Pub Date : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125925
P. Prasad, Vamsi Kongara, Pavan Kumar Ankireddy, Santosh Jagga, Srinivaas Guduru, Shashank K
One of the deadliest illnesses that cause death is heart disease. Worldwide, almost 17 million people died each year because of various heart diseases. To aid in the early diagnosis of heart illness, improved diagnosis, high-risk individuals, and enhanced decision-making for extra treatment and prevention, a prediction model can be proposed. Many academics have looked at the heart disease risk variables and suggested certain machine learning algorithms. However, these models need to be enhanced in order to produce findings that are extremely precise due to the large dimensionality of the data. This study intends to develop a novel framework for accurate heart disease diagnosis. The proposed model can generate precise data for the training model by applying effective approaches for data collection, pre-processing, and transformation. The proposed model employs a combined dataset from the universities of Switzerland, Hungarian, Cleveland, Long Beach VA. This model employs Relief methods for feature selection. Ensemble learning is used to generate novel hybrid classifiers. The outcomes demonstrated that hybrid classifiers performed better than current models that displayed an accuracy of above 95%. These results suggests that the model with relief feature selection and hybrid classifiers may be a more effective approach for predicting heart diseases.
{"title":"Estimating the Chances of Getting Heart Disease using Machine Learning Algorithms","authors":"P. Prasad, Vamsi Kongara, Pavan Kumar Ankireddy, Santosh Jagga, Srinivaas Guduru, Shashank K","doi":"10.1109/ICOEI56765.2023.10125925","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125925","url":null,"abstract":"One of the deadliest illnesses that cause death is heart disease. Worldwide, almost 17 million people died each year because of various heart diseases. To aid in the early diagnosis of heart illness, improved diagnosis, high-risk individuals, and enhanced decision-making for extra treatment and prevention, a prediction model can be proposed. Many academics have looked at the heart disease risk variables and suggested certain machine learning algorithms. However, these models need to be enhanced in order to produce findings that are extremely precise due to the large dimensionality of the data. This study intends to develop a novel framework for accurate heart disease diagnosis. The proposed model can generate precise data for the training model by applying effective approaches for data collection, pre-processing, and transformation. The proposed model employs a combined dataset from the universities of Switzerland, Hungarian, Cleveland, Long Beach VA. This model employs Relief methods for feature selection. Ensemble learning is used to generate novel hybrid classifiers. The outcomes demonstrated that hybrid classifiers performed better than current models that displayed an accuracy of above 95%. These results suggests that the model with relief feature selection and hybrid classifiers may be a more effective approach for predicting heart diseases.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134437880","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125785
M. N, E. S, H. S, Megha A
In industries, monitoring the flow and level of liquid in water treatment plants requires wired monitoring. There is a long distance between the control room and the water treatment plant. If there is any fault or error there is a necessity of physical monitoring in case of emergency also this is not safe all the time. Hence, there should be some alternative to monitor the flow and level of liquid This can be done by wireless monitoring using LORA communication and also by NodeMCU. Through this, monitoring of flow and level of liquid in water treatment plants are analyzed. The main aim is to change it from a wired monitoring system to wireless monitoring system. It is done by using ultrasonic sensor, water flow meter, node MCU, Arduino. These are interfaced and the data are stored in the cloud, these values are displayed in LCD display. Node MCU is used for transmitting and receiving data. So through this monitoring of flow and level of liquid in water treatment plants are done in wireless method.
{"title":"Wireless Flow and Level Monitoring for Water Treatment Plants in Paper and Pulp Industry","authors":"M. N, E. S, H. S, Megha A","doi":"10.1109/ICOEI56765.2023.10125785","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125785","url":null,"abstract":"In industries, monitoring the flow and level of liquid in water treatment plants requires wired monitoring. There is a long distance between the control room and the water treatment plant. If there is any fault or error there is a necessity of physical monitoring in case of emergency also this is not safe all the time. Hence, there should be some alternative to monitor the flow and level of liquid This can be done by wireless monitoring using LORA communication and also by NodeMCU. Through this, monitoring of flow and level of liquid in water treatment plants are analyzed. The main aim is to change it from a wired monitoring system to wireless monitoring system. It is done by using ultrasonic sensor, water flow meter, node MCU, Arduino. These are interfaced and the data are stored in the cloud, these values are displayed in LCD display. Node MCU is used for transmitting and receiving data. So through this monitoring of flow and level of liquid in water treatment plants are done in wireless method.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131758274","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125826
S. Karthikeyan, J. Kiruthik, S. Madumitha, R. Manikandan, V. Prakash Raj
In the modern world, an increase in the usage of automobiles for commercial purposes has also increased the number of accidents occurring in commercial vehicles, which leads to the loss of life of the people involved in the accident. To minimize the death rates involved in an accident, the people who are met with the accident must claim medical assistance at the correct time. This study is concerned with two set-ups. One set-up is associated with the vehicle, where the use of a MEMS or gyroscopic sensor, a vibration sensor, and a gas sensor integrated with Arduino helps to detect the accident. Here, the location is detected by the GPS module and updated in the cloud by using the ESP8266 Wi-Fi module. If any accident is detected, the RF transmitter circuit sends the signal to the RF receiver. The other configuration is related to the Ambulance which consists of an RF receiver circuit integrated with the NodeMCU microcontroller. When the signal reaches the receiver, NodeMCU retrieves the information from the cloud and displays it on the LCD. Integration of a tracking system with a Radio frequency transmitter and receiver helps build IoT services using embedded systems. The system of providing medical assistance to the people involved in the accident would help us reduce the death rates.
{"title":"Design and Implementation of IoT Based Accident Detection and Prevention System","authors":"S. Karthikeyan, J. Kiruthik, S. Madumitha, R. Manikandan, V. Prakash Raj","doi":"10.1109/ICOEI56765.2023.10125826","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125826","url":null,"abstract":"In the modern world, an increase in the usage of automobiles for commercial purposes has also increased the number of accidents occurring in commercial vehicles, which leads to the loss of life of the people involved in the accident. To minimize the death rates involved in an accident, the people who are met with the accident must claim medical assistance at the correct time. This study is concerned with two set-ups. One set-up is associated with the vehicle, where the use of a MEMS or gyroscopic sensor, a vibration sensor, and a gas sensor integrated with Arduino helps to detect the accident. Here, the location is detected by the GPS module and updated in the cloud by using the ESP8266 Wi-Fi module. If any accident is detected, the RF transmitter circuit sends the signal to the RF receiver. The other configuration is related to the Ambulance which consists of an RF receiver circuit integrated with the NodeMCU microcontroller. When the signal reaches the receiver, NodeMCU retrieves the information from the cloud and displays it on the LCD. Integration of a tracking system with a Radio frequency transmitter and receiver helps build IoT services using embedded systems. The system of providing medical assistance to the people involved in the accident would help us reduce the death rates.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919891","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125963
Josephine Selle Jeyanathan, B. Veerasamy, B. Medha, G. V. Sai, R.Bharath Kumar, Varsha Sahu
Agriculture is one of the key drivers of Indian economy. The primary problem now confronting Indian farmers is that farmers don't choose the right crop based on their land requirements. A significant decline in production is seen as a result. Precision agriculture will provide the farmers with a solution to this problem. To suggest the optimal crop to farmers based on site-specific criteria, precision agriculture uses research data on soil types, features, and crop yields. With the help of an intelligent system, this study aims to help Indian farmers increase crop productivity by selecting the right type of soil. The proposed prototype considers soil characteristics, such as pH value, soil temperature, and soil moisture, as well as environmental factors, such as humidity, as inputs to the machine learning algorithm for decision-making. The output is integrated with the web program known as proteus. The entire prototype is designed using STM32 ARM Processor and simulated using proteus, and the same is implemented using the Nucleo board by integrating the humidity, pH, and temperature sensors for collecting the input data. The result of the prototype is also displayed in the Blynk app as well as the LCD display, where the system recommends the appropriate crop.
{"title":"Design of Crop Recommender System using Machine Learning and IoT","authors":"Josephine Selle Jeyanathan, B. Veerasamy, B. Medha, G. V. Sai, R.Bharath Kumar, Varsha Sahu","doi":"10.1109/ICOEI56765.2023.10125963","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125963","url":null,"abstract":"Agriculture is one of the key drivers of Indian economy. The primary problem now confronting Indian farmers is that farmers don't choose the right crop based on their land requirements. A significant decline in production is seen as a result. Precision agriculture will provide the farmers with a solution to this problem. To suggest the optimal crop to farmers based on site-specific criteria, precision agriculture uses research data on soil types, features, and crop yields. With the help of an intelligent system, this study aims to help Indian farmers increase crop productivity by selecting the right type of soil. The proposed prototype considers soil characteristics, such as pH value, soil temperature, and soil moisture, as well as environmental factors, such as humidity, as inputs to the machine learning algorithm for decision-making. The output is integrated with the web program known as proteus. The entire prototype is designed using STM32 ARM Processor and simulated using proteus, and the same is implemented using the Nucleo board by integrating the humidity, pH, and temperature sensors for collecting the input data. The result of the prototype is also displayed in the Blynk app as well as the LCD display, where the system recommends the appropriate crop.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":" 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113950616","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125873
V. Rafi, Sharief Nadendla, V. Nayak, K.Venkata Naga Sai Reddy, G.UmeshSai Kumar, A. Maniteja
In this research work.the formulation and reorganization of RDN is detailed using loop matrix. The analytical method of determining optimal reorganization consumes more computation time. The computation time increases with number of buses inthe system. So, an optimization algorithm is needed for finding the optimal reorganization of the radial distribution system. The major objective of the optimal reorganization is the minimizing the losses of the network. The optimization algorithms which are used in this article are Genetic Algorithm, Particle Swarm Optimization. In this article, the metaheuristic method is used for optimal reorganization. The organic optimization technique like PSOalgorithm is used for reorganization. The reorganisation issue is explored and examined in the presence and absence of the optimisation approach in a conventional large-scale 119 node network in different circumstances. The acquired findings are then compared.
{"title":"Minimization of Losses in 119 Bus Radial Distribution Network using PSO Algorithm","authors":"V. Rafi, Sharief Nadendla, V. Nayak, K.Venkata Naga Sai Reddy, G.UmeshSai Kumar, A. Maniteja","doi":"10.1109/ICOEI56765.2023.10125873","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125873","url":null,"abstract":"In this research work.the formulation and reorganization of RDN is detailed using loop matrix. The analytical method of determining optimal reorganization consumes more computation time. The computation time increases with number of buses inthe system. So, an optimization algorithm is needed for finding the optimal reorganization of the radial distribution system. The major objective of the optimal reorganization is the minimizing the losses of the network. The optimization algorithms which are used in this article are Genetic Algorithm, Particle Swarm Optimization. In this article, the metaheuristic method is used for optimal reorganization. The organic optimization technique like PSOalgorithm is used for reorganization. The reorganisation issue is explored and examined in the presence and absence of the optimisation approach in a conventional large-scale 119 node network in different circumstances. The acquired findings are then compared.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"49 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027907","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125744
V. Praveena, A. S., Anu Sankari S, Girija K, Kirthivarsini M
Automated teller machines (ATMs) are utilizedby almost everyone today. Due to the inconvenience of carrying an ATM card everywhere, people might forget to bring their card or PIN code. The ATM card could be broken, whichwould restrict the user from having access to theirmoney. An actual security solution is offered in this proposal. Technologies like Face recognition and Mobile app confirmation to increase the security of accounts and the privacy of users are included. When a user attempts to make a transaction after having their face recorded and stored in the bank's database, the system performs face detection using the A TM’ s camera and performs user face verification. If the invalid user needs to continue the transaction process, the OTP authentication should be made by the valid user in the Mobile application, so that the unauthorized person would continue the transaction.
{"title":"Face Detection based Secured ATM System with Two Step Verification using Fisher Face Method","authors":"V. Praveena, A. S., Anu Sankari S, Girija K, Kirthivarsini M","doi":"10.1109/ICOEI56765.2023.10125744","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125744","url":null,"abstract":"Automated teller machines (ATMs) are utilizedby almost everyone today. Due to the inconvenience of carrying an ATM card everywhere, people might forget to bring their card or PIN code. The ATM card could be broken, whichwould restrict the user from having access to theirmoney. An actual security solution is offered in this proposal. Technologies like Face recognition and Mobile app confirmation to increase the security of accounts and the privacy of users are included. When a user attempts to make a transaction after having their face recorded and stored in the bank's database, the system performs face detection using the A TM’ s camera and performs user face verification. If the invalid user needs to continue the transaction process, the OTP authentication should be made by the valid user in the Mobile application, so that the unauthorized person would continue the transaction.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124023633","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}
One of the way to think of railway signalling systems is as a collection of intricate systems that work together to control, supervise and safeguard railway operations. When there is a problem with the railway signalling system other safety measures are put in a place to keep the train running like slowing down, with the driver being responsible for keeping the train safe. In a nutshell, issues with the railway's capacity and safety result from malfunctions in the signalling system. A railway signalling system can be considered a group of complex systems that work together to provide control, supervision and protection of railway operations. The principles upon which railway signalling systems operate are extremely intricate. A railway scheduling algorithm can be used to optimize the train schedule, to minimize delays and traffic. The performance of the system is improved by optimizing the code running on the Arduino UNO. This minimizes memory usage, simplifying code and optimizing data processing methods.
{"title":"Railway Signalling System using Encoder and Decoder","authors":"M. Ghute, Ajinkya Barhate, Swejal Dhengle, Yachana Bakal, Sharwari Kawale, Devichand Rathod","doi":"10.1109/ICOEI56765.2023.10125937","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125937","url":null,"abstract":"One of the way to think of railway signalling systems is as a collection of intricate systems that work together to control, supervise and safeguard railway operations. When there is a problem with the railway signalling system other safety measures are put in a place to keep the train running like slowing down, with the driver being responsible for keeping the train safe. In a nutshell, issues with the railway's capacity and safety result from malfunctions in the signalling system. A railway signalling system can be considered a group of complex systems that work together to provide control, supervision and protection of railway operations. The principles upon which railway signalling systems operate are extremely intricate. A railway scheduling algorithm can be used to optimize the train schedule, to minimize delays and traffic. The performance of the system is improved by optimizing the code running on the Arduino UNO. This minimizes memory usage, simplifying code and optimizing data processing methods.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"70 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128002528","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125931
U. R. Babu, Tarun Gehlot, S. Thenmozhi, S. Chandre, A. Ravitheja, A. Gopi
Cracks in concrete allow aggressive chemicals to enter the reinforcement and cause corrosion, affecting reinforced concrete longevity. Crack identification is crucial to damage assessment. Visual examination is the most common concrete infrastructure monitoring method. Inspectors visually estimate flaws using skill, engineering judgment, and experience. However, this process is subjective, time-consuming, and requires access to numerous challenging structures. One progress hinges on improving or combining conventional digital image processing methods. Deep learning (DL) methods like CNN can now overcome image processing's crack detection limitations. This study introduces the Real-Time Building Crack Visual Measurement System utilizing Metaheuristics with Deep Learning (RBCVMS-MDL) model. RBCVMS-MDL detects construction cracks using DL principles. Three main steps are involved in RBCVMS-MDL. First, ResNet is used to build feature vectors. Salp Swarm Algorithm (SSA) also tunes ResNet method hyperparameters Finally, Radial Basis Function (RBF) can detect and classify cracks. RBCVMS-MDL outperforms other methods in crack image dataset performance validation.
{"title":"Real Time Building Crack Visual Measurement System using Metaheuristics with Deep Learning Model","authors":"U. R. Babu, Tarun Gehlot, S. Thenmozhi, S. Chandre, A. Ravitheja, A. Gopi","doi":"10.1109/ICOEI56765.2023.10125931","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125931","url":null,"abstract":"Cracks in concrete allow aggressive chemicals to enter the reinforcement and cause corrosion, affecting reinforced concrete longevity. Crack identification is crucial to damage assessment. Visual examination is the most common concrete infrastructure monitoring method. Inspectors visually estimate flaws using skill, engineering judgment, and experience. However, this process is subjective, time-consuming, and requires access to numerous challenging structures. One progress hinges on improving or combining conventional digital image processing methods. Deep learning (DL) methods like CNN can now overcome image processing's crack detection limitations. This study introduces the Real-Time Building Crack Visual Measurement System utilizing Metaheuristics with Deep Learning (RBCVMS-MDL) model. RBCVMS-MDL detects construction cracks using DL principles. Three main steps are involved in RBCVMS-MDL. First, ResNet is used to build feature vectors. Salp Swarm Algorithm (SSA) also tunes ResNet method hyperparameters Finally, Radial Basis Function (RBF) can detect and classify cracks. RBCVMS-MDL outperforms other methods in crack image dataset performance validation.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116903594","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125700
S. S, Sarang Dileep, Rahan Manoj, Adarsh M, Sandhya Harikumar
In this study, we compare various data visualization methods for exploring a complicated network dataset containing details on illnesses, symptoms, and safety measures. The dataset was obtained from Kaggle and split into train and test subsets at a 4:1 ratio. It has 269 nodes and 483 edges. To evaluate the network data, we used Neo4j and Gephi, two data visualization tools. The dataset was queried and visually analyzed using Neo4j, and graphical representations of the network were produced using Gephi. We tested the potency of different visualization methods for finding patterns and correlations in the data, including force-directed layouts, node-link diagrams, and matrix views. Moreover, Neo4j's querying capabilities allowed us to analyze sub-networks and their connections in greater detail. Overall, our study shows the value of using a variety of visualization methods to have a deeper understanding of complicated network data. Researchers, medical experts, and public health officials attempting to comprehend and manage illness linkages will find the findings of this study to be quite insightful.
{"title":"Comparing the Effectiveness of Data Visualization Techniques for Discovering Disease Relationships in a Complex Network Dataset","authors":"S. S, Sarang Dileep, Rahan Manoj, Adarsh M, Sandhya Harikumar","doi":"10.1109/ICOEI56765.2023.10125700","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125700","url":null,"abstract":"In this study, we compare various data visualization methods for exploring a complicated network dataset containing details on illnesses, symptoms, and safety measures. The dataset was obtained from Kaggle and split into train and test subsets at a 4:1 ratio. It has 269 nodes and 483 edges. To evaluate the network data, we used Neo4j and Gephi, two data visualization tools. The dataset was queried and visually analyzed using Neo4j, and graphical representations of the network were produced using Gephi. We tested the potency of different visualization methods for finding patterns and correlations in the data, including force-directed layouts, node-link diagrams, and matrix views. Moreover, Neo4j's querying capabilities allowed us to analyze sub-networks and their connections in greater detail. Overall, our study shows the value of using a variety of visualization methods to have a deeper understanding of complicated network data. Researchers, medical experts, and public health officials attempting to comprehend and manage illness linkages will find the findings of this study to be quite insightful.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658125","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 : 2023-04-11DOI: 10.1109/ICOEI56765.2023.10125764
Sonia Setia, A. Shukla, Amartya Raj, Abhimanyu Rathore
Nowadays., object detection has become very crucial in the area of computer vision. Many day-to-day activities require the use of technology that can help in vigilance such as traffic rules violations., road safety., etc. The detection techniques work on the images or videos and act as a model that provide the required area of interest from that input media. To solve these existing problems., different algorithms are available to perform object detection. This study focuses on reviewing the available algorithms to assist in the detection of object based on time and accuracy. The end result will help to identify the best available algorithm that can achieve faster object detection. The algorithms taken for the review process are CNN (Convolutional Neural Networks)., RCNN., Fast CNN., Faster RCNN., Single shot., YOLO (You Only Look Once).
{"title":"A Detailed Review on Object Detection Algorithms","authors":"Sonia Setia, A. Shukla, Amartya Raj, Abhimanyu Rathore","doi":"10.1109/ICOEI56765.2023.10125764","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125764","url":null,"abstract":"Nowadays., object detection has become very crucial in the area of computer vision. Many day-to-day activities require the use of technology that can help in vigilance such as traffic rules violations., road safety., etc. The detection techniques work on the images or videos and act as a model that provide the required area of interest from that input media. To solve these existing problems., different algorithms are available to perform object detection. This study focuses on reviewing the available algorithms to assist in the detection of object based on time and accuracy. The end result will help to identify the best available algorithm that can achieve faster object detection. The algorithms taken for the review process are CNN (Convolutional Neural Networks)., RCNN., Fast CNN., Faster RCNN., Single shot., YOLO (You Only Look Once).","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125262452","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}
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