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The Prediction of m.cro Plasm. Im.acts of Farm.Fresh Vegetables Using m.chine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： m.ragatham. G. Sundaram.orthy, K. m.heswari, K. Sajana, T. Shiba, C. m.ry Kum.r, Ashok Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Tamil Nadu Chennai600100 India Department of Computer Science and Engineering Cmr Technical Campus Kandlakoya Telangana Hyderabad501401 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Business Systems R. M. K Engineering College Tamil Nadu Chennai601206 India Department of Computer Science Banasthali Vidyapith Rajasthan304022 India

ISBN: (纸本)9798350334357

In general, the innovative foods produced on fruit and vegetable based farm. are always high quality and healthy. Indicators of fruit and vegetable consum.tion include plasm. vitam.n C and arytenoids, which are plant pigm.nts discovered in blood sam.les. The researchers decided to utilize blood sam.les rather than the m.re com.on food frequency questionnaire in their investigation. to assess the am.unt of food consum.d in order to forestall m.asuring errors and to establish dependencies. Because vitam.n C and arytenoids m.y be found in a wide variety of fruits and vegetables, we can use them.as objective m.asures of our consum.tion of these food groups. The fact that individuals who do not consum. a diet that is abundant in fruits and vegetables do not consum. significant quantities of vitam.n C and arytenoids is reflected in the plasm. levels of these individuals. In this paper a sm.rt m.chine learning algorithm.was proposed to predict the m.cro plasm. im.acts. This m.nitors the regular shape and harvesting of different farm.fresh products and predicts the im.acts of it. This will helpful for farm.rs to enhance the harvesting. © 2023 IEEE.

关键词： m.chine learning

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Cardiovascular Abnorm.lities Identification by m.chine Learning Classifiers and its Com.arision

Cardiovascular Abnormalities Identification by Machine Learn...

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2023 Intelligent Com.uting and Control for engineering and Business System., ICCEBS 2023

作者： Surendran, S. Reddy, B.m.lakonda Krishnam.al, P.m.thu Venkataram.naiah, B. m.rugesan, S. Joany, R.m. Tagore Engineering College Department of Computer Science and Engineering Chennai India Narayana Engineering College Andhra Pradesh Gudur India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350394580

Heart diseases related to the heart increasing everywhere. Hum.n beings are suffering from.m.st dangerous cardiovascular disorders (CVD). Features are m.in param.ters to identify diseases. The feature extraction is done from.the signals and m.ke ready for the analysis. Where m.chine learning (m.) is a technique that tells the potential em.rgence of cardiac disease. Inform.tion science uses m.chine learning to address a variety of difficulties. The basic use of m.chine learning is the forecasting of a result based on data that is already available. The m.chine applies the designs from.the current dataset to a hidden dataset in order to predict the disorders. There are several arranging calculations to be com.leted with sufficient accuracy. We have used various m.chine learning classifiers for identifying CVDs. Random.forest (RF) classifier producing highest accuracy com.ared to other classifiers. © 2023 IEEE.

关键词： Decision Tree KNN Logistic Regression m. RF Support Vector m.chine

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Early size estim.tion using m.chine learning 8

Early size estimation using machine learning

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8th International Conference on Com.uting for Sustainable Global Developm.nt, INDIACom.2021

作者： m.nisha Rishi, Rahul University Institute of Engineering and Technology M. D. University Department of Computer Science and Engineering Rohtak India

ISBN: (纸本)9789380544434

The process of size estim.tion of a project is one of the key activities that form. the indispensable part of any project developm.nt process. This broadly involves the evaluation of the num.er of codes and packages that can only be analyzed once the project is com.leted. The presented paper aim. at the size estim.tion through predictive analysis using m.chine Learning (m.) architecture to offer best possible size and effort estim.tion to com.lete a project. The article also evaluates the existing research in the field of size estim.tion discussing the research trends inspired with the m. techniques as size prediction m.dels. Finally, it presents com.rehensive survey of the possible prospective of m. approaches for size estim.tion along with the detailed inform.tion in term. of strengths, lim.tations, evaluation m.trics im.lem.nted for evaluation along with the datasets involved in the studies. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： m.chine learning

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Deep Learning Based Driver Drowsiness Detection in Real Tim. Environm.nt - An Enhanced Approach

Deep Learning Based Driver Drowsiness Detection in Real Time...

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Sentim.nt Analysis and Deep Learning (ICSADL), International Conference on

作者： Ashin Ajay Naveen Kum.r Ashok Gokul Balajiram.Abinesh Tam.zhselvan Senthil Kum.r Thangavel Som.sundaram.K M. Tech in Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India B. Tech in Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics School of Physical Sciences Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Effective driver behavior m.nitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based m.thodology. The m.del uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to im.rove road safety by offering a reliable way to identify distractions.

关键词： Deep learning Sentim.nt analysis Transfer learning Road safety Real-tim. system. Reliability Convolutional neural networks Driver behavior m.nitoring Vehicles

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Im.rove Sustainable Farm.ng 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International Conference on Intelligent System., Advanced Com.uting, and Com.unication, ISACC 2025

作者： Ilam.irai, R.C. Barkathulla, A. m.halakshm., G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly im.acts fruit production yield and quality. Traditional pruning techniques take a lot of tim. and m.ney because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system.intended to precisely prune fruit trees to address these issues. This system.uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud com.uting integration m.kes it possible to process and m.ke real-tim. decisions, im.roving the system.s flexibility in responding to changing tree conditions and pruning needs. The system.s prim.ry parts are robotic arm. for accurate cuts, various sensors for analyzing tree structures, and CNN m.dels for m.xim.zing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy com.ared to conventional techniques. This study dem.nstrates how cloud-integrated AI robotics can transform.agricultural practices and im.rove fruit farm.ng's sustainability and productivity. © 2025 IEEE.

关键词： Robotic arm.

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Perform.nce Investigation on Cross Layered Routing Protocol in Cognitive Radio Networks (CRN) 3

Performance Investigation on Cross Layered Routing Protocol ...

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3rd International Conference on Electronics and Sustainable Com.unication System., ICESC 2022

作者： Senthil Kum.r, N.K. Prathipa, R. Um. Priyadarsini, S. Subha, T.D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India Panimalar Institute of Technology Electronics and Communication Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering India R.M.K. Engineering College Electronics and Communication Engineering

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

The Cognitive Radio Network (CRN) has been developed to fulfill the high needs of future wireless com.unication system. (5G). Rapid use of wireless com.unication services has m.de a huge dem.nd for spectrum.and bandwidth allocation. Proper spectrum.sharing and efficient usage of spectrum.are the two ways to m.et the necessary dem.nd. This Cognitive Radio technology m.kes use of these two ways to im.rovise the bandwidth utilization and achieve better quality of services. To m.xim.ze the spectrum.utilization, the Cognitive Radio is adapted with various other com.ining techniques. By properly designed with Dynam.c Spectrum.Access (DSA), m.ltihop cognitive radio is the best approach to provide better bandwidth. This DSA addresses m.jor challenges like spectrum.awareness, quality route establishm.nt, route m.intenance and node m.bility. Gravitational Search Genetic Algorithm.(GSGA) protocol has been developed for the betterm.nt of speed of convergence without com.rom.sing the accuracy of the predictor m.del. Conventional approaches, although im.roved channel usage, lead to a rise in overhead as the num.er of nodes grows. To overcom. these drawbacks, the proposed GSGA protocol has been designed that efficiently utilizes the channel and predicts the availability of the channel. When it com.s to throughput and stability, sim.lations show that the proposed cross-layered routing protocol is superior. © 2022 IEEE.

关键词： Genetic algorithm.

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An Efficient technique for the Better Recognition of Oral Cancer using Support Vector m.chine

An Efficient Technique for the Better Recognition of Oral Ca...

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International Conference on ent Com.uting and Control System. (ICICCS)

作者： J m.nikandan Sterlin Rani Devakadacham.m. Shanthalakshm. Y Arockia Raj K Vijay Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Computer Science and Engineering R. M. D. Engineering College Kavaraipettai India Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India Computer Science and Engineering PSNA College of Engineering and Technology Dindugal India

Accuracy is am.ng the m.st im.ortant factors in a disease diagnosis. It is essential to select the characteristics that you find m.st pertinent for the highest accuracy. This study aim. to m.re accurately predict the presence of the prim.ry stage of squam.us cell carcinom. using fewer indicators. Stages of oral cancer were first dem.nstrated to be predicted by 25 features. The variety of features that are obtained from.various patient records is indirectly decreased in this study through the com.ination of the unified m.dical system.using hybrid features selection techniques to identify the characteristics that are m.st useful for the identification of oral cancer. Hybrid feature selection has been used to condense 25 qualities into 14 features. The diagnosis of patients with oral cancer is then predicted using four classifiers: Updatable Naive Bayes, m.ltilayer Perceptrons, K-Nearest Neighbor, and Support Vector m.chines. Also, the data show that, after adding feature developm.nt decisions with Sm.TE during the preprocessing phases, the support vector m.chine’s perform.nce surpasses other m.chine learning techniques.

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SYNERGY OF ROBOTICS AND IOT m.NITORING IN INDUSTRIES USING DEEP RESNETS

SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING D...

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2023 IEEE International Conference on Research m.thodologies in Knowledge m.nagem.nt, Artificial Intelligence and Telecom.unication engineering, Rm.m.TE 2023

作者： Gobinath Kum.rasam., m. Kongu Kum.r, m. Anbarasu, L. Padm.vathi, N. Kum.r, S. Sathish Ram.a, P. Dept. of Computer Science and Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India College of Engineering Tamilnadu Karur India Engineering College Tamilnadu Perundurai India Department of Electronics and Communication Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India Department of EEE Erode Sengunthar Engineering College Tamilnadu Perundurai India General Engineering- Electrical RMD Engineering College Chennai India Department of EEE M. Kumarasamy College of Engineering Karur Tamilnadu India Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305708

This paper explores the synergistic integration of robotics and Internet of Things (IoT) m.nitoring within industrial settings, em.loying Deep Residual Networks (Deep ResNets). The convergence of robotics and IoT has the potential to revolutionize various industries by enabling efficient data collection, real-tim. analysis, and inform.d decision-m.king. Deep ResNets, known for their exceptional feature extraction capabilities, further enhance the fusion of these technologies. In this study, we delve into the collaborative fram.work of robotics and IoT, elucidating how Deep ResNets bolster the process. We discuss the benefits of this integration, including optim.zed resource allocation, predictive m.intenance, and adaptable production system.. Through em.irical analysis, we dem.nstrate the efficacy of Deep ResNets in handling com.lex data from.integrated robotics and IoT sources. Ultim.tely, this research sheds light on the transform.tive im.act of am.lgam.ting robotics, IoT m.nitoring, and Deep ResNets, paving the way for intelligent and responsive industries. © 2023 IEEE.

关键词： Robotics

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A Com.rehensive Survey on Cybersecurity of Ev: Roles and Requirem.nts of Stakeholders, Gaps and an Exem.lar Future Direction

SSRN

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SSRN 2024年

作者： Kangralkar, Yash m.dhav Pattar, Santosh Bavalatti, Shradha Iranna Department of Computer Science and Engineering KLE Technological University’s Dr. M. S. Sheshgiri College of Engineering and Technology Belagavi590008 India

With the ever growing technological advancem.nts, socio-political and econom.cal changes, fueled by sustainable developm.nt goals, the autom.bile industry today is reorienting towards the Electric Vehicles (EVs). Its vision includes transportation safety, a cleaner environm.nt, and im.roved econom.c growth, am.ng others. The EVs are today equipped with onboard equipm.nt that m.kes them.progressively intellectual. Further, to support EVs, stakeholders, viz., m.nufacturers, and governing bodies have created a network of infrastructure (like charging stations, roadside com.unication devices), power grids, etc. These interconnections have created a com.lex cyber-physical association that is prone to cyber-attacks. In the recent past, several works have been carried out to address these challenges. However, there is a gap in these works towards the understanding of the roles of the stakeholders in this nexus of connected vehicles and infrastructure. In this regard, our aim.in this paper is to provide an overview of the roles and requirem.nts of various stakeholders towards a safe cyber society with respect to EVs. In addition, we conducted a com.rehensive review of existing literature to identify the gaps and put forth an exem.lary future direction through m.cro-segm.ntation of network traffic flow data to m.tigate a few of the cyber-attacks. The views presented in this work will enhance the ongoing research efforts on the cybersecurity of EVs, their com.onents, and stakeholders against cyber-attacks. © 2024, The Authors. All rights reserved.

关键词： computer crim.

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ACoCo: An Adaptive Congestion Control Approach for Enhancing CoAP Perform.nce in IoT Network 2

ACoCo: An Adaptive Congestion Control Approach for Enhancing...

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2nd International Conference on Applied Artificial Intelligence and Com.uting, ICAAIC 2023

作者： Jayaudhaya, J. Supriya, S. Kandaswam., Vijay Anand Sam.thira Pandi, V. Kam.tchi, S. Priya, C Pushpa R. M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Ramapuram India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9781665456302

The Industrial Internet of Things (IIoT) requires the real-tim. transm.ssion of critical data to ensure functionality and prevent hazardous situations. However, current data transm.ssion scheduling m.thods in 6TiSCH networks do not efficiently handle heterogeneous traffic based on its criticality and perform.nce requirem.nts, potentially leading to violations of tim.ng lim.ts. To address this issue, this paper proposes ACoCo, an Adaptive Congestion Control approach for CoAP that uses reinforcem.nt learning techniques to dynam.cally adapt congestion control param.ters based on real-tim. network conditions, node behaviors, and traffic patterns. Sim.lation results dem.nstrate ACoCo's effectiveness in reducing end-to-end transaction delay and im.roving transaction delivery ratio under congested network conditions, providing valuable insights for IoT network optim.zation and design. ACoCo operates effectively within the 6TiSCH network architecture, taking into account the scheduling function and com.unication requirem.nts of the network. © 2023 IEEE.

关键词： Network architecture

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