ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images were used in the experiments, which involved the training of deep learning and machine learning classifiers. Experiments were performed using convolutional neural networks and machine learning models. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.
{"title":"CoviDecode : Detection of COVID-19 from Chest\u0000X-Ray images using Convolutional Neural\u0000Networks","authors":"Rishabh Raj","doi":"10.46501/ijmtst061283","DOIUrl":"https://doi.org/10.46501/ijmtst061283","url":null,"abstract":"ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory\u0000syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19),\u0000using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries\u0000that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed\u0000to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images.\u0000Publicly available X-ray images were used in the experiments, which involved the training of deep learning\u0000and machine learning classifiers. Experiments were performed using convolutional neural networks and\u0000machine learning models. Images and statistical data were considered separately in the experiments to\u0000evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%.\u0000A convolutional neural network without pre-processing and with minimized layers is capable of detecting\u0000COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79358470","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}
As the world is changing rapidly and there has been a significant increase in the number of technologies which are getting introduced over the years to bridge the gap between the people. Video conferencing is one of the that which has seen a significant growth over the years. Understanding what are required for video conferencing and its application has become one amongst the foremost important researched topics by various learning institutions and businessmen. In this paper, an introduction to video conferencing is presented with the strain on its application in distance learning.
{"title":"Video Conference Application using Android\u0000and Firebase","authors":"Rahul Roy and SeemaKalonia","doi":"10.46501/ijmtst061289","DOIUrl":"https://doi.org/10.46501/ijmtst061289","url":null,"abstract":"As the world is changing rapidly and there has been a significant increase in the number of technologies\u0000which are getting introduced over the years to bridge the gap between the people. Video conferencing is one\u0000of the that which has seen a significant growth over the years. Understanding what are required for video\u0000conferencing and its application has become one amongst the foremost important researched topics by\u0000various learning institutions and businessmen. In this paper, an introduction to video conferencing is\u0000presented with the strain on its application in distance learning.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"30 8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86483378","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}
The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.
{"title":"Heart Disease Prediction and Classification Using\u0000Machine Learning Algorithms Optimized by\u0000Particle Swarm Optimization and Ant Colony\u0000Optimization","authors":"Aditya, Lalit and Mantosh Kumar","doi":"10.46501/ijmtst061282","DOIUrl":"https://doi.org/10.46501/ijmtst061282","url":null,"abstract":"The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization\u0000algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and\u0000adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter\u0000redundant features in order to improve the quality of heart disease classification. Then, we perform a\u0000classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector\u0000Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by\u0000Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed\u0000mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of\u0000the proposed hybrid method in processing various types of data for heart disease classification. Therefore,\u0000this study examines the different machine learning algorithms and compares the results using different\u0000performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of\u000099.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance\u0000of the proposed system is superior to that of the classification technique presented above.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86337119","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}
Data,Information or knoweldge,in this rapidly moving and growing world.we can find any kind of information on Internet.And this can be too useful,however for acedemic world too it is useful but along with it plagarism is highly in practice.Which makes orginality of work degrade and fraudly using someones original work and later not acknowleging them is becoming common.And some times teachers or professors could not identify the plagarised information provided.So higher educational systems nowadays use different types of tools to compare.Here we have an idea to match no of different documents like assignments of students to compare with each other to find out, did they copied each other’s work?Also an idea to compare ideal answeer sheet of particular subject examination to similar test sheets of students.Idea is to compare and on similarity basis we can rank them.Both approach is one kind and that is to compare documents.To identify plagarism there are many methods used already.So we could compare and develop them if needed.
{"title":"Natural Language Processing methods for\u0000Document Matching","authors":"Maitri Patel and Dr Hemant D Vasava","doi":"10.46501/ijmtst061271","DOIUrl":"https://doi.org/10.46501/ijmtst061271","url":null,"abstract":"Data,Information or knoweldge,in this rapidly moving and growing world.we can find any kind of information\u0000on Internet.And this can be too useful,however for acedemic world too it is useful but along with it plagarism\u0000is highly in practice.Which makes orginality of work degrade and fraudly using someones original work and\u0000later not acknowleging them is becoming common.And some times teachers or professors could not identify\u0000the plagarised information provided.So higher educational systems nowadays use different types of tools to\u0000compare.Here we have an idea to match no of different documents like assignments of students to compare\u0000with each other to find out, did they copied each other’s work?Also an idea to compare ideal answeer sheet of\u0000particular subject examination to similar test sheets of students.Idea is to compare and on similarity basis\u0000we can rank them.Both approach is one kind and that is to compare documents.To identify plagarism there\u0000are many methods used already.So we could compare and develop them if needed.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85844340","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}
Video conferencing may be a technology that permits users in several locations to carry face-to-face meetings without having to maneuver to one location together. Uses for video conferencing include holding routine meetings, negotiating business deals, and interviewing job candidates
{"title":"Open Source Software, Low Cost Video\u0000Conferencing Solution","authors":"Prasanna Bisen","doi":"10.46501/ijmtst061277","DOIUrl":"https://doi.org/10.46501/ijmtst061277","url":null,"abstract":"Video conferencing may be a technology that permits users in several locations to carry face-to-face\u0000meetings without having to maneuver to one location together. Uses for video conferencing include holding\u0000routine meetings, negotiating business deals, and interviewing job candidates","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"96 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80305914","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}
Image recognition plays a foundational role in the field of computer vision and there has been extensive research to develop state-of-the-art techniques especially using Convolutional Neural Network (CNN). This paper aims to study some CNNs, heavily inspired by highly popular state-of-the-art CNNs, designed from scratch specifically for the Cifar-10 dataset and present a fair comparison between them.
{"title":"Comparative Study of Various Convolutional\u0000Neural Networks on Cifar-10","authors":"Tushar Goyal","doi":"10.46501/ijmtst061276","DOIUrl":"https://doi.org/10.46501/ijmtst061276","url":null,"abstract":"Image recognition plays a foundational role in the field of computer vision and there has been extensive\u0000research to develop state-of-the-art techniques especially using Convolutional Neural Network (CNN). This\u0000paper aims to study some CNNs, heavily inspired by highly popular state-of-the-art CNNs, designed from\u0000scratch specifically for the Cifar-10 dataset and present a fair comparison between them.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83199354","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}
Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning and recognizing patterns from data that is unstructured or unlabelled. It is also known as deep neural learning or deep neural network. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars. ConvNets can also be used for Radio Imaging which helps in disease detection. This paper helps in detecting COVID-19 from the X-ray images provided to the model using Convolutional Neural Networks (CNN) and image augmentation techniques.
{"title":"COVID-19 Detection using Deep Learning","authors":"V. Gupta","doi":"10.46501/ijmtst061281","DOIUrl":"https://doi.org/10.46501/ijmtst061281","url":null,"abstract":"Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing\u0000data and creating patterns for use in decision making. Deep learning is a subset of machine learning in\u0000artificial intelligence (AI) that has networks capable of learning and recognizing patterns from data that is\u0000unstructured or unlabelled. It is also known as deep neural learning or deep neural network. Convolutional\u0000Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in\u0000areas such as image recognition and classification. ConvNets have been successful in identifying faces,\u0000objects and traffic signs apart from powering vision in robots and self-driving cars. ConvNets can also be\u0000used for Radio Imaging which helps in disease detection.\u0000This paper helps in detecting COVID-19 from the X-ray images provided to the model using Convolutional\u0000Neural Networks (CNN) and image augmentation techniques.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72846105","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}
In today’s globalized world thousands of peoples travels from one country to another. To interact with different peoples we generally speaks in English and communicate but this can’t be possible always as sometimes we need to talk in the local language so for these situations my system comes in picture as is provides quick, simple, and reliable way to convert a sentence into many language. Not only convert but also speaks so that user don’t need to speak in local language and can easily communicate.
{"title":"Text to Multiple Language Translator","authors":"Deepak kumar","doi":"10.46501/ijmtst061278","DOIUrl":"https://doi.org/10.46501/ijmtst061278","url":null,"abstract":"In today’s globalized world thousands of peoples travels from one country to another. To interact with\u0000different peoples we generally speaks in English and communicate but this can’t be possible always as\u0000sometimes we need to talk in the local language so for these situations my system comes in picture as is\u0000provides quick, simple, and reliable way to convert a sentence into many language. Not only convert but also\u0000speaks so that user don’t need to speak in local language and can easily communicate.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76232592","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}
In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.
{"title":"Detection of Novel Corona Virus Using Machine\u0000Learning and Image Recognition","authors":"Dhruv Garg and Saurabh Gautam","doi":"10.46501/ijmtst061274","DOIUrl":"https://doi.org/10.46501/ijmtst061274","url":null,"abstract":"In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne\u0000nature of this disease has made it highly contagious which has led to a great number of people being\u0000infected very fast. This requires a new method of testing that is faster and more precise. Machine\u0000Learning has allowed us to develop sophisticated self-learning models that can learn from data being\u0000fed and decide on entirely new options. In the past we have used different Machine Learning algorithm\u0000to make models on different biomedical dataset to detect various kind of acute or chronic diseases.\u0000Here we have developed a model that successfully detects severe cases of Novel corona virus affected\u0000person with great precision.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"194 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87649209","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}
The online food ordering system provides conveniencefor the customers. It overcomes the disadvantages of the traditional queuing system. This system increases the takeaway of foods than visitors. Therefore, this system enhances the speed and standardization of taking the order from the customer. It provides a better communication platform. the user’s details are noted electronically. The online food ordering system set up menu online and the customers easily places the order with a simple mouse click. also with a food menu online you can easily track the orders, maintain customer's database and improve your food delivery service. This system allows the user to select the desired food items from the displayed menu. The user orders the food items. The payment can be made online or pay-ondelivery system. The user’s details are maintainedconfidential because it maintains a separate account for each user. An id and password is provided for each user. Therefore it provides a more secured ordering.
{"title":"Food Ordering Web Application for the Fitness\u0000freaks","authors":"Tarun Garg Ms. Meenu Garg and Dr. Bhoomi Gupta","doi":"10.46501/ijmtst061286","DOIUrl":"https://doi.org/10.46501/ijmtst061286","url":null,"abstract":"The online food ordering system provides conveniencefor the customers. It overcomes the disadvantages of\u0000the traditional queuing system. This system increases the takeaway of foods than visitors. Therefore, this\u0000system enhances the speed and standardization of taking the order from the customer. It provides a better\u0000communication platform. the user’s details are noted electronically.\u0000The online food ordering system set up menu online and the customers easily places the order with a\u0000simple mouse click. also with a food menu online you can easily track the orders, maintain customer's\u0000database and improve your food delivery service. This system allows the user to select the desired food items\u0000from the displayed menu. The user orders the food items. The payment can be made online or pay-ondelivery system. The user’s details are maintainedconfidential because it maintains a separate account for\u0000each user. An id and password is provided for each user. Therefore it provides a more secured ordering.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"36 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82718826","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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