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/ICOEI56765.2023.10125925
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/ICOEI56765.2023.10125634
P. Manikandan, K. K. Babu, G. M. Reddy, G. Kalayan, V. Muneeswaran
In the midst of a technological revolution that is set to drastically alter human daily lives and may even redefine the concept of humanity. One area where technology is being implemented is in canteen management systems, which offer a convenient way for university students and staff to order food without having to physically go to the cafeteria and wait in long queues. This research work proposes a canteen management system that utilizes an ARM processor, Bluetooth module, thermal printer, Liquid Crystal Display (LCD), and an Android application. The Android app dis plays the food menu with prices and ratings, allowing users to remotely order their food from within the canteen. This saves time for students and staff, who no longer must stand in line for extended periods. The canteen cashier can see the orders and print the bill once the user has paid. This system is designed to streamline the food ordering process and reduce wait times. Many universities do not have a food order collection system, forcing students to go directly to the counter and place an order, which is a time-consuming process. This system aims to solve this problem and provide a more efficient way for students and staff to order food.
{"title":"KARE - Presto Canteen Management System with an Android Application","authors":"P. Manikandan, K. K. Babu, G. M. Reddy, G. Kalayan, V. Muneeswaran","doi":"10.1109/ICOEI56765.2023.10125634","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125634","url":null,"abstract":"In the midst of a technological revolution that is set to drastically alter human daily lives and may even redefine the concept of humanity. One area where technology is being implemented is in canteen management systems, which offer a convenient way for university students and staff to order food without having to physically go to the cafeteria and wait in long queues. This research work proposes a canteen management system that utilizes an ARM processor, Bluetooth module, thermal printer, Liquid Crystal Display (LCD), and an Android application. The Android app dis plays the food menu with prices and ratings, allowing users to remotely order their food from within the canteen. This saves time for students and staff, who no longer must stand in line for extended periods. The canteen cashier can see the orders and print the bill once the user has paid. This system is designed to streamline the food ordering process and reduce wait times. Many universities do not have a food order collection system, forcing students to go directly to the counter and place an order, which is a time-consuming process. This system aims to solve this problem and provide a more efficient way for students and staff to order food.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"77 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":"131646829","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/ICOEI56765.2023.10125628
N. Srikanta, M. Pachiyaannan
This article presents and analyses a low profile truncated hexagonal nested rings patch antenna for use in various wireless applications. This dual band antenna is fed by a 50 ohm microstrip feed line and is made of truncated nested hexagonal rings-shaped radiating patch elements on a single layer 1.6 mm thick FR4 substrate. To achieve adequate impedance matching between the antenna and the source, the truncated patch with partial ground plane is used. While the S11value is less than −10 dB, the designed antenna functions at two different frequency bands, including 4.6-6.7 GHz, and 12 GHz to 14.2 GHz. In order to test the antenna operation at the two distinct frequency bands and optimise the design, the Ansoft High Frequency Structure Simulator (HFSS) is used. At two frequency bands, the developed antenna shows greater gain. To verify its performance, the dual band antenna underwent testing and prototype development. The simulation outcomes demonstrate a fair level of agreement with the measurement outcomes.
{"title":"A Dual Band Fractal Antenna with Truncated Hexagonal Nested Rings for Wireless Applications","authors":"N. Srikanta, M. Pachiyaannan","doi":"10.1109/ICOEI56765.2023.10125628","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125628","url":null,"abstract":"This article presents and analyses a low profile truncated hexagonal nested rings patch antenna for use in various wireless applications. This dual band antenna is fed by a 50 ohm microstrip feed line and is made of truncated nested hexagonal rings-shaped radiating patch elements on a single layer 1.6 mm thick FR4 substrate. To achieve adequate impedance matching between the antenna and the source, the truncated patch with partial ground plane is used. While the S11value is less than −10 dB, the designed antenna functions at two different frequency bands, including 4.6-6.7 GHz, and 12 GHz to 14.2 GHz. In order to test the antenna operation at the two distinct frequency bands and optimise the design, the Ansoft High Frequency Structure Simulator (HFSS) is used. At two frequency bands, the developed antenna shows greater gain. To verify its performance, the dual band antenna underwent testing and prototype development. The simulation outcomes demonstrate a fair level of agreement with the measurement outcomes.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"109 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":"117353412","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/ICOEI56765.2023.10125698
Nakayiza Hellen, Ggaliwango Marvin
The ongoing digital revolution is having a significant impact on homes and communities worldwide, affecting access to information, communication, learning, and sports. One of the most significant changes brought about by this revolution is the shift from traditional classroom-based education to virtual and hybrid online learning environments. Higher education institutions, in particular, are recognizing the value of online educational programs, which allow them to expand their digital pre se n ce, increase access to their programs, and reach students beyond their physical borders. The advancements in educational technology made possible by the 4th Industrial Revolution are also allowing for more flexible, engaging, and accessible learning experiences for both students and teachers. However, there remains a significant gap in terms of education planning, access to digital learning tools, and engagement among stakeholders. This research uses data analytics to examine cl oud-based digital learning tools, education stakeholder engagement, and education access. The findings provide insight for academic stakeholders, particularly governments, private sector, and educational investors, on ways to bridge the gaps between access and engagement for students and teachers.
{"title":"Learning Analytics for Cloud-based Education Planning","authors":"Nakayiza Hellen, Ggaliwango Marvin","doi":"10.1109/ICOEI56765.2023.10125698","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125698","url":null,"abstract":"The ongoing digital revolution is having a significant impact on homes and communities worldwide, affecting access to information, communication, learning, and sports. One of the most significant changes brought about by this revolution is the shift from traditional classroom-based education to virtual and hybrid online learning environments. Higher education institutions, in particular, are recognizing the value of online educational programs, which allow them to expand their digital pre se n ce, increase access to their programs, and reach students beyond their physical borders. The advancements in educational technology made possible by the 4th Industrial Revolution are also allowing for more flexible, engaging, and accessible learning experiences for both students and teachers. However, there remains a significant gap in terms of education planning, access to digital learning tools, and engagement among stakeholders. This research uses data analytics to examine cl oud-based digital learning tools, education stakeholder engagement, and education access. The findings provide insight for academic stakeholders, particularly governments, private sector, and educational investors, on ways to bridge the gaps between access and engagement for students and teachers.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"75 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":"114762025","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/ICOEI56765.2023.10126052
A. Palaniammal, P. Anandababu
Sarcasm is a procedure of verbal irony that is planned to convey ridicule, contempt or mockery with the aid of words that expresses the opposite of what is meant or through facial expression, tone of voice, or inflection. In another word, it is a way of saying something but meaning the opposite, often intending to be critical or humorous. Sarcasm is widely applied in social media, humour, and casual conversation. Sarcasm detection using deep learning (DL) includes training a machine learning (ML) algorithm for identifying instances of sarcasm and recognizing the pattern in language. The study presents a new Chaos Sine Cosine Algorithm with Graph Convolution Network for Sarcasm Detection (CSCA-GCNSD) technique in Social Media. The presented CSCA-GCNSD technique aims to recognize and categorize various kinds of sarcasm. Primarily, the CSCA-GCNSD technique involves different stages of data pre-processing. Next, the CSCA-GCNSD technique applies the GCN model for the detection and classification of various kinds of sarcasm. Finally, the CSCA technique is used to optimally choose the hyperparameter values of the GCN model and thereby resulting in improved detection outcomes. The simulation outcomes of the CSCA-GCNSD methodology was tested on different sarcasm datasets and the outcomes reported the betterment of the CSCA-GCNSD algorithms over other models.
{"title":"Chaos Sine Cosine Algorithm with Graph Convolution Network for Sarcasm Detection in Social Media","authors":"A. Palaniammal, P. Anandababu","doi":"10.1109/ICOEI56765.2023.10126052","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10126052","url":null,"abstract":"Sarcasm is a procedure of verbal irony that is planned to convey ridicule, contempt or mockery with the aid of words that expresses the opposite of what is meant or through facial expression, tone of voice, or inflection. In another word, it is a way of saying something but meaning the opposite, often intending to be critical or humorous. Sarcasm is widely applied in social media, humour, and casual conversation. Sarcasm detection using deep learning (DL) includes training a machine learning (ML) algorithm for identifying instances of sarcasm and recognizing the pattern in language. The study presents a new Chaos Sine Cosine Algorithm with Graph Convolution Network for Sarcasm Detection (CSCA-GCNSD) technique in Social Media. The presented CSCA-GCNSD technique aims to recognize and categorize various kinds of sarcasm. Primarily, the CSCA-GCNSD technique involves different stages of data pre-processing. Next, the CSCA-GCNSD technique applies the GCN model for the detection and classification of various kinds of sarcasm. Finally, the CSCA technique is used to optimally choose the hyperparameter values of the GCN model and thereby resulting in improved detection outcomes. The simulation outcomes of the CSCA-GCNSD methodology was tested on different sarcasm datasets and the outcomes reported the betterment of the CSCA-GCNSD algorithms over other models.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"681 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":"116108508","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/ICOEI56765.2023.10126051
Sowmen Mitra, P. Kanungoe
Recognition of human activities is essential for many applications, and the widespread availability of low-cost sensors on smartphones and wearables has enabled the development of mobile apps capable of tracking user activities “in the wild.” However, dealing with heterogeneous data from different devices and real-time scenarios presents significant challenges. In this study, a novel learning framework is proposed for Human Activity Recognition (HAR) that combines a Convolutional Neural Network (CNN) with an autoencoder for feature extraction. The study also investigates the importance of preprocessing techniques, including orientation-independent transformation, to mitigate heterogeneity when dealing with multiple types of smartphones. The results show that the proposed approach outperforms state-of-the-art methods in HAR, with an accuracy of 95.74% on the heterogeneous dataset used in this study. Furthermore, the study demonstrates that proposed framework can be effectively deployed on smartphones with limited computational resources, making it suitable for real-world applications.
{"title":"Smartphone based Human Activity Recognition using CNNs and Autoencoder Features","authors":"Sowmen Mitra, P. Kanungoe","doi":"10.1109/ICOEI56765.2023.10126051","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10126051","url":null,"abstract":"Recognition of human activities is essential for many applications, and the widespread availability of low-cost sensors on smartphones and wearables has enabled the development of mobile apps capable of tracking user activities “in the wild.” However, dealing with heterogeneous data from different devices and real-time scenarios presents significant challenges. In this study, a novel learning framework is proposed for Human Activity Recognition (HAR) that combines a Convolutional Neural Network (CNN) with an autoencoder for feature extraction. The study also investigates the importance of preprocessing techniques, including orientation-independent transformation, to mitigate heterogeneity when dealing with multiple types of smartphones. The results show that the proposed approach outperforms state-of-the-art methods in HAR, with an accuracy of 95.74% on the heterogeneous dataset used in this study. Furthermore, the study demonstrates that proposed framework can be effectively deployed on smartphones with limited computational resources, making it suitable for real-world applications.","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":"123684391","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/ICOEI56765.2023.10125652
Phanitha Sai Lakshmi Veeranki, Gaja Lakshmi Banavath, P. R. Devi
According to statistics from WHO, brain tumors will account for roughly 9.5 million deaths globally in the next few decades. Early identification and treatment are the best ways to stop deaths from brain cancer. Brain tumors fall into two categories: benign, which is not cancerous, and malignant, which is cancerous. A brain tumor that originates in a specific location and then metastasizes to other regions of the body, including other areas of the brain, is referred to as a primary tumor. Secondary tumors, commonly referred to as metastatic tumors, arise from primary tumors. It is now possible to more easily analyze medical pictures thanks to the quick development of image processing and soft computing technologies that aid in early detection and therapy. The use of computer-aided diagnostic (CAD) technology for diagnosing illnesses, predicting prognoses, and determining the likelihood of recurrence is expanding as a result of technological improvements. The main area of investigation in this study is the utilization of feature extraction and tumor cell classification for the automatic identification and categorization of brain tumors in magnetic resonance imaging (MRI) scans. Brain tumor detection and classification are done using CNN, and VGG-16 models. Accuracy is obtained by doing a comparative study of these two models. VGG-16 is the best-trained model.
{"title":"Detection and Classification of Brain Tumors using Convolutional Neural Network","authors":"Phanitha Sai Lakshmi Veeranki, Gaja Lakshmi Banavath, P. R. Devi","doi":"10.1109/ICOEI56765.2023.10125652","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125652","url":null,"abstract":"According to statistics from WHO, brain tumors will account for roughly 9.5 million deaths globally in the next few decades. Early identification and treatment are the best ways to stop deaths from brain cancer. Brain tumors fall into two categories: benign, which is not cancerous, and malignant, which is cancerous. A brain tumor that originates in a specific location and then metastasizes to other regions of the body, including other areas of the brain, is referred to as a primary tumor. Secondary tumors, commonly referred to as metastatic tumors, arise from primary tumors. It is now possible to more easily analyze medical pictures thanks to the quick development of image processing and soft computing technologies that aid in early detection and therapy. The use of computer-aided diagnostic (CAD) technology for diagnosing illnesses, predicting prognoses, and determining the likelihood of recurrence is expanding as a result of technological improvements. The main area of investigation in this study is the utilization of feature extraction and tumor cell classification for the automatic identification and categorization of brain tumors in magnetic resonance imaging (MRI) scans. Brain tumor detection and classification are done using CNN, and VGG-16 models. Accuracy is obtained by doing a comparative study of these two models. VGG-16 is the best-trained model.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"40 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":"121711147","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}
Covid-19 diagnosis systems are being improved with the emerging development of deep learning techniques. Covid-19 is widely known for the deadly effects and its high transmission rate. To overcome the challenges, different deep learning-based detection methods have been introduced through which the disease can easily be identified in patient's body. But only identification of the disease is not sufficient to assist physicians for further diagnosis. Infection identification process with severity measurement from medical image can put an advancement in current Covid-19 diagnosis systems. This work presents a novel infection detection approach based on image segmentation technique that can be used to localize the infection. The proposed system is able to predict segmented lung and mask images with visual representation so that it makes the diagnosis task easier for the physicians. ResNet-U-N et, VGG16-U-Net and a modified U-Net model have been implemented in the proposed work where the modified U-Net performed better with 0.968 IoU, 98.60% accuracy and 0.9567 of dice coefficient. An advanced module using OpenCV has been designed that can calculate the area of the predicted lung and infection mask images separately and then the infection percentage can be calculated accurately.
{"title":"A Novel Deep Learning-based Approach for Covid-19 Infection Identification in Chest X-ray Image using Improved Image Segmentation Technique","authors":"Gouri Shankar Chakraborty, Salil Batra, Makul Mahajan","doi":"10.1109/ICOEI56765.2023.10125745","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125745","url":null,"abstract":"Covid-19 diagnosis systems are being improved with the emerging development of deep learning techniques. Covid-19 is widely known for the deadly effects and its high transmission rate. To overcome the challenges, different deep learning-based detection methods have been introduced through which the disease can easily be identified in patient's body. But only identification of the disease is not sufficient to assist physicians for further diagnosis. Infection identification process with severity measurement from medical image can put an advancement in current Covid-19 diagnosis systems. This work presents a novel infection detection approach based on image segmentation technique that can be used to localize the infection. The proposed system is able to predict segmented lung and mask images with visual representation so that it makes the diagnosis task easier for the physicians. ResNet-U-N et, VGG16-U-Net and a modified U-Net model have been implemented in the proposed work where the modified U-Net performed better with 0.968 IoU, 98.60% accuracy and 0.9567 of dice coefficient. An advanced module using OpenCV has been designed that can calculate the area of the predicted lung and infection mask images separately and then the infection percentage can be calculated accurately.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 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":"129410676","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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