Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702539
Pritika Aggarwal, Anshu Singla
Covid-19, an ongoing global pandemic, is one of the major reasons for the current disruption in the field of health and education. Although the Covid-19 led to a plethora of problems for students and negatively impacted the health and education of students, on the brighter side, it also exposed every country’s weakness and vulnerability to this situation and forced them to deal with the pandemic in innovative ways all the while ensuring the safety of their people. Schools were required to shift to an online platform, students and teachers were involuntary asked to adapt and adopt the same rapidly. At the same time, students observed fewer physical activities and both their mental and physical health were adversely affected. The present study focuses on ascertaining the impact of this Covid-19 Pandemic on the health and education of students, as well as propose solutions to tackle any challenges in the future.
{"title":"A survey to ascertain the impact of COVID-19 on education and health of students","authors":"Pritika Aggarwal, Anshu Singla","doi":"10.1109/ICIIP53038.2021.9702539","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702539","url":null,"abstract":"Covid-19, an ongoing global pandemic, is one of the major reasons for the current disruption in the field of health and education. Although the Covid-19 led to a plethora of problems for students and negatively impacted the health and education of students, on the brighter side, it also exposed every country’s weakness and vulnerability to this situation and forced them to deal with the pandemic in innovative ways all the while ensuring the safety of their people. Schools were required to shift to an online platform, students and teachers were involuntary asked to adapt and adopt the same rapidly. At the same time, students observed fewer physical activities and both their mental and physical health were adversely affected. The present study focuses on ascertaining the impact of this Covid-19 Pandemic on the health and education of students, as well as propose solutions to tackle any challenges in the future.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746460","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702686
Shramona Chakraborty, Dipanwita Roy Chowdhury
Feature Extraction is a long active topic in image processing, and it attracts lots of attention over the last five decades due to its uses in real-life applications. Edge detection method allows users to discover the features of an image for a notable change in the gray level. This texture indicates an ending of one region and the beginning of another in the image. Likewise, over the last few decades, researchers have been exploring to exploit the simple computing model of Cellular Automata (CA) with local neighborhood structure in image processing techniques. The usage of CA in applications of medical image-processing is identified by Kendall Preston [1] in 1979. It is found that edge detection using CA has a potential advantage over traditional approaches for its lightweight nature and computational efficiency. This paper introduces a new technique for image edge detection using two-dimensional CA. The method can use specific rule sets of CA using the Von Neumann neighborhood for edge detection with null boundary conditions. The performance analysis of the scheme is done and compared with some existing standard edge detection techniques. The results obtained from the proposed technique is very promising for feature extraction.
{"title":"Multiple Feature Extraction of Image using 2D CA","authors":"Shramona Chakraborty, Dipanwita Roy Chowdhury","doi":"10.1109/ICIIP53038.2021.9702686","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702686","url":null,"abstract":"Feature Extraction is a long active topic in image processing, and it attracts lots of attention over the last five decades due to its uses in real-life applications. Edge detection method allows users to discover the features of an image for a notable change in the gray level. This texture indicates an ending of one region and the beginning of another in the image. Likewise, over the last few decades, researchers have been exploring to exploit the simple computing model of Cellular Automata (CA) with local neighborhood structure in image processing techniques. The usage of CA in applications of medical image-processing is identified by Kendall Preston [1] in 1979. It is found that edge detection using CA has a potential advantage over traditional approaches for its lightweight nature and computational efficiency. This paper introduces a new technique for image edge detection using two-dimensional CA. The method can use specific rule sets of CA using the Von Neumann neighborhood for edge detection with null boundary conditions. The performance analysis of the scheme is done and compared with some existing standard edge detection techniques. The results obtained from the proposed technique is very promising for feature extraction.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"131 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129887511","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702663
Richa Verma, Ravinder Bhatt
There has been a fundamental shift in the way firms in every industry manage, examine, and utilize their data. Health care is one of the most promising industries in which the use of big data may make a positive impact. Healthcare technology is being improved at a fast rate as an outcome of growing information and innovative innovation. In healthcare, there are different articles of big data. Digital medical data, biometric data, medical image processing, biosensor data, physician data, patient information, and administrative data are examples of these types. Many combined technologies are being deployed to modify healthcare systems in the COVID-19 pandemic. The security of medical data is required for the management of an integrated healthcare solution. In this paper, we found that many researchers face significant hurdles in encrypting sensitive patient information to prevent misuse or leakage. Our aim is to provide a focus on security issues in healthcare system and try to give a solution.
{"title":"Security in Big Data Health Care System","authors":"Richa Verma, Ravinder Bhatt","doi":"10.1109/ICIIP53038.2021.9702663","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702663","url":null,"abstract":"There has been a fundamental shift in the way firms in every industry manage, examine, and utilize their data. Health care is one of the most promising industries in which the use of big data may make a positive impact. Healthcare technology is being improved at a fast rate as an outcome of growing information and innovative innovation. In healthcare, there are different articles of big data. Digital medical data, biometric data, medical image processing, biosensor data, physician data, patient information, and administrative data are examples of these types. Many combined technologies are being deployed to modify healthcare systems in the COVID-19 pandemic. The security of medical data is required for the management of an integrated healthcare solution. In this paper, we found that many researchers face significant hurdles in encrypting sensitive patient information to prevent misuse or leakage. Our aim is to provide a focus on security issues in healthcare system and try to give a solution.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301687","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702676
Adwait Mahadar, Priyen Mangukiya, T. Baraskar
At present, India has the largest population of below 14 years children in the Asia Pacific. With the increasing birth rate, critical Pneumonia cases have been referred to Neonatal Hospital for treatment. As the number of adults in India who have tested positive for COVID-19 has grown, so has the number of children who have contracted it. However, we haven't noticed a dramatic increase in the number of children infected with COVID-19 across the country. It's important to note that, unlike the previous wave, the second wave is more likely to infect whole homes. We must be vigilant and adhere to COVID-19's recommended practices. The current study says that the mortality rate of Pneumonia and Covid-19 infection in rural areas is high. Radiology plays a vital role to diagnose Pneumonia by the examination of X-Ray images. Over the years CNN Architectures have evolved and now produce appreciable accuracy (over 85%) for classification tasks. This has promoted the use of CNN Architectures in the field of medicine, especially for classification tasks such as disease detection from x-rays. This implementation evaluates the performance of four popular CNN Architectures viz. VGG16, ResNet50V2, InceptionV3 and MobileNetV2. The implementation will classify x-ray images into normal, covid, and pneumonia and then compare the performance of the aforementioned models over the accuracy, Area under the curve (AUC), precision, recall metrics.
{"title":"Comparison and Evaluation of CNN Architectures for Classification of Covid-19 and Pneumonia","authors":"Adwait Mahadar, Priyen Mangukiya, T. Baraskar","doi":"10.1109/ICIIP53038.2021.9702676","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702676","url":null,"abstract":"At present, India has the largest population of below 14 years children in the Asia Pacific. With the increasing birth rate, critical Pneumonia cases have been referred to Neonatal Hospital for treatment. As the number of adults in India who have tested positive for COVID-19 has grown, so has the number of children who have contracted it. However, we haven't noticed a dramatic increase in the number of children infected with COVID-19 across the country. It's important to note that, unlike the previous wave, the second wave is more likely to infect whole homes. We must be vigilant and adhere to COVID-19's recommended practices. The current study says that the mortality rate of Pneumonia and Covid-19 infection in rural areas is high. Radiology plays a vital role to diagnose Pneumonia by the examination of X-Ray images. Over the years CNN Architectures have evolved and now produce appreciable accuracy (over 85%) for classification tasks. This has promoted the use of CNN Architectures in the field of medicine, especially for classification tasks such as disease detection from x-rays. This implementation evaluates the performance of four popular CNN Architectures viz. VGG16, ResNet50V2, InceptionV3 and MobileNetV2. The implementation will classify x-ray images into normal, covid, and pneumonia and then compare the performance of the aforementioned models over the accuracy, Area under the curve (AUC), precision, recall metrics.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133662154","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702576
S. Nigam, R. Singh, M. Kumar Singh, V. Kumar Singh
Great efforts are made for the recognition of a person’s activity, still it is challenging research domain in security and surveillance. This paper proposes an efficient framework to recognize activities captured from multiple views by incorporating cameras placed at different viewing angle. These viewpoints may be horizontal, vertical, top-down as well as several others. Sometimes three cameras are placed for this purpose whereas sometimes number of cameras may be five or eight. The framework includes 3 consecutive modules that are: to locate humans in a video using background subtraction method, to extract uniform LBP and to classify human actions/activities using SVM multiclass classifier with OVA architecture. The rotation invariant characteristic of LBP supports in human activity classification from multiple views. In addition to this, better discrimination capability of these patterns provides high efficiency to the proposed framework. A hierarchical classification technique has been implemented and multiple SVMs are aggregated to classify human activities. Experimentation was performed on CASIA and IXMAS activity datasets and demonstrates the effectiveness of the proposed framework for multiple views.
{"title":"Multiple Views Based Recognition of Human Activities using Uniform Patterns","authors":"S. Nigam, R. Singh, M. Kumar Singh, V. Kumar Singh","doi":"10.1109/ICIIP53038.2021.9702576","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702576","url":null,"abstract":"Great efforts are made for the recognition of a person’s activity, still it is challenging research domain in security and surveillance. This paper proposes an efficient framework to recognize activities captured from multiple views by incorporating cameras placed at different viewing angle. These viewpoints may be horizontal, vertical, top-down as well as several others. Sometimes three cameras are placed for this purpose whereas sometimes number of cameras may be five or eight. The framework includes 3 consecutive modules that are: to locate humans in a video using background subtraction method, to extract uniform LBP and to classify human actions/activities using SVM multiclass classifier with OVA architecture. The rotation invariant characteristic of LBP supports in human activity classification from multiple views. In addition to this, better discrimination capability of these patterns provides high efficiency to the proposed framework. A hierarchical classification technique has been implemented and multiple SVMs are aggregated to classify human activities. Experimentation was performed on CASIA and IXMAS activity datasets and demonstrates the effectiveness of the proposed framework for multiple views.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133068199","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702647
Richa Singh, Nidhi Srivastava, Ashwani Kumar
In today's technological era, anomaly detection is a major concern in front of network users. Due to the development of various network techniques, network users are also increased which leads to more traffic on the network, and due to this, it's very difficult to recognize the anomalous patterns. This paper discussed the overview of various ML techniques used to solve the problem of anomaly detection along with their pros and cons and also discussed here the framework/model’s accuracy level. In this survey, strategies for identifying and mitigating abnormalities in network traffic are discussed and compared the result in terms of its accuracy and anomaly types. The current research gaps and important research concerns in network traffic anomaly detection are presented in detail. We hope that the analysis, comparisons, and after that, the identification of gaps will point out the researchers in the right direction for doing advanced development in this field.
{"title":"Machine Learning Techniques for Anomaly Detection in Network Traffic","authors":"Richa Singh, Nidhi Srivastava, Ashwani Kumar","doi":"10.1109/ICIIP53038.2021.9702647","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702647","url":null,"abstract":"In today's technological era, anomaly detection is a major concern in front of network users. Due to the development of various network techniques, network users are also increased which leads to more traffic on the network, and due to this, it's very difficult to recognize the anomalous patterns. This paper discussed the overview of various ML techniques used to solve the problem of anomaly detection along with their pros and cons and also discussed here the framework/model’s accuracy level. In this survey, strategies for identifying and mitigating abnormalities in network traffic are discussed and compared the result in terms of its accuracy and anomaly types. The current research gaps and important research concerns in network traffic anomaly detection are presented in detail. We hope that the analysis, comparisons, and after that, the identification of gaps will point out the researchers in the right direction for doing advanced development in this field.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132552347","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702696
Nayan Varma Alluri, Neeli Dheeraj Krishna
Memes are one of the most ubiquitous forms of social media communication. The study and processing of memes, which are intrinsically multimedia, is a popular topic right now. The study presented in this research is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. Three separate innovative transformer-based techniques have been developed, and their outcomes have been thoroughly reviewed.The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme out of all our techniques.
{"title":"Multi Modal Analysis of memes for Sentiment extraction","authors":"Nayan Varma Alluri, Neeli Dheeraj Krishna","doi":"10.1109/ICIIP53038.2021.9702696","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702696","url":null,"abstract":"Memes are one of the most ubiquitous forms of social media communication. The study and processing of memes, which are intrinsically multimedia, is a popular topic right now. The study presented in this research is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. Three separate innovative transformer-based techniques have been developed, and their outcomes have been thoroughly reviewed.The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme out of all our techniques.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"678 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130044648","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702608
A. Kubde, Sharad W. Mohod
Diabetic retinopathy is a potentially fatal condition that affects diabetics worldwide, resulting in blurred vision or total blindness. A technique for identifying diabetic retinopathy using the fundus image obtained from the patient's retina is proposed in this paper. The method entails processing a digital image of the fundus image, which assists the ophthalmologist in examining DR. A neural network was utilized to diagnose a micro-aneurysm, a type of diabetic retinopathy that is the first stage. A comparison was made between the proposed Support Vector Machine and the existing Naive Bayes classifier. For experimental validation, the programed MATLAB/SIMULINK is employed. The preprocess image was used as input data for pattern recognition using a neural network. There has been a significant improvement in terms of sensitivity, specificity, and accuracy when compared to the aforementioned existing techniques.
{"title":"Automated Computer Aided Detection of Diabetic Retinopathy Using Machine Learning Hybrid Model","authors":"A. Kubde, Sharad W. Mohod","doi":"10.1109/ICIIP53038.2021.9702608","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702608","url":null,"abstract":"Diabetic retinopathy is a potentially fatal condition that affects diabetics worldwide, resulting in blurred vision or total blindness. A technique for identifying diabetic retinopathy using the fundus image obtained from the patient's retina is proposed in this paper. The method entails processing a digital image of the fundus image, which assists the ophthalmologist in examining DR. A neural network was utilized to diagnose a micro-aneurysm, a type of diabetic retinopathy that is the first stage. A comparison was made between the proposed Support Vector Machine and the existing Naive Bayes classifier. For experimental validation, the programed MATLAB/SIMULINK is employed. The preprocess image was used as input data for pattern recognition using a neural network. There has been a significant improvement in terms of sensitivity, specificity, and accuracy when compared to the aforementioned existing techniques.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365112","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}
Weather is the everyday climate that is hard to predict and impacts human activities and is important in many different sectors. However, it is expensive and huge, bringing pain to current meteorological stations on the market. The aim is to develop a weather station that provides real-time warnings for climate monitoring, interfaces with a cloud platform and analyses weather. This project has been completed with a Weather Station to record weather conditions by means of SparkFun Weather Shield, Arduino Uno R3 and Weather Meter. Data from the sensors are then recorded using Raspberry Pi 3 Model B in Google Cloud SQL, where they are gateway and the analysis of meteorological data is done. The website and mobile application are designed to illustrate real-time weather conditions for managing and users using Google Data Studio and Android Studio. In this Article various environmental components may be monitored in real time utilising IoT at minimum costs. For this reason, we use the ARM-based Raspberry Pi board. The Raspberry Pi OS is selected for the Linux kernel. Python, as the Idle is understood, is a programming language. A wide range of digital and analogue sensors, such DHT11, BMP180, LDR and a distinctive scale, are used with ULN2803 for measuring the environment parameter. The Raspberry Pi server, which is saved to CSV and text files, reads these input sensor data. Customers may obtain this information from anywhere in the globe on stuffpeak.com in real time. To connect the server to the client, use the HTTP protocol.
天气是难以预测的日常气候，影响人类活动，在许多不同领域都很重要。然而，它既昂贵又庞大，给目前市场上的气象站带来了痛苦。目标是开发一个气象站，为气候监测提供实时预警，与云平台接口并分析天气。这个项目已经完成了一个气象站，通过SparkFun天气屏蔽，Arduino Uno R3和气象仪来记录天气状况。来自传感器的数据然后在谷歌云SQL中使用树莓派3模型B进行记录，在那里它们是网关和气象数据分析。该网站和移动应用程序旨在为使用谷歌数据工作室和安卓工作室的管理和用户展示实时天气状况。在本文中，可以利用物联网以最低成本实时监控各种环境组件。出于这个原因，我们使用基于arm的树莓派板。Linux内核选择树莓派操作系统。正如Idle所理解的那样，Python是一种编程语言。ULN2803采用多种数字和模拟传感器，如DHT11、BMP180、LDR和独特的刻度，用于测量环境参数。树莓派服务器(保存为CSV和文本文件)读取这些输入的传感器数据。客户可以在全球任何地方的stuffpeak.com上实时获取这些信息。为了将服务器连接到客户端，使用HTTP协议。
{"title":"Weather Station Using Raspberry Pi","authors":"V. Mathur, Yashika Saini, Vipul Giri, Vikas Choudhary, Uday Bharadwaj, Vishal Kumar","doi":"10.1109/ICIIP53038.2021.9702687","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702687","url":null,"abstract":"Weather is the everyday climate that is hard to predict and impacts human activities and is important in many different sectors. However, it is expensive and huge, bringing pain to current meteorological stations on the market. The aim is to develop a weather station that provides real-time warnings for climate monitoring, interfaces with a cloud platform and analyses weather. This project has been completed with a Weather Station to record weather conditions by means of SparkFun Weather Shield, Arduino Uno R3 and Weather Meter. Data from the sensors are then recorded using Raspberry Pi 3 Model B in Google Cloud SQL, where they are gateway and the analysis of meteorological data is done. The website and mobile application are designed to illustrate real-time weather conditions for managing and users using Google Data Studio and Android Studio. In this Article various environmental components may be monitored in real time utilising IoT at minimum costs. For this reason, we use the ARM-based Raspberry Pi board. The Raspberry Pi OS is selected for the Linux kernel. Python, as the Idle is understood, is a programming language. A wide range of digital and analogue sensors, such DHT11, BMP180, LDR and a distinctive scale, are used with ULN2803 for measuring the environment parameter. The Raspberry Pi server, which is saved to CSV and text files, reads these input sensor data. Customers may obtain this information from anywhere in the globe on stuffpeak.com in real time. To connect the server to the client, use the HTTP protocol.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114149444","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702609
Shaveta Arora, Meghna Sharma
Magnetic Resonance Imaging popularly known as MRI is one of the primary scans to visualize the brain tumor. The detailed pictures obtained from MRI when processed using deep learning methods help the neurologist in classifying brain tumor. The paper shows the exploratory analysis of brain MRI images based on extracted features and also a comparative analysis of different CNN based transfer learning models for the classification of MRI images for brain tumor. It shows the efficiency of deep learning techniques for the detection of brain cancer from the MRI images of the brain. The performance is measured in terms of training accuracy and test accuracy. Here binary classification is done with no tumor and with tumor classes. The goal of our study is to accurately detect tumors in the brain and classify it through the means of several techniques involving medical image processing, pattern analysis, and computer vision for enhancement, segmentation and classification of brain diagnosis.
{"title":"Deep Learning for Brain Tumor Classification from MRI Images","authors":"Shaveta Arora, Meghna Sharma","doi":"10.1109/ICIIP53038.2021.9702609","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702609","url":null,"abstract":"Magnetic Resonance Imaging popularly known as MRI is one of the primary scans to visualize the brain tumor. The detailed pictures obtained from MRI when processed using deep learning methods help the neurologist in classifying brain tumor. The paper shows the exploratory analysis of brain MRI images based on extracted features and also a comparative analysis of different CNN based transfer learning models for the classification of MRI images for brain tumor. It shows the efficiency of deep learning techniques for the detection of brain cancer from the MRI images of the brain. The performance is measured in terms of training accuracy and test accuracy. Here binary classification is done with no tumor and with tumor classes. The goal of our study is to accurately detect tumors in the brain and classify it through the means of several techniques involving medical image processing, pattern analysis, and computer vision for enhancement, segmentation and classification of brain diagnosis.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116298665","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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