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Precision Mapping of Subsurface Water Resources with Octocopters and GER Water Detectors 4

Precision Mapping of Subsurface Water Resources with Octocop...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Prajith, B. Sivanesh, K.S. Rakesh, R. Shiny, A. Roshan, R.S. Afra Mariyam, A. Sri Sairam Engineering College Department of MTech Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Recent advancements in drone technology have enabled accurate measurement of groundwater levels, an important resource for water management and agricultural practices. This innovative approach uses high-resolution sensors and high-quality images to search for underground water and map it like never. The addition of AI and machine learning makes data capture even better, providing faster analytics and future insights [1]. These new technologies not only contribute to water management but also help to protect nature through the efficient use of groundwater. The paper discusses how this new drone tech was developed, how it can be used and where it can be useful in different environments. It shows how the way we manage water is changing. Water is essential for life, agriculture and industry, but groundwater management remains challenging. Older methods of measuring groundwater tend to destroy the site, take longer and do not cover much. But new drone tech offers a game-changing fix. Called the "GER Water Detector,"the new technique uses sophisticated automated technology with sharper sensors and better imaging to measure and map groundwater [2]. © 2024 IEEE.

关键词： Remote sensing

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Perishable Innovative Agriculture Hub - An E-Commerce Website Application for Farmers and Consumers Trading Association 4

Perishable Innovative Agriculture Hub - An E-Commerce Websit...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Nandhini Devi, S. Prajith, B. Roshan, R.S. Sri Sairam Engineering College Department of MTech Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

The abstract of a forest fire detection system using deep learning techniques and IoT involves the integration of advanced technologies to address the critical issue of timely and accurate wildfire detection. This innovative approach harnesses the power of deep learning algorithms, specifically designed for image recognition, to analyze visual data captured by surveillance cameras deployed in forested areas. The Internet of Things (IoT) plays a pivotal role in this system by facilitating real-time communication between these cameras and a central monitoring system. The interconnected network enables swift data transmission and analysis, allowing for the rapid identification of potential fire incidents. By leveraging deep learning, the system can distinguish between normal environmental conditions and the presence of smoke or flames, ensuring a low rate of false alarms. This integrated solution not only enhances the efficiency of early fire detection but also aids in prompt response efforts, ultimately contributing to effective forest fire prevention and mitigation strategies. © 2024 IEEE.

关键词： Fire detectors

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Enhancing Generator Efficiency: IoT-Based Distilled Water Monitoring System 4

Enhancing Generator Efficiency: IoT-Based Distilled Water Mo...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Yuvaraj, G. Shiny, A. Ganesh, R. Sivanesh, K.S. Vinothini, A. Sri Sairam Engineering College Computer Science and Engineering Department of MTech Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Artificial Intelligence and Data Science Avadi India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

In present day instances, electronic devices have emerge as vital in ordinary life [1]. With family appliances and electronic gadgets running constantly, uninterrupted power supply is important. Any strength loss at some stage in tool operation can reason harm. To mitigate such risks, batteries function a backup electricity deliver. This paper proposes a way to control battery voltage the use of Internet of Things (IoT) generation, augmented with MQTT (Message Queuing Telemetry Transport) for more suitable communique performance. The gadget targets to be price-effective, reduce human resource necessities, and enhance time efficiency. It utilizes a voltmeter for tracking battery voltage, Raspberry Pi for information monitoring and updates, Arduino for facts transmission, cloud services for records transmission and garage, and a cellular utility for remote monitoring of battery status. By integrating MQTT generation, this paper discusses the implementation and advantages of the IoT-based battery voltage manipulate system, highlighting the stepped forward communique efficiency and actual-time data shipping. Additionally, the mixing of a mobile software complements person accessibility and comfort, further improving the device's functionality and relevance in cutting-edge contexts. © 2024 IEEE.

关键词： Uninterruptible power systems

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Comparison of various machine learning approaches uses in heart ailments prediction 1

Comparison of various machine learning approaches uses in he...

引用

1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Gupta, Gunjan Adarsh, U. Reddy, N.V. Subba Rao, B. Ashwath MTech Computer Science and Engineering MIT Manipal576104 India Department of Computer Science and Engineering MIT Manipal576104 India

Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

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Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers 1

Prediction on Cardiovascular disease using Decision tree and...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Reddy, V. Sai Krishna Meghana, P. Reddy, N.V. Subba Rao, B. Ashwath MTech Computer Science and Engineering MIT Manipal576104 India Department of Computer Science MIT Manipal576104 India

Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Decision trees

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MED-LENS - Al-based tool for preliminary diagnosis of Dermatological manifestations

MED-LENS - Al-based tool for preliminary diagnosis of Dermat...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Priya, E. Lohith Kamal, T.M. Nithya Vishnu, V. Aakash, A. Ruthi Shankari, S. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India Sri Sairam Engineering College Department of MTech Integrated Computer Science Engineering Chennai India

ISBN: (纸本)9798350394580

Access to healthcare for skin diseases is challenging, particularly in rural areas, due to limited specialized services, long waiting times, and low awareness. The Med Lens project aims to address these disparities with user-friendly skin disease detection, diagnosis, and consultation services. It leverages technology, including Raspberry Pi 4 Model B and CNNs, to enhance disease detection and provide information The hardware uses Raspberry Pi 4 Model B with a camera module and LCD screen for image capture and display. The software employs CNNs to identify skin diseases accurately, generating detailed medical reports. Database management and cloud technologies ensure data integrity and accessibility. A user-friendly GUI simplifies interactions, allowing input of patient data and delivering rapid, accurate results. This research yielded a positive result achieving a test accuracy of 92.78% and a very low-test loss of 1.11% Med Lens aims to reduce healthcare disparities and improve dermatology care in underserved areas, enhancing patient outcomes. © 2023 IEEE.

关键词： computer Vision Healthcare Disparities Machine Learning Raspberry Pi Convolutional Neural Networks (CNNs) Rural Healthcare Skin Disease Telemedicine

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Fake News Detection from Online media using Machine learning Classifiers 1

Fake News Detection from Online media using Machine learning...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Pandey, Shalini Prabhakaran, Sankeerthi Reddy, N.V. Subba Acharya, Dinesh MTech CSIS Department of Computer Science and Engineering MIT MAHE Manipal India Department of Computer Science and Engineering MIT MAHE Manipal India

With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of "Fake news" being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Decision trees

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A Proficient Approach in Crop Recommendation System using Gradient Boosting Machine Technique 3

A Proficient Approach in Crop Recommendation System using Gr...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kumar, Kandukuri Joseph Reddy, N Praneeth Kumar Vasu, Andhavarapu Sandosh, S. Business Analytics Vellore Institute of Technology Integrated Mtech in CSE Specialization Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The Crop Recommendation System (CRS), a powerful data-driven tool, has revolutionized farming practices by delivering precise crop recommendations. Decision Trees and Logistic Regression are employed as initial models to assess crop choices and further enhance accuracy with Gradient Boosting Machine (GBM), which consistently outperforms other methods. CRS meticulously processes essential factors, including Nitrogen (N), Phosphorus (P), Potassium (K), Temperature, Humidity, Rainfall, and pH levels. These inputs lead to highly personalized recommendations, tailored to specific farming contexts. CRS significantly boosts agricultural productivity, providing farmers with insights for optimal crop selection, increased yields, and efficient resource allocation. It promotes sustainability by encouraging environmentally responsible farming practices. In modern agriculture, CRS emerges as a pivotal asset, contributing to productivity and global food security. Its ability to guide farmers toward sustainable practices positions it as a driving force for positive change in the agricultural sector, offering a path toward a more sustainable and food-secure future. © 2023 IEEE.

关键词： Agricultural Decision Support System Crop Modeling Data-driven Agriculture Machine Learning Algorithms Precision Farming Remote Sensing Technologies

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Cyclone Chronicles: A Comprehensive Survey of Advancements in Cyclone Prediction Techniques 2

Cyclone Chronicles: A Comprehensive Survey of Advancements i...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Raj, Abhineet Tiwari, Tanmay Madurwar, Ayush Sandosh, S. Integrated Mtech in CSE specialization In Business Analytics Vellore Institute of Technology Chennai India School Of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Tropical cyclones represent formidable natural phenomena, demanding constant progress in prediction methodologies. The survey paper amalgamates diverse insights from recent research, offering an extensive overview of the state of tropical cyclone prediction. This study analyzes the stat-of-the-art approaches, such as Support Vector Machine and polynomial regression models to achieve pre cise cyclone tracking. Additionally, this study examines the critical role of satellite imagery in automation, shedding light on the automated techniques employed. Furthermore, this study explores the emerging applications of Generative Adversarial Networks (GANs) for learning physical characteristics within cyclone data distribution, enhancing predictive capabilities. It also addresses challenges like rapid intensification, presenting prototype visualization tools and bias correction techniques as innovative solutions. The survey underscores the significance of machine learning models and ensemble forecasting in elevating the precision of cyclone predictions. © 2023 IEEE.

关键词： Bias correction Convolutional Neural Networks Deep learning Ensemble forecasting Generative Adversarial Networks Machine learning Satellite imagery Tropical cyclone prediction Weather forecasting

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Architectural Design of Malware Infected File Detection Using Deep Learning

Architectural Design of Malware Infected File Detection Usin...

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International Conference on Power, Instrumentation, Control and Computing (PICC)

作者： Sreeparna Sreejay O P Shibily Joseph MTech in Computer Science & Engineering Government Engineering College Thrissur Computer Science & Engineering Government Engineering College Thrissur

I. Abstract Currently, with the ongoing development of 5G and the IoT Cyberspace has grown to be a significant part of social and economic development thanks to the technology of social and economic development, as well as a fundamental domain of national security. Therefore, the detection of Malware and its variations are extremely important in cyberspace. Malware is a harmful software that is designed to damage and destroy computers and computer systems. Malware is constantly generating, updating, and manipulating which makes the detection more challenging. Viruses, Worms, Trojans, Ransomware, Adware, etc. are the common types of malware attacks. Signature based, Behavioral based and Heuristic ones are the three main methods used for malware detection. This paper discusses the architectural design for malware prediction in executable files of various applications and visualization techniques used to achieve better results by creating RGB images of these executable files and try to convert the malware prediction method into an image classification problem. InceptionV3 and the SVM model are used for feature extraction and prediction. Leopard mobile dataset and Malimg dataset are used here.

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