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3rd International Conference for Advancement in technology, ICONAT 2024

作者： rahul, S.G. rajkumar, r. Abburi, rudrakonda Sai Kumar reddy, B. Trivikram raja, C.H. Teja, K. Pavan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350354171

This paper presents the design, development, and implementation of a scalable Condition Monitoring System for controlled-environment greenhouse farming. To continuously monitor key environmental elements influencing crop growth, the system interfaces an ESP32 microprocessor with various sensors that measure temperature, water level, humidity, light intensity, and methane. The developed system prioritizes the selection of sturdy hardware parts that are specially made to endure the various circumstances found in a greenhouse. To guarantee ideal growing circumstances for crops, the gathered data can be displayed in the live IoT Blynk portal and possibly examined using the threshold values that have been established. The proposed technology provides real-time insights and data visualization into the greenhouse environment to improve agricultural productivity, yield, and resource efficiency. This project aids in outperforming conventional greenhouse systems in economic aspects by providing automation and real-time data insights for ideal plant growth. In terms of sustainable agriculture, this is a paradigm change that encourages resource optimization, decreased environmental impact, and maybe higher agricultural yields. © 2024 IEEE.

关键词： Environmental monitoring

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Performance and Analysis of Skin Cancer detection Using deep Learning 3

Performance and Analysis of Skin Cancer Detection Using Deep...

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3rd IEEE International Conference on Intelligent techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Hemanth, P. dhanujay, L. Girinadh, G. Bennet, M. Anto Subha, K.J. Jeyaraj, Josephine Pon Gloria Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai600062 India

ISBN: (纸本)9798350361186

Skin cancer is notably one of the most dangerous expressions of this ailment, originating from dNA damage in skin cells that results in the formation of genetic abnormalities or mutations on the skin. The insidious characteristic of skin cancer lies in its inclination to gradually spread to other parts of the body, underscoring the importance of early detection for successful treatment. Given the increasing prevalence of skin cancer, its elevated mortality rate, and the significant expenses associated with medical interventions, prompt diagnosis becomes imperative. Acknowledging the severity of these challenges, researchers have developed various methods for the early identification of skin cancer. Parameters such as the symmetry, color, size, and shape of lesions are utilized to distinguish skin cancer and differentiate benign cases from melanoma. This paper explores deep learning approaches applied to the early detection of skin cancer, presenting the study's findings through tools, graphs, tables, techniques, and frameworks. This enhances comprehension and contributes to the body of knowledge in this critical field. © 2024 IEEE.

关键词： diagnosis

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A KNN Algorithm Based Predictive Model for Heart disease Progression

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recent Advances in Computer science and Communications 2023年第5期16卷 76-81页

作者： Jaffrin, Lijetha C. Srikanth, G. Umarani Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Background: disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization (WHO) predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated development Environment. Experiments revealed that technique worked well and was more accurate than before. © 2023 Bentham science Publishers.

关键词： diseases

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Implementation of Smart Solar Power System Monitoring and Automated Cleaning Mechanism with Python based Embedded technology 4

Implementation of Smart Solar Power System Monitoring and Au...

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4th International Conference on Intelligent technologies, CONIT 2024

作者： dhal, P.K. reddy, C.Jaswanth Kumar, r S Aswin Sabareesh, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350349900

The research paper focuses on the design and implementation of a 55 Watts solar power monitoring and automated cleaning system integrated with Python-based embedded technology. The system aims to maximize the efficiency of solar power generation by monitoring the output of the solar panel and automatically cleaning it when necessary to maintain optimal performance. The system consists of voltage and current sensors to monitor the solar panel output, a dHT11 sensor to measure temperature and humidity. The Ldr sensor is to detect ambient light levels. The data from the sensors are processed by an Arduino Uno board and sent to a Node MCU board which communicates with a server to log and display the data on the LCd screen. The Python program running on the Node MCU board analyses the sensors and triggers an automatic cleaning mechanism using an L293 driver to move cleaning brush across the surface of the solar panel. This ensures the solar panel remains free from dust and dirt. © 2024 IEEE.

关键词： Python

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IoT Based on Non-invasive Glucometer 7th

IoT Based on Non-invasive Glucometer

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Premalatha, P. Poonguzhali, E. rekha, r. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819908370

diabetes mellitus is named the most important developer source of any kind of major disease and if it’s not treated properly then the majority of it gives severe health-related issues and leads to death. This is considered one of the non-contagious diseases, and it occurs when the sugar level within the blood increases above an average value. Therefore, regular calculation of blood glucose levels could be the condition to take care of sugar patients. Existing techniques for the determination of blood glucose levels are invasive methods that carry small single-use needles that are directly pricked into a finger to collect blood samples out of the body so it's passed to the fresh test tube for further processing chemically to see the number of sugar molecules present there. However, tolerating and reducing the patient's pain and the use of strips of those techniques have led to the process of non-invasive techniques. The near-infrared LEd and photodetector determine the sugar and insulin concentration. The attentiveness of glycemia within the blood sample is estimated by the level of infrared emission in the blood. The total concentration level of sugar molecules is on the LCd in an appropriate unit. To start the process, insert the fingertip in between the NIr-LEd and NIr-Pd. The reflected signal is fetched by the NIr-Pd. The evaluation value obtained from it'll be filtered out and then amplified. The noise has been removed by FFT. The filtered signal and amplified to attach to an NPN transistor for getting the negative value and for early showing the evaluated value within the LCd. The signal has been connected to Arduino port A0. Finally, the output value is displayed on the LCd display. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light emitting diodes

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riddance of Weed Using an Agri-robot Employed by Arduino and ESP32 CAM 15

Riddance of Weed Using an Agri-Robot Employed by Arduino and...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Vinod, dasari Naga Kamalesh, A. Bhaskar, B. Teja, B. ravi Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350370249

This article presents the enhancement of a weed detection robot designed to automate the identification and riddance of unwanted vegetation in agricultural fields. The robot integrates a suite of hardware components, including an ultrasonic sensor, Arduino microcontroller, ESP32 CAM, robotic hand, L293d motor driver, battery, power supply module, Bluetooth module, and dC motor for locomotion. The dC motor is utilized to drive the wheels of the robot, enabling it to traverse the agricultural terrain efficiently. The ultrasonic sensor facilitates obstacle detection, ensuring safe navigation through the field while avoiding collisions. Meanwhile, the ESP32 CAM captures visual data of the surroundings, which is processed by machine learning algorithms to identify and classify weeds. Upon identifying weeds, the robotic hand, controlled by the Arduino microcontroller via the L293d motor driver, is deployed to remove the unwanted vegetation. The Arduino microcontroller acts as the central processing unit, orchestrating the operation of all components and executing decision-making algorithms. Additionally, the inclusion of a Bluetooth module enables remote monitoring and control of the robot, providing farmers with real-time updates on weed infestation levels and operational status. The weed detection robot offers a solution for weed management in agriculture and obtained 97% accuracy, promising increased crop yields and reduced labor costs through efficient weed riddance. ©2024 IEEE.

关键词： dC motors

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Analyzing the downforce generated by rear spoiler of hatchback vehicle and creating an aeromapp using CFd analysis

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Materials Today: Proceedings 2023年 92卷 48-55页

作者： Amala Justus Selvam M. rajesh Kumar M. Padmanabhan S. Hitesh Sri Subramanyam K. Navneeth reddy M. Department of Automobile Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Aeromapping is a chart which shows the changes in a performance metric such as Coefficient of Lift, Coefficient of drag and balance as a function of the car attitudes. A generic four-wheeler vehicle body was designed for commercial purposes. In this study, the working principle was based on Bernoulli's principle of Fluid Flow. Laminar flow was tested on the model of the vehicle under static conditions. From the New Car Assessment Program (NCAP) standards, boundary conditions were fixed, so that the experiment can be as realistic as possible. Using ANSYS Fluent multiple simulations were done varying the experimental parameters, such as Fluid velocity, Yaw Angle of the Vehicle and varying the Fluid Pressure. All of these experiments were studied using the flow visualization, pressure graphs, graphs between independent variables etc. Successful results were achieved and the aero map was created and a significant downforce of 33% was achieved. © 2023 Elsevier Ltd. All rights reserved.

关键词： ANSYS Computational Fluid dynamics downforce drag Lift Solidworks

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Emerging Trends and Obstacles in Predicting Preeclampsia: A Comprehensive review of Machine Learning and deep Learning techniques

Emerging Trends and Obstacles in Predicting Preeclampsia: A ...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Aruna, r. Sivaranjani, S. Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522988

Preeclampsia, a serious hypertension condition during pregnancy, presents considerable hazards to both mother and fetal health, potentially resulting in catastrophic consequences if not promptly identified. Conventional diagnostic techniques depend on clinical evaluations and biomarkers, which may not consistently provide prompt or precise predictions. recently, deep learning algorithms have emerged as potent instruments for studying intricate, high-dimensional data, potentially improving illness prediction by uncovering patterns and features overlooked by traditional methods. recent research reveals the advancement of several machine learning and deep learning models for predicting preeclampsia from 2021 to 2024, employing a wide array of input data, including maternal demographics, clinical histories, and ultrasound pictures. research has indicated favorable outcomes, with these models exhibiting enhanced sensitivity, specificity, and overall accuracy relative to conventional methods. Nonetheless, obstacles persist in domains such as data integrity, model interpretability, and the applicability of findings across varied populations. This literature review highlights recent developments in deep learning methodologies for the detection of preeclampsia and examines potential enhancements for practical use. Keywords include preeclampsia, maternal demographics, clinical evaluations, biomarkers, ultrasound imaging, and fetal health. © 2024 IEEE.

关键词： Ultrasonic imaging

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Optimization of drilling Process Variables Using Taguchi technique for drilling of LM6/B4C/Fly Ash Hybrid Composites 1st

Optimization of Drilling Process Variables Using Taguchi Tec...

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1st International Conference on Energy and Materials technologies, ICEMT 2021

作者： Sarala rubi, C. Udaya Prakash, J. raj Kumar, C. Jayavelu, S. Department of Physics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789811938948

drilling is an important part of the hole-creation process. It is extremely useful in the industrial sectors for parts assembly. Hard ceramic particles were dispersed into the metallic matrix, making them difficult to machine. So, optimizing the drilling process parameters becomes unavoidable. The primary goal of this research work is to prepare LM6/B4C/fly ash hybrid composites with three, six, and nine weight % of second phase material, by stir casting method. drilling operation was carried out with three levels of feed rate (F), spindle speed (S), drill material (d), and percentage of reinforcement (r) as input process variables by Taguchi’s design of experiments approach. Optimization of drilling parameters for obtaining lowest thrust force (TF) using single objective approach. results reveal that feed rate is having greatest impact on TF followed by spindle speed, reinforcement percentage, and then drill material. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： design of experiments

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Octa multibit upset correction in SrAM memories using interleaved MPC code

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International Journal of Information technology (Singapore) 2025年 1-7页

作者： Sindhu, T.V. devi, P. Kalpana Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

In space applications, cosmic radiation-induced multiple cells upset (MCU) poses a major stability issue for static random-access memory (SrAM). during the past decade, radiation-induced MCUs have been responsible for a significant increase in the proportion of mistakes affecting multiple memory cells. This research proposes an Interleave Multibit Parity Code (IMPC) for Octa multibit upset correction in SrAM, achieving low latency and error rates. The IMPC technique predicts MCUs before decoding and corrects adjacent 8-bit errors effectively, utilizing a parity-check matrix with minimal redundancy. Additionally, the encoder is reused to reduce overhead without interfering with the encoding and decoding process by reducing the area overhead of additional circuits. With the 45-nm library, encoders and decoders can be constructed with a minimal amount of area and delay overhead. Experimental results shows that the proposed code is more area-efficient, faster, and more power-efficient than current ECC codes. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Error correction codes Multiple cell upset Parity code SrAM memory

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