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2024 American Control Conference, ACC 2024

作者： Arana, Diego Chavez Garcia A, Omar A. Scola, Ignacio Rubio Espinoza, Eduardo S. Garcia Carrillo, Luis Rodolfo Sornborger, Andrew Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States National Polytechnic Institute Center for Research and Advanced Studies Mexico City Mexico National Institute of Industrial Technology CONICET National University of Rosario Department of Industrial Products Engineering Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances. © 2024 AACC.

关键词： Neural networks

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Simulate the Machine Learning Algorithm to Organize the CRAHN Network System 3

Simulate the Machine Learning Algorithm to Organize the CRAH...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Prabakaran, P. Chandra Sekhar Reddy, L. Ravikumar, L.V.D. Verma, Manish Kumar School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Hyderabad Medchal India Shri Vishnu Engineering College for Women Department of Information Technology Bhimavaram India Lovely Professional University Department of Psychology Phagwara India

ISBN: (纸本)9798350330724

Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machine learning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability. © 2023 IEEE.

关键词： Ad hoc networks

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide. © 2025 IEEE.

关键词： Precision agriculture

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Yantramanav: a humanoid to spread literacy among rural children through eGurukul education system

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CSI Transactions on ICT 2022年第3期10卷 227-233页

作者： Rohini, A. Kalaivani, K. Karthika, R. A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

eGurukul system is derived from the ancient Indian’s Gurukul education, where students gather in one place and learn from their master. Early childhood is the right time for children to improve their foundation skills. Children in Urban areas are exposed to such an environment where parents put their kids in kindergarten school. In rural areas, children would be admitted in school only at the age of eight years. Also, in our current education system, many rural schools across India do not have sufficient teachers to educate the children. So, we propose an eGurukul system, teaching aid to engage the students to learn the basics of communication language without the presence of a teacher/human. The Yantramanav is designed to teach the students with basic language (English, Tamil & Hindi) alphabets writing, pronunciation of the alphabets and rhymes, also the basics of Mathematics to the rural kids under the age of eight. The Yantramanav is designed with a mini projector, camera & speakers as a teaching and monitoring aid during time of activity. The humanoid uses the projector as a major tool for teaching. The projector helps the students to gather in one place and make the students to be attentive during the activities. The humanoid takes the attendance using the camera with the help of face recognition. Humanoid uses the speakers’ to deliver audio contents during activities. The entire activities are controlled by a MCU Board, which operates based on the programmed instructions. Switches are used to choose the communication language and activities. The content can also be delivered to other regions of various districts through online mode at the same time.

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Vapor growth of V‑doped MoS_(2)monolayers with enhanced B‑exciton emission and broad spectral response

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Frontiers of Optoelectronics 2023年第4期16卷 109-120页

作者： Biyuan Zheng Xingxia Sun Weihao Zheng Chenguang Zhu Chao Ma Anlian Pan Dong Li Shengman Li Key Laboratory for Micro-Nano Physics and Technology of Hunan Province State Key Laboratory of Chemo/Biosensing and ChemometricsHunan Institute of Optoelectronic IntegrationCollege of Materials Science and EngineeringHunan UniversityChangsha 410082China College of Advanced Interdisciplinary Studies and Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Wuhan National Laboratory for Optoelectronics(WNLO) Huazhong University of Science and Technology(HUST)Wuhan 430074China

Dynamically engineering the optical and electrical properties in two-dimensional(2D)materials is of great signifcance for designing the related functions and *** introduction of foreign-atoms has previously been proven to be a feasible way to tune the band structure and related properties of 3D materials;however,this approach still remains to be explored in 2D ***,we systematically demonstrate the growth of vanadium-doped molybdenum disulfde(V-doped MoS_(2))monolayers via an alkali metal-assisted chemical vapor deposition *** transmission electron microscopy demonstrated that V atoms substituted the Mo atoms and became uniformly distributed in the MoS_(2)*** was also confrmed by Raman and X-ray photoelectron ***-dependent photoluminescence spectra clearly revealed the enhanced B-exciton emission characteristics in the V-doped MoS_(2)monolayers(with low doping concentration).Most importantly,through temperature-dependent study,we observed efcient valley scattering of the B-exciton,greatly enhancing its emission *** transport experiments indicated that typical p-type conduction gradually arisen and was enhanced with increasing V composition in the V-doped MoS_(2),where a clear n-type behavior transited frst to ambipolar and then to lightly p-type charge carrier *** addition,visible to infrared wide-band photodetectors based on V-doped MoS_(2)monolayers(with low doping concentration)were *** V-doped MoS_(2)monolayers with distinct B-exciton emission,enhanced p-type conduction and broad spectral response can provide new platforms for probing new physics and ofer novel materials for optoelectronic applications.

关键词： Atomic substitution.V-doped MoS_(2) Distinct B-exciton Broad spectral response

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Next – Gen Driver Monitoring System

Next – Gen Driver Monitoring System

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Global Power, Energy and Communication Conference (GPECOM)

作者： Suganiya Murugan Harish Krishnadas S Abiishek G Pradeep Kumar S Rubini B Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

the Next Gen Driver Monitoring System utilizes advanced techniques to enhance road safety by detecting driver drowsiness, nodding, and blink patterns. Integrating cutting-edge technology, the system analyzes facial landmarks and eye movements in real-time through sophisticated algorithms. Through the fusion of computer vision and advanced algorithms, it accurately identifies signs of driver fatigue and distraction, providing timely alerts to mitigate potential risks on the road. This research delves into the design, implementation, and evaluation of the system, demonstrating its effectiveness in reducing accidents caused by drowsy or inattentive driving. The Next Gen Driver Monitoring System represents a significant advancement in automotive safety technology, ushering in safer driving experiences.

关键词： Computer vision Vehicle safety Fatigue Road safety Real-time systems Monitoring Vehicles

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Optimization Model for Machine Learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge Computing 4

Optimization Model for Machine Learning Based Intrusion Dete...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Kiruthiga, R. Jeyalaksshmi, S. Vanitha, V. Khan, Abdul Azeem Pal, Souvik Kumar, Rahul SRM Institute of Science & Technology Department of Computer Applications Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Sri Sankara Arts and Science College Department of Computer Applications Kanchipuram India Faculty of Islamic Technology Brunei Sister Nivedita University Department of CS and Engineering Kolkata India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The Internet of Things (IoT) and 5G communications now primarily use mobile edge computing (MEC). The fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. The network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. Thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time. © 2024 IEEE.

关键词： Network intrusion

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Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species

Designing a Heuristic Based Hybrid CNN with Attention Mechan...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： M. Bhanumathi B. Arthi Department of Computer Science and Engineering SRM Institute of Science and Technology Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies(VISTAS) Department of Computer Science and Engineering Faculty of Engineering and Technology College of Engineering and Technology SRM Institute of Science and Technology

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic System are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats.

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A Comparison of COVID-19 Detection using Deep Learning Methods

A Comparison of COVID-19 Detection using Deep Learning Metho...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： S. Pradeep Kumar Suganiya Murugan B. Rubini Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

The recognition of covid-19 is major confront in today’s world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure.

关键词： COVID-19 Deep learning Training Renewable energy sources Neural networks Predictive models Convolutional neural networks

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