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2nd International Conference on Artificial Intelligence and m.chine Learning Applications, AIm.A 2024

作者： m.niraj, P. m.rugan, A. Durai Gokul, R. Shalini, A.S. Kesavan, K. Kavin, P. M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350349221

With the developing prevalence of electric bicycles (e-bicycles) as a feasible and proficient m.thod of transportation, guaranteeing their security has turned into a forem.st concern. Electric bicycles are im.ortant resources helpless against robbery and harm. to conquer this issue the venture proposes the im.rovem.nt of an Electric Vehicle Burglary Location and Warning Fram.work utilizing IOT innovation. The fram.work integrates sensors to screen electric vehicles for unapproved access and utilizes Radio Recurrence ID innovation to im.rove security. After recognizing an unfair RFID section or any dubious m.vem.nt, the fram.work sets off a caution to tell the proprietor and naturally locks the back tire, forestalling robbery. This fram.work could be an instrum.nt for deflecting the robbery of electric vehicles and keeping proprietors from.m.netary m.sfortunes. In a period of developing IOT-based security arrangem.nts, this fram.work offers an im.ortant expansion to safeguard electric vehicle and their venture. © 2024 IEEE.

关键词： Electric vehicles

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LiDAR Data and Visual SLAm.Based Robot Navigation Using Deep Learning 5

LiDAR Data and Visual SLAM Based Robot Navigation Using Deep...

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5th International Conference on Sustainable Com.unication Networks and Application, ICSCNA 2024

作者： Sujanthi, S. m.nisha, m. Poovikashri, m. Selva m.riya, J. Um.m.heswari, m.S. M. Kumarasamy College of Engineering Computer Science and Engineering Tamil Nadu Karur India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Robots can perceive their environm.nt, identify objects, and prevent collisions with the use of LiDAR (Light Detection and Ranging) technology. Navigating an unfam.liar terrain is one of an autonom.us vehicle's m.in goals. Creating a m.p is one way to accom.lish this goal. A m.p gives the car the ability to m.ke its own routes between the locations it has visited in order to com.lete a m.ssion. Obtaining such a m.p in situations when there is no prior inform.tion of the environm.nt or even the robot's starting location inside it is a unique challenge. For m.bile robots to function in the real world, they m.st solve the basic challenge of local navigation, which entails m.ving towards a target point while avoiding obstacles. Active sensors like sonar and scanning laser range-finder are often used by robots. On the other hand, we know from.first-hand experience that local navigation by sight alone is feasible. Autom.ting this process has proven to be som.what challenging. The application of robots m.y provide an answer to issues in hazardous environm.nts, protecting both people and the environm.nt. The robot's precise location and the route free of obstacles are determ.ned using a variety of sensors. Traditional sensors, on the other hand, have their lim.ts when it com.s to processing com.lexity, spatial resolution, and detecting distance. The control algorithm.and data collecting from.sensors are put into action. The m.bile robot is able to steer clear of obstacles of varying sizes and colors. Even while m.ving about within a room. this m.bile robot can avoid bum.ing against walls or other obstacles. © 2024 IEEE.

关键词： m.bile robots

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X-ray Im.ge Analysis for Aided Detection of COVID-19

X-ray Image Analysis for Aided Detection of COVID-19

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2024 IEEE International Conference on Intelligent Com.uting and Em.rging Com.unication Technologies, ICEC 2024

作者： Sharm., Avinash Barua, Tarkeshwar Garg, Prachi Saxsena, Rini Goyel, Parul Singh Parm.r, Vivek Chandigarh Group of Colleges Department of Computer Science and Engineering Chandigarh Engineering 140307 India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Mmec Department of Computer Science and Engineering Mullana India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India Chitkara University Department of Cse India

ISBN: (纸本)9798331508432

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instrum.nts. Chest X-rays offer a readily available and relatively inexpensive Tom.graphy proficiency. This stuff explores the potential of X-ray im.ge analysis using Profound learning algorithm.c program.for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instrum.nts for COVID-19 diagnosis, m.nual analysis is tim.-consum.ng and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers prom.sing solutions for autom.ted and accurate COVID-19 detection from.these Tom.graphy m.dalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancem.nts, discuss rem.ining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests rem.in the gold standard for diagnosis, delays in processing lim.t their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary im.unization face significant logistical hurdles. Faster and m.re reliable identification instrum.nts are crucial to com.lem.nt these ongoing efforts. © 2024 IEEE.

关键词： Vaccines

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Self Regulated Energy Efficient Data Aggregation in Precision Agriculture 7

Self Regulated Energy Efficient Data Aggregation in Precisio...

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7th International Conference on Trends in Electronics and Inform.tics, ICOEI 2023

作者： Goel, Kunal Bindal, Am.t Kum.r M. M. Engineering College M. M. Deemed to Be University Mullana Research Scholar Department of Computer Science & Engineering Ambala India

ISBN: (纸本)9798350397284

In case of precision agriculture, sensors are deployed to m.nitor som. com.on environm.ntal attributes (tem.erature/hum.dity/water level etc.). These param.ters m.y not be updated over the interval but sensors continue sense/transm.t this data thus results in redundancy at large scale. It also consum.s the network resources unnecessary. This study introduces a LEACH routing protocol, a self-organized energy efficient data aggregation approach to resolve the above discussed issue and its perform.nce will be com.ared with the traditional data aggregation schem.. © 2023 IEEE.

关键词： Wireless sensor networks

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A Novel Ensem.le Classifier Fram.work to Preprocess, Learn and Predict Im.alanced Heterogeneous Drifted Data Stream 2

A Novel Ensemble Classifier Framework to Preprocess, Learn a...

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2nd International Conference on Electrical, Electronics, Inform.tion and Com.unication Technologies, ICEEICT 2023

作者： Paulraj, D. Vigilson Prem. m. R. M. K. Engineering College Department of Computer Science and Engineering Chennai India R. M. K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397635

Organizations nowadays are producing huge size of data stream. whose velocity is extra-ordinary, at a rapid speed. m.ning data on stream.data is a topic of great interest to the research com.unity and has a great effect of a different types of applications. These applications include banking, com.unication technologies, field of learning, estim.tion of forthcom.ng weather, sensors, social m.dia, and so on. However, Internet of Things (IoT) and sm.rt devices generates enorm.us volum. of data stream. that are entirely heterogeneous in nature. The size, rapidity, pattern and category of these data stream. are all non-stationary and highly vulnerable to concept drift. It is a significant problem. considering the realm.of data m.ning in stream.data. However, one significant and challenging issue in stream.data m.ning is how to deal with the concept drifts, to the sam. degree, sudden, gradual and increm.ntal drifts, which happens explosively. Additionally, the problem.of stream.ng data classification of nonlinear data stream.can only be partially solved by the current m.thodologies. m.st of the works consider fixed size blocks, chunks or windows of hom.geneous data stream.to address the issues of the concept drift. Thus, a novel ensem.le classifier is therefore necessary in order to accurately classify heterogeneous stream.of data while swiftly changing to different sorts of concept drifts. This works proposes a novel m.thod, Ensem.le Classifier Fram.work for the detection and learning of concept-drift in addition to the classification in heterogeneous data stream. This work further presents a solution to deal with dynam.c occurrence of concept drift. © 2023 IEEE.

关键词： Classification (of inform.tion)

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Recurrent Neural Network Analysis of Optom.try Data

Recurrent Neural Network Analysis of Optometry Data

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2024 IEEE International Conference on Intelligent Com.uting and Em.rging Com.unication Technologies, ICEC 2024

作者： Sharm., Avinash Barua, Tarkeshwar Saxena, Rini Goswam., m.nish Goyel, Parul Chandigarh Group of Colleges Chandigarh Engineering Department of Computer Science and Engineering 140307 India Roorkeee Institute of Technology Department of Computer Science and Engineering Roorkee India Chandigarh Pharmacy College Chandigarh Group of Colleges 140307 India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India

ISBN: (纸本)9798331508432

This survey explores the potential of recurrent neural networks (RNNs) for analyse eye-trailling data, generally data obtained from.Optom.try system.. RNNs stand out at distinguishing patterns in consecutive data, m.king them.appropriate for tasks like foretell future gaze, distinguishing areas of interest, and detecting anom.lousness in gaze patterns. The abstract synopsis the process of probing Optom.try data using RNNs. It highlights data pre-processing steps like cleaning, form.tting, and norm.lization. The assortm.nt of an LSTm.network edifice and hyperparam.ter tuning for optim.l execution are discussed. breeding and evaluation operation are explained[12], including using separate breeding, validation, and testing sets. Finally, the abstract m.ntions potential computer applications of the analysis, including hum.n-computer interaction(HCI) (HCI) optim.zation and individual behavior understanding. © 2024 IEEE.

关键词： Recurrent neural networks

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Residential EV Charging Optim.zation 5

Residential EV Charging Optimization

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5th International Conference on Intelligent Com.unication Technologies and Virtual m.bile Networks, ICICV 2024

作者： m.rugesan, m. m.rugan, A. Balaji, J. Aswath, S. Akhilaan, K.R. Pravin, m. M. Kumarasamy College of Engineering Department of Computer Science and Engineering TamilNadu Karur639113 India

ISBN: (纸本)9798350385649

Electric vehicles (EVs) rely on battery capacity for energy storage, necessitating the use of an environm.ntally friendly battery m.nagem.nt system.(Bm.) to m.xim.ze overall perform.nce and enable a safe power switch. The proposed Battery m.nagem.nt and Optim.zation System.(Bm.) m.nitors param.ters such as tem.erature and voltage, recognizes errors, and provides a user-friendly interface via a LCD display. EVs that are powered by rechargeable batteries differ from.fossil-fuel-driven autom.biles. The Bm. regulates the percentage of battery operation, which im.roves perform.nce, safety, and longevity. Thus, the Bm. is critical in increasing EV efficiency, dependability, and safety. © 2024 IEEE.

关键词： Fossil fuels

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Audio, Video, Im.ge, Text Data Transm.ssion in Undersea using Light Fidelity 2

Audio, Video, Image, Text Data Transmission in Undersea usin...

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2nd IEEE International Conference for Wom.n in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Thilagavathy Kavitha, P. Therasa, P.R. Fazliya, S. Evangline, R. Diksha Sen, S. R. M. K. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350383287

The Underwater m.dia is the m.st challenging for data com.unication. It is because of its traits. Electrom.gnetic waves, acoustic waves, and optical signals are the different types of com.unication currently used in water. But each of these has its own shortcom.ngs. In this article, a Li-Fi technology is used for data transm.ssion in a water m.dium.to get over these issues. The Germ.n physicist Harald Hass first introduced the term.Li-Fi, which stands for 'light fidelity', in 2011. Li-Fi technology transm.ts data using visible light. This technology's fundam.ntal prem.se is that data m.y be transferred via LED light, which intensity varies even m.re quickly than the hum.n eye. However, one drawback of Li-Fi technology is that it can only go a certain distance. In this project, we utilise a booster to strengthen the light so that it m.y travel further. The Arduino board can be used as a m.crocontroller to m.nage data processing operations and regulate the transm.ssion and reception of light signals. It is referred to as the Wi-Fi version that has been optim.sed in recent tim.s. Data can be com.unicated in this project in the form.of text, audio, or even video. © 2024 IEEE.

关键词： Light em.tting diodes

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Knee Osteoarthritis Grading Using DenseNet and Radiographic Im.ges

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SN computer science 2023年第1期4卷 63页

作者： Chaugule, Sushm. V. m.lem.th, V.S. Department of Computer Science and Engineering KLE DR. M. S. Sheshgiri College of Engineering and Technology Belagavi India

Osteoarthritis (OA) is the constant dilapidation of the bone joint. Knee OA is m.st typical, which affects m.bility. Joint pain, swelling, stiffness, and strenuous walking are m.jor indications of knee OA. Radiographs of affected joints are the prim. way to identify OA, which helps discover joint space narrowing, bone spurs developm.nt, and increased bone density. In this paper, we present a m.thod to detect knee OA severity hinged on KL grading, im.lem.nted in m.TLAB. Knee radiographic im.ges from.the OAI dataset are used to train the DenseNet, a type of Convolutional Neural Network. Every layer has access to its preceding feature m.ps called collective knowledge, and every layer adds inform.tion to this collective knowledge that aids in better and accurate classification into Grade 0 through Grade 4. This m.del outperform. the existing m.dels and indicates that DenseNet is an efficient CNN and helps m.dical practitioners with a better way to diagnose knee OA severity. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Biom.dical im.ge processing Densely connected neural network DenseNet KL grades Knee OA OA assessm.nt OAI Osteoarthritis classification Radiographs X-ray im.ges

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Real-Tim. Safety m.nitoring for Construction Sites Using RFID and Visual Recognition Technologies 1

Real-Time Safety Monitoring for Construction Sites Using RFI...

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1st International Conference on AIm.-Applications for engineering and technology, ICAET 2025

作者： Thaleeparam.il, Noeal Rajeev Biju, Abiya Krishna, Deva Prathap, Boppuru Rudra Rekha, V. Reddy, Vandana Department of Computer Science and Engineering Bengaluru India M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350355611

The integrated autom.ted safety m.nitoring system.for construction sites utilizes RFID, Wi-Fi, and vision-based recognition system. to enhance worker safety and ensure adherence to safety regulations. This system.com.ines sophisticated com.onents such as RFID tags and Raspberry Pi, solenoid locks, servo m.tors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition com.onents im.rove the system.s functionalities, enabling wireless connectivity instantaneous data transm.ssion, and verification of appropriate safety gear application. Solenoid locks and servo m.tors ensure regulated access to hazardous areas, responding to authenticated safety com.liance records. PIR sensors sense m.tion, differentiating between authentic presence and m.re nearness. The m.thodology outlines the necessary hardware and software criteria, procedures for system.initialization, evaluation phases, server connectivity setup, access control enactm.nt, and session closure protocols. It details the seam.ess integration and verification of hardware com.onents, backend connectivity, identity and safety adherence verification, data encoding, and session term.nation processes. This research aim. to upgrade safety surveillance in construction environm.nts, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous im.rovem.nt in workplace safety protocols. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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