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检索条件"机构=RandD Institute of Science and Technology"

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Machine Learning Algorithm for Predicting Soil Classification using Smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (IoT) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (IoT) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector Machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (IoT) technology. By leveraging these technologies, the research a

关键词： Soils

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Predicting Fish Diseases Using Machine Learning Based on Water Quality Parameters 15

Predicting Fish Diseases Using Machine Learning Based on Wat...

引用

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

作者： Rajkumar, R. Rahul, S.G. Balpunuri, Saketh Reddy Nallagatla, Vishnu Sai Akula, Lokesh Nandhini Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350370249

In aquaculture, detecting fish diseases early is crucial for farmers to prevent outbreaks and financial losses. These diseases, often caused by viruses and bacteria, disrupt water quality parameters, leading to fish mortality. Inspired by recent successes in machine learning, this study utilizes cutting-edge algorithms to swiftly and accurately detect and predict water quality degradation. This research proposes the use of machine learning techniques to enable early identification of diseases in fish populations by predicting deteriorating water quality conditions. The aim is to provide stakeholders with a system that allows them to take preventive actions before disease outbreaks occur. The study evaluated the performance of several machine learning models, including Logistic Regression, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), and Random Forest classifiers, in predicting disease occurrences. Among these techniques, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving 98.97% correct classification of diseased and non-diseased cases. This integration of machine learning enhances aquaculture management, safeguarding against economic losses and fostering sector resilience. A web application utilizing machine learning predicts fish disease outbreaks based on real-Time water quality data, providing early warnings to aquaculture farmers. It offers proactive measures to mitigate risks and promotes sustainable aquaculture practices. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimal Bidirectional Gated Recurrent Neural Network Model for Crop Yield Prediction 5

An Optimal Bidirectional Gated Recurrent Neural Network Mode...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Thangaselvi, R. Sathish, M. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Chennai India

ISBN: (纸本)9781665474672

Crop yield forecasting acts as a vital part of farm management, domestic food supply, global food trade, ecological sustainability, etc. Several features which affect the crop yield are plantation region, competence of irrigation system, variation in rainfall and temperature, soil quality and fertilization, and diseases. An automated crop yield prediction model using machine learning and deep learning approaches is necessary for effective decision making in agriculture. In this view, this study designs an optimal bidirectional gated recurrent neural network (OBGRNN) based crop yield prediction model. The goal of the OBGRNN technique is to forecast the crop yield using histoiical farming data. The OBGRNN technique performs two processes namely prediction and parameter optimization. At the initial stage, the BGRNN technique is applied for the actual prediction of the crop yield. Besides, in the next stage, the Differential Evolution (DE) is applied for the effectual tuning of the parameters involved in the BGRNN technique. The OBGRNN technique involves two processes namely prediction and parameter optimization and helps to improve the performance. A series of simulations were performed on the benchmark test dataset and the experimental values reported the better performance over the other techniques. © 2023 IEEE.

关键词： Forecasting

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Fenugreek Spinach Growth through Controlled Hydroponics and Real-Time Monitoring 2

Fenugreek Spinach Growth through Controlled Hydroponics and ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rahul, S.G. Rajkumar, R. Gadde, Mukesh Narayana Rupa Sree Reddy, B. Afrin Sultana, S.K. Harshavardhan Reddy, S. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350327533

This study showcases the innovation in hydroponic cultivation, specifically focusing on Fenugreek Spinach, a valuable leafy vegetable. The unique aspect of this research lies in the integration of a hydroponic system with real-time monitoring through ThingSpeak, enabling precise control over essential growth parameters like pH levels, temperature, water level, and moisture content. Maintaining the pH within the ideal range of 6.0 to 7.0 was crucial for optimal nutrient availability, fostering robust plant growth. A controlled temperature range of 26°C to 31°C provided an environment conducive to healthy plant development. Effective management of water level and moisture, facilitated by ThingSpeak, played a pivotal role in averting waterlogging and maintaining appropriate moisture levels in the growth medium. The successful cultivation of Fenugreek Spinach in this controlled hydroponic environment underscores the potential for resource-efficient utilization, reduced water consumption, and consistent year-round cultivation, independent of external climate conditions. This study establishes a foundation for future research and advancements in hydroponic methodologies across various crops, promoting sustainable agricultural practices and addressing constraints linked to traditional soil-based farming. Through technological integration and growth condition optimization, hydroponics emerges as a promising solution for sustainable food production, significantly bolstering global food security. © 2024 IEEE.

关键词： Arduino (ATMega328P) Hydroponics Sensors Think and speak

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Analysis of Impacts of Various Masks on Human Health using IoT Device 5

Analysis of Impacts of Various Masks on Human Health using I...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Vijayalakshmi, S. Paramasivam, A. Khanal, Riddhi Kamar, Ridakordor Lyngdoh, Lawanbha Banu, N.M. Masoodhu Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665474672

In general, the protective masks are used to lower the chances of the virus entering our respiratory system. Nowadays, wearing face masks is highly recommended in crowded places. People have been wearing different types of masks such as N95, surgical masks, cloth masks etc. to protect themselves from airborne foreign bodies. In this work, an Internet of Things (IoT) based human vital sign monitoring system is designed and developed. Further, the impacts of various masks such as cloth masks, surgical masks and N95 masks on human health are analyzed. Results demonstrate that the wearing of cloth mask impacts more on human SpO2 levels when compared to the other masks namely surgical masks and N95 masks. Also, it is seen that the wearing of various masks doesn't affect the human heart beat rate. © 2023 IEEE.

关键词： Internet of things

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GreenGuard: A Fusion Approach for Plant Leaf Disease Detection Using Deep Learning Architecture 2

GreenGuard: A Fusion Approach for Plant Leaf Disease Detecti...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Senthil Kumar, N.K. Amudha, S. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science Chennai Avadi India

ISBN: (纸本)9798350314168

In Indian Cuisine Tomatoes plays a substantial role on the everyday platter. India is one of the largest producers of tomatoes in the world, nevertheless, the stature and volume of tomato yield befall accompanying numerous recent diseases. Tomatoes may help fight various diseases in human health but it is appropriate to save tomatoes from getting affected by a variety of diseases. Existing CNN models are good in disease classification, still, accurate disease detection with minimal computational complexity is a challenging task. A fusion-based voting strategy is performed to propose a novel disease detection technique with optimized deep learning for enriching the detection accuracy. A deep learning-based CNN model including EfficientNet, ShuffleNet, and SqueezeNet categorizes the diseases by using the following iteration, Identification of plant leaf images, pre-Treatment of images, segmentation, feature extraction, and categorization of different diseases based on the voting strategy. The effectiveness of this study is assessed using various evaluation metrics and among the CNN models evaluated, EfficientNet achieved the highest accuracy at 98.74%, followed by SqueezeNet at 97.39%, and ShuffleNet at 95.86%. Enabling farmers to achieve bountiful crop yields and enhance food production, this research assists in early disease detection in plants with minimal computational complexity. © 2023 IEEE.

关键词： Computational complexity

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

Analyzing the downforce generated by rear spoiler of hatchba...

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4th International Conference on Smart and Sustainable Developments in Materials, Manufacturing and Energy Engineering, SME 2022

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 RandD 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. Copyright © 2023 Elsevier Ltd. All rights reserved.

关键词： Drag

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Car Defect Detection Using Image Processing

Car Defect Detection Using Image Processing

引用

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

作者： Durai, S. Sujithra, T. Charan, Athmakuri Sai Pulyala, Sai Kiran Satyam, Battula Vishnuwardhan Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Avadi Dept. of Cse Chennai India Manipal Institute of Technology Computer Science and Engineering Manipal India

ISBN: (纸本)9798350334357

Defect Detection became one of the hardest problems in this world nowadays. As of now humans detected the defects with the help of their naked eye. This process is time consuming and this can be done by only experts and these decisions may be differ from expert to expert. To avoid this problem we are training the collected data set. This will consume very less time and provides the best solution. The detection of defects is essential in quality control in manufacturing to get high quality products in less time. Humans can identify large defects but in case of micro defects humans can't found those so, this implementation will helpful in finding large as well as micro and milli defects as well. In this project we are trying to detect the defects such as dents and ripples present on the surface of the car which are macro in size at the time of manufacturing the cars and in the case of accidents happened. We'll use a high-quality camera to simultaneously capture photographs of the vehicle's five sides. We will use Convolution Neural Network [CNN] for training (which highlights the location of the defects). Once trained, that model we can use it for different end user applications. © 2023 IEEE.

关键词： Feature extraction

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Doctormate- An Early Disease Prediction Approach using Multiple Machine Learning Algorithms 2

Doctormate- An Early Disease Prediction Approach using Multi...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Kiruthiga, S. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350346640

Prediction of Multiple Diseases using Machine Learning Algorithms is a user-friendly ML model that is used to predict the disease based on the symptoms entered by the user and gives the best analysis of possible diseases. It is based on the symptoms collected from large datasets, which is taken from hospitals containing the textual discharge summaries of patient diseases. It is a model which gives tips to the user to maintain a healthy life and also it is used to find out possible diseases very earlier, the user is suffering from. Health industry plays a crucial role in curing patient's diseases in today's world. So our work is going to help the health industry in an indirect way where user does not need to go to hospital for checkup instead user get to know the accurate disease within few seconds by entering at least two symptoms. It can handle enormous amount of data. There are various procedures available for the treatment of various diseases across the world which may take more time for diagnosis and prediction. Machine learning is one of the most emerging technology which helps in prediction as well as diagnosis of a disease. This work is completely done by using Machine learning algorithms like Decision Tree Algorithm, Random Forest Algorithm, Naive Bayes Algorithm, K Nearest Neighbour algorithm where these algorithms are deployed on larger datasets, which is then preprocessed by using data mining techniques to achieve higher accuracy than the existing *** lack of accuracy in existing models may lead to failure of diagnosis of the diseases in early stages and cause threat to patient's life. This study has used Tkinter interface in Python programming language. The objective to attain higher accuracy improves the reliability of the model and gains patient's trust for the cost-effective utilization of the proposed model. © 2023 IEEE.

关键词： Forecasting

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An Adaptive Modelling of Neuro-Fuzzy and Craziness PSO Algorithm for DC Fault Protection System

An Adaptive Modelling of Neuro-Fuzzy and Craziness PSO Algor...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Chandrashekhar, M. Dhal, P.K. Vel Tech Rangarajan Dr.Sagunthala RandD Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9781665474511

This study makes use of PSO and CRPSO-based two unique optimization methodologies. The process of well portions of the DC system must not be suggestively disrupted by a DC fault on a single division. Around DC circuit breakers (DCCBs), like power-driven types, have low costs and power losses, but because of their continued opening times, they have been careful unsuitable for DC fault separation in the system. The combination of switching processes and changing capacitor voltages of the sub-modules in a modular multilevel converter (MMC) source second and other even-order harmonics in the circulating currents. Additional power loss increased current strain on power components, and even changeableness during transients will all result from these harmonic currents. Various case studies that involve both DC and AC problems are established. The strategic control approach is validated by simulation results on a three-phase grid-connected MMC high voltage DC system, which used sophisticated algorithms (ANFIS+CRPSO) to compensate for physical faults at the grid side. © 2023 IEEE.

关键词： Electric circuit breakers

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