task in today's world. Most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a Smart based Secure Morse Code (SSMC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately.

关键词： Wireless communication Wireless sensor networks Codes Transmitters Receivers Light fidelity Encoding Decoding computer crime Standards

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted boltzmann Machine

Smart Water Management in Agriculture: Enhancing Crop Produc...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： K. b. Gore N. Chidambararaj L. Kartheesan Manasi Vyankatesh Ghamande Ghanasham C. Sarode Gourav Kalra Department of Mechanical Engineering SNJB’s LS K. B. Jain College of Engineering Nashik India Department of Electrical and Electronics Engineering St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Engineering Science and Humanities Vishwakarma Institute of Technology Pune India Department of Civil Engineering Dr. D.Y. Patil Institute of Technology Pune India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Ambala India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. by implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RbM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DbN and KNN.

关键词： Training Productivity Adaptation models Irrigation Accuracy Nearest neighbor methods Software systems Socioeconomics Water resources Crop yield

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Design and Development of Hybrid Feature Selection Method for Classification

Design and Development of Hybrid Feature Selection Method fo...

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2021 International Conference on Intelligent technologies, CONIT 2021

作者： Sreedevi, M. Manasa, Garikapati Apurva, Idupulapati Koneru Lakshmaiah Education FoundationVaddeswaram Dept of Computer Science and Engineering Andhra Pradesh India Koneru Lakshmaiah Education Foundation Vaddeswaram Jonnalagadda Mohana and Reddem Pravallika Final Year B.Tech CSE Andhra Pradesh India

ISBN: (纸本)9781728185835

To enhance the capability of the learning model in this research paper we have developed a hybrid feature selection method. To defeat the curse of dimensionality, to speed up the classification process, and to get more accurate results a hybrid feature selection model is developed which is a combination of multiple filter methods and a wrapper method. In this model, we employed two sets of Filter Methods-basic filter methods, a correlation filter method and two Statistical Ranking filter methods (ANOVA and ROC-AUC) to generate two different subsets of important features, and a Wrapper method (Recursive Feature Elimination with Cross-Validation) is applied on the combined subset to generate a final subset of important features for better prediction results. Five machine learning algorithms-Logistic Regression (LR), Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour are used to evaluate the classification accuracy. The proposed hybrid method is applied over four low and four microarray datasets. Outputs are compared to find out which algorithm works best with the proposed model as the results diverge with the machine learning algorithm. Precision, Sensitivity, and Specificity are calculated for each outcome and they demonstrate that the suggested method improved the accuracy of the algorithms. © 2021 IEEE.

关键词： Feature Selection

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Enhancing Human-Robot Collaboration through 5G-Enabled Transformation and Image-Guided Control with TYOLOV5 Algorithms

Enhancing Human-Robot Collaboration through 5G-Enabled Trans...

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International Conference on Image Information Processing

作者： Sridhar Madasamy P.S. Ramesh Priyanka Roy S R Arun Raj Vinay Kumar Yadav Allam balaram Cognizant Technology Solution 500 Frank W. Burr Boulevard Teaneck NJ USA Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Electronics & Communication Engineering U.B.D.T College of Engineering Davanagere Karnataka India Glocal School of Science and Technology Glocal University Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Factories that prioritize digital transformation experience heightened production rates and gain a competitive advantage by enhancing control and operational efficiency. In this study, image processing techniques were employed to achieve digital transformation objectives, and the data obtained was transmitted via 5G communication to a collaborative robot arm, enabling remote control. The robot arm was fitted with a 3D-printed humanoid hand for bin picking, where each finger was controlled by five servo motors. To manipulate the fingers, a user donned a glove equipped with flex sensors, which conveyed the finger positions to the servo motors, facilitating precise pick and place operations. Additionally, image processing was utilized to accurately control the robot arm’s position. The research involved the implementation of tuned YOLO V5 (TYOLOV5) algorithms, using Python software, for object detection within the user’s gloves. by comparing this approach with other existing methods, the experiment achieved the highest detection accuracy, demonstrating the effectiveness of the proposed approach in achieving superior recognition and estimation compared to traditional and existing deep learning methods. To validate its efficacy, experiments were conducted in a laboratory setting, where a collaborative robot was deployed for a small part assembly task. The results conclusively highlight the potential of this approach to significantly enhance recognition and estimation capabilities, especially in the realm of human-robot collaboration.

关键词： YOLO Service robots Image processing Collaboration Robot sensing systems Manipulators Trajectory

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VLSI implementation using fully connected neural networks for energy consumption over neurons

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SUSTAINAbLE ENERGY techNOLOGIES AND ASSESSMENTS 2022年第PartA期52卷

作者： Mehbodniya, Abolfazl Kumar, Ravi Mohanty, Sachi Nandan Geetha, A. bedi, Pradeep Tripathi, Rohit [a]Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area 7th Ring Road Kuwait [b]Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Guna Madhya Pradesh India [c]Department of ECE Saveetha School of Engineering SIMATS Thadalam Chennai - 602105 Tamil Nadu India [d]Department of Computer Science & Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India [e]B.Tech Department of ECE Indian Institute of Space Science and Technology Thiruvananthapuram 695547 Kerala India [f]Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.

关键词： Neural Network Deep learning Energy consumption VLSI

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Finite-Time Robust Formation Control of Multiple Aerial Robotic Vehicles with Uncertainties and Time-Varying Complex Perturbations

SSRN

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SSRN 2022年

作者： Li, Lebao Gao, Jinfeng Liu, Peter Xiaoping School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

This paper addresses the robust formation control problem of finite-time leader-follower for multiple aerial robotic vehicles (ARVs) suffering from parameter uncertainties and time-varying perturbations. To ensure the state (e.g. position and velocity) of all followers to converge to leader in finite time, a new finite-time high-order sliding mode consensus control (HOSMCC) scheme is designed. To better cope with the parameter uncertainties and time-varying disturbances, improve formation control accuracy and achieve the robust formation control of finite-time leader-follower in multiple ARV systems, a finite time high-order sliding mode formation control (HOSMFC) scheme on the basis of finite-time HOSMCC is proposed. The finite-time stability of multi-ARV formation control system is guaranteed, and the desired formation pattern of multi-ARV systems is achieved using Lyapunov stability theorem. Performance comparisons with proportional differential formation controller (PDFC) and sliding mode formation controller (SMFC) are studied on a four-ARV formation control system. The results validate the designed formation control scheme. © 2022, The Authors. All rights reserved.

关键词： Controllers

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Smartcharge: Reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika b. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this Recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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