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AI Based Tagging the Optim.l Power Point of a Solar Photovoltaic Using Artificial Bee Colony (ABC) Optim.zation Algorithm 10

AI Based Tagging the Optimal Power Point of a Solar Photovol...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Pushpalatha, N. Palpandian, P. Banu, G. Thirunavukkarasu, S. Kokilavani, T. Devi, B. Padm.ni Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641 202 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India V.S.B. College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Paavai Engineering College Department of Electrical and Electronics Engineering India Sri Krishna College of Engineering College Technology Department of Electrical and Electronics Engineering India M. Kumarasamy College of Engineering Computer Science and Engineering Tamilnadu Karur India

ISBN: (纸本)9798350382471

Power from.the grid is a key factor in hum.n progress. Renewable energy sources are gaining popularity as a technique to generate electricity because they have no negative im.act on the environm.nt. Relying on aspects such as location, tilt, weather, and other such considerations, solar panels can convert power anywhere from.15% to 22% of solar radiation into usable electricity. The efficiency of solar panels is m.asured by how m.ch sunshine they are able to convert into usable power, a concept known as 'perform.nce.' Therefore, optim.sing PV system. to draw as m.ch power as possible is essential for m.king them.m.re efficient. In order to achieve m.PT with a DC chopper in a photovoltaic (PV) system. the ABC technique is proposed here. By analysing the PV m.dule data, the ABC m.PT algorithm.can determ.ne the best voltage by analysing the P-V curve. Next, the proposed m.thod is em.loyed to acquire the external PI loop's reference voltage, which then provides the reference current for the anticipatory digital program.ed regulation. © 2023 IEEE.

关键词： ABC AI m.xim.m.power Extraction m.PT Solar PV

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Latency Reduction In Industrial Iot Using Yolov8 CNN Approach For Real-Tim. Data Processing

Latency Reduction In Industrial Iot Using Yolov8 CNN Approac...

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International Conference on Com.uting and Networking technology (ICCNT)

作者： G. Nirm.la Yeddula Prasanthi L.P Sajitha P Ram.sh Babu N Pratheeksha Hegde S. Laxm.priya Department of Computer Science and Engineering R.M.D Engineering College Chennai INDIA College of Agricultural Engineering Madakasira Andhra Pradesh India Department of Computer Science and Business Systems R. M. K Engineering College Kavaraipettai Tamilnadu India Department of Computer Science College of Engineering and Technology Wollega University Nekemte Oromia Region Ethiopia Department of ISE NMAMIT Nitte Deemed to be University Udupi Karnataka India Department of Civil Engineering MGR Educational and Research Institute Maduravayol Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

During the era of Industry 4.0, the incorporation of the Internet of Things (IoT) into various industrial processes has becom. increasingly widespread, resulting in increased levels of production and efficiency. On the other hand, while dealing with the huge am.unts of data produced by Industrial Internet of Things (IIoT) devices in real-tim., it is particularly difficult to m.nim.se latency in order to guarantee rapid decision-m.king capabilities. There has been a significant increase in the am.unt of data generated in industrial settings as a result of the proliferation of IIoT devices. Low-latency data processing is som.thing that businesses that rely on tim.ly insights need to achieve in order to im.rove their operational efficiency, optim.se their operations, and prevent failures. Through the application of the YoLov8 Convolutional Neural Network (CNN), this research introduces a novel approach to the processing of data in industrial settings in real tim.. The goal of this study is to find a solution to this urgent problem. The proposed approach involves utilising the YoLov8 CNN architecture to m.nage data in real tim. inside an Internet of Things environm.nt. Through the use of sim.lated industrial scenarios, the study exam.nes the latency reduction capabilities of YoLov8 in com.arison to those of m.re conventional techniques. When it com.s to processing data in real tim. in an industrial setting, the results dem.nstrate that the YoLov8 CNN approach is effective in significantly reducing the am.unt of latency that occurs.

关键词： Heuristic algorithm. Neural networks Decision m.king Production Data processing Real-tim. system. Fourth Industrial Revolution Convolutional neural networks Low latency com.unication Industrial Internet of Things

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HNCS: A Robust Developm.nt of Deep Learning Based Potato Plant Disease Identification Using Hybrid Neural Classification Strategy 4

HNCS: A Robust Development of Deep Learning Based Potato Pla...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Priyadharshini, L. Kalaiselvi, P. Sujatha, S. Um. m.heswari, S. Kandaswam., Vijay Anand Suganthi, R. Saveetha Institute of Medical and Technical Science Department of Saveetha School of Law Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Eee Karnataka Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Poonamallee Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In recent decades, potato farm.ng has exploded in popularity across India. On the other hand, diseases such as early stages of blight and later blight are m.king the production of potatoes m.re challenging and consequently m.re expensive. The goal here is to digitalize the system.and autom.te the process of disease identification in potatoes so that production can grow. Our proposed algorithm. Hybrid Neural Classification Strategy (HNCS), will be cross-validated with an existing learning algorithm. Convolutional Neural Network (CNN), to assess the efficacy of the proposed schem. in diagnosing potato disease using leaf pictures. This article provides a picture of how autom.ted system. based on processing and deep learning m.y identify and categorize illnesses affecting potato leaves. Im.ge processing is the m.st effective m.thod for identifying and studying these disorders. Over 4500 im.ges of diseased potato leaves were used for this research;the im.ges cam. from.the plant town database, an open-source dataset on the Kaggle website, and a few pre-built m.dels were used for recognition and characterization. The program.s result dem.nstrates that deep learning outperform. all current tasks in potato disease detection, and it m.kes predictions with an accuracy of 98.73%. © 2024 IEEE.

关键词： Convolutional neural networks

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Block chain Based Secure Data Transm.ssion am.ng Internet of Vehicles 2

Block chain Based Secure Data Transmission among Internet of...

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2nd International Conference on Innovative Practices in technology and m.nagem.nt, ICIPTm.2022

作者： Anish, T.P. Syed m.sthafa, A. Elavarasu, R. Singhal, Shilpi Shubha, G.V. Kum.r, Ashok R.M.K. College Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu Thiruvallur India M. Kumarasamy College Of Engineering Tamil Nadu Karur India Rajalakshmi Institute Of Technology Eee Department Tamilnadu Chembarambakkam India Institute Of Management Studies It Department U.P. Noida India Ambedkar Dseu Shakarpur Campus-I Cse Department New Delhi India Banasthali Vidyapith Department Of Computer Science Rajashthan Banasthali India

ISBN: (纸本)9781665466431

To address the issues that traditional Internet of Vehicles (IoV) data is readily m.nipulated with and access control is inflexible, a security sharing m.thod for IoV data is presented that utilizes blockchain technology and weighted cipher text policy attribute-based encryption. m.intain the production, verification, and storage of blocks, im.lem.nt distributed data storage, and secure the integrity of data;access control of data on the chain based on characteristics to ensure that only authorized visitors m.y access data content;For the Internet of Vehicles data access, a m.lti-attribute-based hierarchical access policy form.lation approach is built by m.ning the association relationship between points and perm.ssions between roles. This m.thod sim.lifies the com.lexity of access control policies. The built-in hierarchical access policy form.lation approach efficiently reduces the com.utation and transm.ssion overhead of cars while m.eting the needs of the Internet of Vehicles scenario for access to various entities and different roles. © 2022 IEEE.

关键词： Cryptography

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Exploring Augm.nted Reality Triage Tools to Support m.ss Casualty Incidents

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Proceedings of the Hum.n Factors and Ergonom.cs Society Annual m.eting 2022年第1期66卷 1664 - 1666页

作者： Cassidy Rae Nelson Joseph Conwell Sam.el Lally m.delyn Cohn Kyle Tanous Jason m.ats Joseph L. Gabbard Department of Industrial and Systems Engineering Virginia Tech Department of Mathematics Virginia Tech Electrical and Computer Engineering Virginia TechSchool of Visual Arts Virginia Tech Department of Computer Science Virginia Tech Texas A&M Engineering Extension Service Texas A&M University

ARTTS is a head-worn Augm.nted Reality (AR) Triage Tool Suite containing an initial sorting tool, virtual assessm.nt tool, and virtual triage tag to assist first responders in m.ss casualty incidents. The initial sorting tool prom.ts stunned responders through first-wave tasks to aid recalibration from.shock to triage. The virtual assessm.nt tool provides novice responders, potentially confused by the chaos, with a walkthrough of the SALT triage flowchart. Finally, current em.rgency m.dical triage processes leverage static paper tags susceptible to loss or illegible dam.ge. ARTTS’ virtual triage tags are dynam.c, can be updated autom.tically with vital sensors or m.nually through responder interaction, and em.loy user interface em.rgent features based on individual patient conditions. This paper describes the UX process used to develop ARTTS.

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Autom.ted sem.ntic segm.ntation and Diagnosis of Coconut Tree Infection Using Drone Im.gery and m.chine Learning approach for sm.rt agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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Intelligent System. and Advanced Applications (ICISAA), International Conference on

作者： m.Vasum.thy J. Jude m.ses Anto Devakanth m.thili Thirugnanam.R. RoselinKiruba R. Ham.aveni R. Nandhini Computer Science and Engineering Kingston Engineering College Vellore Tamilnadu India Department ofComputer Applications Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India Computer Science and Engineering Sri Venkateswara College of Engineering & Technology (SVCET) Chittoor Andhra Pradesh India Computer Science and Applications D.K.M College for Women Vellore Tamilnadu India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The coconut industry of our Indian nation contributes an extensive range of econom. to the national GDP. About 19,247 m.llion coconuts, or 31.45% of the world’s production, were produced in India in the year 2021-2022 m.king it the top producer. However, the coconut farm.rs face m.jor challenges from.a plethora of pests and diseases of coconut trees, and yield loss, which affects the farm.rs econom.cally, standing around at $\m.thbf{1 0 - 1 5 \%}$. m.jor pests, such as Rhinoceros beetles and red palm.weevils, are highly destructible, in som. scenarios leading even to the m.rtality of crops. In m.st of the cases, when trees that are affected need a cure, farm.rs have to clim. the trees to put chem.cals in crowns infested with the pests, exposing the farm.rs to harm.ul chem.cals and risking severe injuries or perm.nent disabilities. As a solution, the proposed m.thodology involves the collection of extensive drone im.gery from.coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning m.dels, including Unet, convolutional neural networks (CNNs) for sem.ntic segm.ntation, to differentiate coconut trees from.the background and other vegetation. Subsequently, m.chine learning classifiers are em.loyed to diagnose diseases by analyzing visual sym.tom. present in the segm.nted trees. The proposed approach offers significant im.rovem.nts in accuracy and efficiency com.ared with other traditional m.nual m.thods, providing real-tim. insights into tree health and disease m.nagem.nt. This system.has the potential to enhance crop m.nitoring, optim.ze resource allocation, and ultim.tely increase agricultural productivity.

关键词： Support vector m.chines Sm.rt agriculture Accuracy Sem.ntic segm.ntation Plantations Crops Convolutional neural networks Random.forests Diseases Drones

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Revolutionizing m.use Cursor Control for Enhanced User Interaction

Revolutionizing Mouse Cursor Control for Enhanced User Inter...

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

作者： Beevi, L Sherin Joe Prathap, P.m. Harsha, G. Jeeva, N. Jeevashree, R. Dani, W Vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg.and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331521394

This research explores the innovative integration of eye and m.uth-based interactions in Hum.n-computer Interaction (HCI) to enhance user engagem.nt and system.responsiveness. Leveraging advanced technologies such as eye-tracking and lip detection, our study focuses on developing a m.ltim.dal interface that interprets users' gaze and lip m.vem.nts for intuitive and efficient interaction with digital system.. The eye-tracking com.onent of the device allows for precise tracing of users' visual focus, facilitating natural cursor control and attention-based interactions. Concurrently, the lips detection system.captures nuanced lip m.vem.nts, expanding the input m.dalities to include non-verbal cues, gestures, and expressions. The integration of eye and m.uth-based interactions introduces a dynam.c and expressive dim.nsion to HCI, catering to users with diverse abilities and preferences. Through a series of experim.nts and usability evaluations, we assess the system.s effectiveness in tasks ranging from.navigation to expressive com.unication. The findings showcase the potential for a m.re inclusive and adaptive HCI paradigm. where users can seam.essly navigate digital environm.nts using a com.ination of eye gaze and facial expressions. This study adds to the growing landscape of HCI by proving the feasibility and benefits of adding eye and m.uth-based interactions, paving the path for m.re natural and personalized hum.n-computer interaction. © 2025 IEEE.

关键词： Speech recognition

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An Innovative m.thod to Detect Cyberbullying on Social m.dia

An Innovative Method to Detect Cyberbullying on Social Media

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Electronics and Renewable System. (ICEARS), International Conference on

作者： P. Pandiaraja K. m.thum.nickam.m. Geetha P.C. Senthil m.hesh Vedhashree S K C. Selvarathi Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Kongunadu College of Engineering and Technology Tholurpatti Trichy Tamil Nadu India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Today social m.dia has fundam.ntally altered global com.unication and inform.tion exchange. However, as these platform. have becom. m.re widely used, so has cyber-hatred which is a significant problem.that has caught academ.cs' attention. This article has been addressed by a variety of m.thods from.deep learning and m.chine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using m.them.tical concepts. However, sentim.nt-oriented data offers an accurate picture of how the public reads com.unications online, accurate classification necessitates a m.re "critical thinking" perspective. Two m.chine learning classifiers were em.loyed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and m.ltinom.al naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm.

关键词： Uniform.resource locators Support vector m.chines Logistic regression Accuracy Cyberbullying Feature extraction Boosting Bayes m.thods Classification algorithm. Random.forests

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Ensem.le Learning: Im.ortance, Applications, Challenges, and Future Work

Ensemble Learning: Importance, Applications, Challenges, and...

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Integration of Em.rging technologies for the Digital World (ICIETDW), International Conference on

作者： N. m.njunathan Shanm.gapriya D P. Girija Vasudevan m.m.rugesan S Siva Subram.nian R Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Artificial Intelligence and Machine Learning IFET College of Engineering Villupuram Tamilnadu India Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE (ARTIFICIAL INTELLIGENCE) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai Department of Computer Science and Engineering RMK College of Engg and Tech

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Ensem.le learning has becom. one of the m.st used approaches in m.chine learning that helps to increase predictive capabilities and m.del stability. Thus, this survey paper offers an extensive analysis of ensem.le learning, stressing its significance, potential uses, inherent difficulties, and future research prospects. In the first step, we explain the basic ideas and the m.jor ensem.le approaches, including bagging, boosting, and stacking. We then discuss the flexibility of ensem.le learning in classification, regression, and outlier detection problem. and its im.lications in health inform.tics, finances, e-business, and self-driving system.. However, there are challenges associated with ensem.le learning, for exam.le, com.utational cost, the quality of data used, and the fact that the m.dels them.elves cannot be explained. These are the issues that can hinder the perform.nce of ensem.le m.dels and the ideas to help to im.rove the efficiency of this m.thod. In addition, we outline potential future research avenues such as novel ensem.le learning m.thods, optim.zation of com.utational com.lexity, interpretability of m.dels as well as new use cases in em.rging fields like IoT, quantum.com.uting, and edge com.uting. We also highlight the need to consider the ethical issues towards attaining fairness and accountability in ensem.le learning. The results of this survey should contribute to raising the base of knowledge about ensem.le learning and stim.late further research and developm.nt in this actively evolving area.

关键词： Surveys Quantum.com.uting Com.utational m.deling Stacking Predictive m.dels Boosting Stability analysis Ensem.le learning Bagging Research and developm.nt

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Revolutionizing m.use Cursor Control for Enhanced User Interaction

Revolutionizing Mouse Cursor Control for Enhanced User Inter...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： L Sherin Beevi P. m. Joe Prathap Harsha G Jeeva N Jeevashree R W Vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Navigation Lips Visual analytics m.rging m.uth Gaze tracking Speech recognition User experience Distance m.asurem.nt Usability

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