Pub Date : 2023-05-25DOI: 10.1109/iconscept57958.2023.10170018
{"title":"IConSCEPT 2023 Cover Page","authors":"","doi":"10.1109/iconscept57958.2023.10170018","DOIUrl":"https://doi.org/10.1109/iconscept57958.2023.10170018","url":null,"abstract":"","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170532
R. B. Jayanthi Rajee, S. M. Roomi, V. PooAnnamalai, M.Parisa Begam
Diagnosing retinal disease in an earlier stage using fundus images is a complicated, error-prone, time-consuming, and challenging process. Therefore, a computerized retinal disease detection system with advances in technology is required to identify various eye disorders in fundus images. The proposed work creates a dataset that comprises of fundus images with some of the retinal diseases such as Diabetic retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma (GA), Hemorrhages (HG), Epiretinal membrane (EM), and No disease (NOD) and it is named as “Multi Disease Dataset” (MUD). To identify the disease in retinal images, the created dataset is evaluated using different transfer learning techniques. Compared to state-of-the-art methods, experimental analysis demonstrates that the proposed method achieves an accuracy of 89.11% using Inceptionv3 on the MUD dataset and is capable of detecting five diseases.
{"title":"A Transfer Learning Approach For Retinal Disease Classification","authors":"R. B. Jayanthi Rajee, S. M. Roomi, V. PooAnnamalai, M.Parisa Begam","doi":"10.1109/IConSCEPT57958.2023.10170532","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170532","url":null,"abstract":"Diagnosing retinal disease in an earlier stage using fundus images is a complicated, error-prone, time-consuming, and challenging process. Therefore, a computerized retinal disease detection system with advances in technology is required to identify various eye disorders in fundus images. The proposed work creates a dataset that comprises of fundus images with some of the retinal diseases such as Diabetic retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma (GA), Hemorrhages (HG), Epiretinal membrane (EM), and No disease (NOD) and it is named as “Multi Disease Dataset” (MUD). To identify the disease in retinal images, the created dataset is evaluated using different transfer learning techniques. Compared to state-of-the-art methods, experimental analysis demonstrates that the proposed method achieves an accuracy of 89.11% using Inceptionv3 on the MUD dataset and is capable of detecting five diseases.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"os-30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127772140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170285
A. Abhishek, Sagar Deep Deb, R. K. Jha, R. Sinha, K. Jha
Leukemia is a hematological disorder which affects the ability of the body to resist against diseases and infection. Early detection of the disease can play a vital role in the treatment of a patient. Computer aided detection system based on machine learning and deep learning algorithms can reduce the burden of doctors and the mortality rate due to leukemia. Transfer learning technique is frequently used in biomedical field due to unavailability of huge and well annotated dataset. The proposed work applies transfer learning to classify leukemia using 1358 microscopic images of blood smears. Pre-trained VGG16 is fine tuned on the leukemic dataset to classify an image as acute leukemia instance, chronic leukemia instance or a healthy instance with an accuracy of 93.01%.
{"title":"Classification of Leukemia using Fine Tuned VGG16","authors":"A. Abhishek, Sagar Deep Deb, R. K. Jha, R. Sinha, K. Jha","doi":"10.1109/IConSCEPT57958.2023.10170285","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170285","url":null,"abstract":"Leukemia is a hematological disorder which affects the ability of the body to resist against diseases and infection. Early detection of the disease can play a vital role in the treatment of a patient. Computer aided detection system based on machine learning and deep learning algorithms can reduce the burden of doctors and the mortality rate due to leukemia. Transfer learning technique is frequently used in biomedical field due to unavailability of huge and well annotated dataset. The proposed work applies transfer learning to classify leukemia using 1358 microscopic images of blood smears. Pre-trained VGG16 is fine tuned on the leukemic dataset to classify an image as acute leukemia instance, chronic leukemia instance or a healthy instance with an accuracy of 93.01%.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116861705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170152
V. Krishnamurthy, B. Jafrin Rosary, G. Oliver Joel, B Surendiran, Sakshi Kumari
This research work aims to create an Augmented Reality (AR) based android app that can project the dimensions of an automobile in the real world and recognize voice commands to operate functions like opening car doors and changing colors. The app uses a combination of augmented reality, machine learning technology, Unity game engine, C# script, Google speech recognition API and Vuforia SDK to superimpose images of the car in the real world and allow control through voice commands. The initial focus is on cars, but the solution can also be used to create AR-enabled brochures for marketing companies to enhance sales and provide customers with a better understanding of the product before purchase.
{"title":"Voice command-integrated AR-based E-commerce Application for Automobiles","authors":"V. Krishnamurthy, B. Jafrin Rosary, G. Oliver Joel, B Surendiran, Sakshi Kumari","doi":"10.1109/IConSCEPT57958.2023.10170152","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170152","url":null,"abstract":"This research work aims to create an Augmented Reality (AR) based android app that can project the dimensions of an automobile in the real world and recognize voice commands to operate functions like opening car doors and changing colors. The app uses a combination of augmented reality, machine learning technology, Unity game engine, C# script, Google speech recognition API and Vuforia SDK to superimpose images of the car in the real world and allow control through voice commands. The initial focus is on cars, but the solution can also be used to create AR-enabled brochures for marketing companies to enhance sales and provide customers with a better understanding of the product before purchase.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169970
P. Manojkumar, L. S. Kumar, B. Jayanthi
Computer vision is a recent technological advancement to digitally perceive the real world at an advanced level, through digital images and videos. Object detection is a subset of computer vision which is one of the prominent techniques used for object tracking, automatic driving, anomaly detection, etc. Object detection can be based on either machine learning or deep learning algorithms, it can be used for the localization of the image and classification of elements into diverse classes. This work provides a comparison of the object detection approaches such as Region with Convolutional Neural Network (R-CNN), Fast R-CNN, and You Only Look Once(YOLO) and Single Shot multibox Detector (SSD). The implementation of an object detection technique YOLOv4 and a custom model are done, which recognizes the objects from an input image, webcam image and live stream webcam video.
计算机视觉是最近的一项技术进步，通过数字图像和视频在高级水平上数字化地感知现实世界。目标检测是计算机视觉的一个分支，是用于目标跟踪、自动驾驶、异常检测等领域的重要技术之一。物体检测可以基于机器学习或深度学习算法，它可以用于图像的定位和元素分类到不同的类别。本研究对区域卷积神经网络(R-CNN)、快速R-CNN和You Only Look Once(YOLO) and Single Shot multibox Detector (SSD)等目标检测方法进行了比较。实现了目标检测技术YOLOv4和自定义模型，从输入图像、网络摄像头图像和实时网络摄像头视频中识别目标。
{"title":"Performance Comparison of Real Time Object Detection Techniques with YOLOv4","authors":"P. Manojkumar, L. S. Kumar, B. Jayanthi","doi":"10.1109/IConSCEPT57958.2023.10169970","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169970","url":null,"abstract":"Computer vision is a recent technological advancement to digitally perceive the real world at an advanced level, through digital images and videos. Object detection is a subset of computer vision which is one of the prominent techniques used for object tracking, automatic driving, anomaly detection, etc. Object detection can be based on either machine learning or deep learning algorithms, it can be used for the localization of the image and classification of elements into diverse classes. This work provides a comparison of the object detection approaches such as Region with Convolutional Neural Network (R-CNN), Fast R-CNN, and You Only Look Once(YOLO) and Single Shot multibox Detector (SSD). The implementation of an object detection technique YOLOv4 and a custom model are done, which recognizes the objects from an input image, webcam image and live stream webcam video.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170691
K. Arunkumar, S. Leninisha
A modern agricultural sector requires accurate crop identification and classification. A new computer vision system is presented here that successfully discriminates between various plant species in real time under uncontrolled lighting. Features are vital for image classification and shape descriptors are mainly considered in this study. This system consists of image processing delivering results in real-time and a pixel calculator with more accuracy. Using these components together results in an efficient, reliable system for achieving excellent results in many different situations. Tested on several leaf species taken from the UCI repository. The system successfully detects an average of 87% under different variety of species. Additionally, the system has shown to produce acceptable results even under extremely challenging conditions, such as disease infected leaf or irregular shape leaf. The leaf boundaries was determined and evaluated through Harris corner algorithm. Compared to other high-cost methods, it was observed high species classification and lower testing time for our approach. The researchers also discussed challenges and solutions related to leaf classification, including identifying different leaves, classes of leaf shapes, lighting conditions, and stages of growth.
{"title":"An effective identification between various plant species using shape descriptors and image processing technique","authors":"K. Arunkumar, S. Leninisha","doi":"10.1109/IConSCEPT57958.2023.10170691","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170691","url":null,"abstract":"A modern agricultural sector requires accurate crop identification and classification. A new computer vision system is presented here that successfully discriminates between various plant species in real time under uncontrolled lighting. Features are vital for image classification and shape descriptors are mainly considered in this study. This system consists of image processing delivering results in real-time and a pixel calculator with more accuracy. Using these components together results in an efficient, reliable system for achieving excellent results in many different situations. Tested on several leaf species taken from the UCI repository. The system successfully detects an average of 87% under different variety of species. Additionally, the system has shown to produce acceptable results even under extremely challenging conditions, such as disease infected leaf or irregular shape leaf. The leaf boundaries was determined and evaluated through Harris corner algorithm. Compared to other high-cost methods, it was observed high species classification and lower testing time for our approach. The researchers also discussed challenges and solutions related to leaf classification, including identifying different leaves, classes of leaf shapes, lighting conditions, and stages of growth.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122577637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170570
S. Gopalram, L. Nirmal Raja K, N. Nishanth, S. Sashaank, S. Thanush, K. Varunapriyan
Liquefied Petroleum Gas (LPG) is one of the most widely used domestic fuels. It is consumed in households for cooking and is also used for industrial purposes. Being a commonly used fuel, it is prone to occasional accidents in cases where the gas cylinder nozzle is not closed properly during the night, or when the user is out of the house. This may lead to safety hazards, causing damage to life and property. Currently, cylinders are operated only physically by the user. It is human nature to be occasionally inattentive, forgetful or negligent. Sometimes when the user leaves their home, they may forget to close the cylinder nozzle properly. This causes gas leakages, which are dangerous. This work is focused on building a system that uses Internet of Things to control the opening and closing of gas nozzles or valves using a mobile or web application remotely. The user can check if their home gas valve is open or closed on the application, and can use it to either close or open it as per their need. This way, they have more control over their home, contribute towards reducing wastage and create a safer environment.
{"title":"Smart valve control system for LPG cylinders using IoT","authors":"S. Gopalram, L. Nirmal Raja K, N. Nishanth, S. Sashaank, S. Thanush, K. Varunapriyan","doi":"10.1109/IConSCEPT57958.2023.10170570","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170570","url":null,"abstract":"Liquefied Petroleum Gas (LPG) is one of the most widely used domestic fuels. It is consumed in households for cooking and is also used for industrial purposes. Being a commonly used fuel, it is prone to occasional accidents in cases where the gas cylinder nozzle is not closed properly during the night, or when the user is out of the house. This may lead to safety hazards, causing damage to life and property. Currently, cylinders are operated only physically by the user. It is human nature to be occasionally inattentive, forgetful or negligent. Sometimes when the user leaves their home, they may forget to close the cylinder nozzle properly. This causes gas leakages, which are dangerous. This work is focused on building a system that uses Internet of Things to control the opening and closing of gas nozzles or valves using a mobile or web application remotely. The user can check if their home gas valve is open or closed on the application, and can use it to either close or open it as per their need. This way, they have more control over their home, contribute towards reducing wastage and create a safer environment.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122608908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170555
R. Arthi, S. Nishuthan, L. Deepak Vignesh
Agriculture is an essential industry that provides the necessities of life, including food, clothing, and shelter. It is crucial in rural areas, as it creates jobs and income opportunities and contributes to the Indian economy. Furthermore, agricultural practices play a critical role in maintaining the environment and preserving its fragile balance. This paper proposes a low-cost system that uses Internet of Things (IoT) and Machine Learning (ML) to maximize crop yield and productivity. The system consists of three key components: an IoT device, a mobile application, and servers. The IoT device uses an Espressif System Platform 32(ESP32) microcontroller, a Digital Humidity and Temperature sensor 11 (DHTII) temperature humidity sensor, and a soil moisture sensor to gather data and sends it to the Amazon web services (AWS) IoT via the Message Queuing Telemetry Transport (MQTT) protocol. The IoT device is interfaced with a relay switch to turn ON/OFF water pumps. The mobile application helps us to monitor the temperature, humidity, soil moisture and light intensity in real time. It also allows us to control the water pump connected to the IoT device and give access to our prediction ML model for crop and fertilizer recommendations. The server is an integral part of this system as it helps us connect the mobile application with the IoT device and provides storage for the sensor values and Representational State Transfer-Application Programming Interface (REST-APIs) to access our ML models. The proposed work concludes that it can highly increase agricultural productivity with the support of IoT.
{"title":"Smart Agriculture System Using IoT and ML","authors":"R. Arthi, S. Nishuthan, L. Deepak Vignesh","doi":"10.1109/IConSCEPT57958.2023.10170555","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170555","url":null,"abstract":"Agriculture is an essential industry that provides the necessities of life, including food, clothing, and shelter. It is crucial in rural areas, as it creates jobs and income opportunities and contributes to the Indian economy. Furthermore, agricultural practices play a critical role in maintaining the environment and preserving its fragile balance. This paper proposes a low-cost system that uses Internet of Things (IoT) and Machine Learning (ML) to maximize crop yield and productivity. The system consists of three key components: an IoT device, a mobile application, and servers. The IoT device uses an Espressif System Platform 32(ESP32) microcontroller, a Digital Humidity and Temperature sensor 11 (DHTII) temperature humidity sensor, and a soil moisture sensor to gather data and sends it to the Amazon web services (AWS) IoT via the Message Queuing Telemetry Transport (MQTT) protocol. The IoT device is interfaced with a relay switch to turn ON/OFF water pumps. The mobile application helps us to monitor the temperature, humidity, soil moisture and light intensity in real time. It also allows us to control the water pump connected to the IoT device and give access to our prediction ML model for crop and fertilizer recommendations. The server is an integral part of this system as it helps us connect the mobile application with the IoT device and provides storage for the sensor values and Representational State Transfer-Application Programming Interface (REST-APIs) to access our ML models. The proposed work concludes that it can highly increase agricultural productivity with the support of IoT.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116802290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170246
Raj Gusain, A. Vidyarthi, R. Prakash, A. Shukla
The aim of this research paper is to evaluate the performance of the Indian Regional Navigation Satellite System (NavIC) in the low latitude northern region of India during December 2019 observing low elevation angles (below 50°) of most of the NavIC satellites. The study includes an analysis of statistical methods to analyze positional variability of NavIC receiver, and found out its impact on the calculation of circular error probability (CEP) using a statistical approach. The study was conducted by collecting data from a NavIC receiver located in the low latitude northern region of India during December 2019. The results showed that the CEP was within acceptable limits for most of the time, but occasional outliers were observed due to the low elevation of the satellites. When low-elevation satellite observations produce outliers in the NavIC system, the CEP calculation can become inaccurate due to signal blockages, interference, or environmental factors that influence position estimation precision. The consequences of occasional outliers in the CEP calculation can be significant, particularly for applications that require high precision location data. The study suggests that more research is needed to enhance the accuracy of the NavIC system in situations where the satellites are at a low elevation angle and there are strong ionospheric irregularities or ionospheric scintillations.
{"title":"Assessing NavIC Accuracy at Dehradun in the Winter Season: A Case Study","authors":"Raj Gusain, A. Vidyarthi, R. Prakash, A. Shukla","doi":"10.1109/IConSCEPT57958.2023.10170246","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170246","url":null,"abstract":"The aim of this research paper is to evaluate the performance of the Indian Regional Navigation Satellite System (NavIC) in the low latitude northern region of India during December 2019 observing low elevation angles (below 50°) of most of the NavIC satellites. The study includes an analysis of statistical methods to analyze positional variability of NavIC receiver, and found out its impact on the calculation of circular error probability (CEP) using a statistical approach. The study was conducted by collecting data from a NavIC receiver located in the low latitude northern region of India during December 2019. The results showed that the CEP was within acceptable limits for most of the time, but occasional outliers were observed due to the low elevation of the satellites. When low-elevation satellite observations produce outliers in the NavIC system, the CEP calculation can become inaccurate due to signal blockages, interference, or environmental factors that influence position estimation precision. The consequences of occasional outliers in the CEP calculation can be significant, particularly for applications that require high precision location data. The study suggests that more research is needed to enhance the accuracy of the NavIC system in situations where the satellites are at a low elevation angle and there are strong ionospheric irregularities or ionospheric scintillations.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170600
R. Nagaraj, V. Arulvadivelan, K. Gouthamkumar, K. Dharshen, L. S. Kumar
Freshwater mapping is a crucial element for water resource planning and conservation. Recently, the estimation of surface area and its temporal changes have been made easier due to the availability of remote sensing data. However, the quantification of water body volume is limited because the existing remote sensing technologies cannot estimate bathymetry data. In this study, Lake Victoria’s surface water extent and volume are estimated by combining the remote sensing and bathymetry data. The surface water extent is determined by feature extraction and classification using Machine Learning (ML). Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are the ML algorithms considered. Landsat ETM+images have been used for experimentation. Experimental results concluded that LightGBM and DT are the best and least performing ML algorithms for determining surface extent and volume.
{"title":"Surface water mapping and volume estimation of Lake Victoria using Machine Learning Algorithms","authors":"R. Nagaraj, V. Arulvadivelan, K. Gouthamkumar, K. Dharshen, L. S. Kumar","doi":"10.1109/IConSCEPT57958.2023.10170600","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170600","url":null,"abstract":"Freshwater mapping is a crucial element for water resource planning and conservation. Recently, the estimation of surface area and its temporal changes have been made easier due to the availability of remote sensing data. However, the quantification of water body volume is limited because the existing remote sensing technologies cannot estimate bathymetry data. In this study, Lake Victoria’s surface water extent and volume are estimated by combining the remote sensing and bathymetry data. The surface water extent is determined by feature extraction and classification using Machine Learning (ML). Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are the ML algorithms considered. Landsat ETM+images have been used for experimentation. Experimental results concluded that LightGBM and DT are the best and least performing ML algorithms for determining surface extent and volume.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：