Pub Date : 2023-02-15DOI: 10.47164/ijngc.v14i1.1046
A. Karandikar, Yogesh Thakare, O. Sah, R. K. Sah, S. Nafde, S. Kumar
The appearance of web-based media has implied genuine and anecdotal stories introduced in such a comparative manner that it can now and then be hard to differentiate the two. Similarly, manipulation of real photos, audios or videos with the help of Artificial Intelligence techniques is done such that it is difficult to distinguish between the real and fake thus called Deepfake. It can happen to big celebrities, politicians, and to layman as well for some malicious purpose. Consequently, this procedure can end up being very threat to human culture subsequently expected to identify it appropriately. This paper intends to tackle this issue by proposing a model that uses Residual Neural Network (ResNet50) and Long Short-term Memory (LSTM) to detect video as fake or real. This approach tries to find flaws in the fake data left behind while its creation using neural based techniques like generative adversarial networks (GAN).
{"title":"Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory","authors":"A. Karandikar, Yogesh Thakare, O. Sah, R. K. Sah, S. Nafde, S. Kumar","doi":"10.47164/ijngc.v14i1.1046","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1046","url":null,"abstract":"The appearance of web-based media has implied genuine and anecdotal stories introduced in such a comparative manner that it can now and then be hard to differentiate the two. Similarly, manipulation of real photos, audios or videos with the help of Artificial Intelligence techniques is done such that it is difficult to distinguish between the real and fake thus called Deepfake. It can happen to big celebrities, politicians, and to layman as well for some malicious purpose. Consequently, this procedure can end up being very threat to human culture subsequently expected to identify it appropriately. This paper intends to tackle this issue by proposing a model that uses Residual Neural Network (ResNet50) and Long Short-term Memory (LSTM) to detect video as fake or real. This approach tries to find flaws in the fake data left behind while its creation using neural based techniques like generative adversarial networks (GAN).","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"41 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86619032","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}
Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.
{"title":"Facial Emotion Recognition through Neural Networks","authors":"Abhijeet R. Raipurkar, Pravesh Dholwani, Atharva Pandhare, Rishabh Mittal, Aniket Tawani","doi":"10.47164/ijngc.v14i1.1045","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1045","url":null,"abstract":"Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"23 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73655756","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1042
Abhijeet R. Raipurkar, Pratik K. Agrawal, Radha Malichkar, Snehal Mopkar, Chetan Pardhi, Saiyyed Khhizr Aalam
Customers always have to compare product prices and offers across several websites when they go to purchase a specific item. The solution seeks to address the fore mentioned issue. The comparison that Google Search now offers is focused more on text search than anything else. The pricing and any available coupons or discounts on that website are not listed. This paper proposes a solution- a price-deals-lister, a microservice based website which scrapes the various e-commerce websites and get the deals available, stores them in database and later the request processor unit will take the data from the database according to the user’s request and show it to the user. Customers and retailers alike will benefit from this initiative because it allows them to quickly and easily access all the data with just one click. Anyone can access the website and compare the offers found on other e-commerce websites. In view of the Market analysis, Shopkeepers can use a website to verify the current market price of a product, especially retailers that must offer their goods with tight margins. After that, they can raisethe product’s price in their store and sell it for a fair amount to make a profit. Shop owners can learn about various lucrative offers that are currently being offered for a specific product and utilize that knowledge to better serve their customers. Producers can research the products that consumers are most interested in and concentrate on making those products. Additionally, by providing the finest deals to the clients, they might consider various strategies for increasing the profitability of the products.
{"title":"Dynamic Microservice based scalable approach to list product deals","authors":"Abhijeet R. Raipurkar, Pratik K. Agrawal, Radha Malichkar, Snehal Mopkar, Chetan Pardhi, Saiyyed Khhizr Aalam","doi":"10.47164/ijngc.v14i1.1042","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1042","url":null,"abstract":"Customers always have to compare product prices and offers across several websites when they go to purchase a specific item. The solution seeks to address the fore mentioned issue. The comparison that Google Search now offers is focused more on text search than anything else. The pricing and any available coupons or discounts on that website are not listed. This paper proposes a solution- a price-deals-lister, a microservice based website which scrapes the various e-commerce websites and get the deals available, stores them in database and later the request processor unit will take the data from the database according to the user’s request and show it to the user. Customers and retailers alike will benefit from this initiative because it allows them to quickly and easily access all the data with just one click. Anyone can access the website and compare the offers found on other e-commerce websites. In view of the Market analysis, Shopkeepers can use a website to verify the current market price of a product, especially retailers that must offer their goods with tight margins. After that, they can raisethe product’s price in their store and sell it for a fair amount to make a profit. Shop owners can learn about various lucrative offers that are currently being offered for a specific product and utilize that knowledge to better serve their customers. Producers can research the products that consumers are most interested in and concentrate on making those products. Additionally, by providing the finest deals to the clients, they might consider various strategies for increasing the profitability of the products.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"114 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88137059","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1011
Amol Bhopale, Sanskar Zanwar, Aarya Balpande, Jaweria Kazi
Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the healthcare data for effective analysis is complicated and challenging most of the time. This research work examines clustering approaches on the healthcare datasets with the optimum K-clusters, in order to perform the analysis of the data. In this work, the K-means clustering method is examined and the silhouette score is computed to estimate the optimal K-value and the quality of the cluster.
{"title":"Optimised Cluster-based Approach for Healthcare Data Analytics","authors":"Amol Bhopale, Sanskar Zanwar, Aarya Balpande, Jaweria Kazi","doi":"10.47164/ijngc.v14i1.1011","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1011","url":null,"abstract":"Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the healthcare data for effective analysis is complicated and challenging most of the time. This research work examines clustering approaches on the healthcare datasets with the optimum K-clusters, in order to perform the analysis of the data. In this work, the K-means clustering method is examined and the silhouette score is computed to estimate the optimal K-value and the quality of the cluster.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"14 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87262439","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1030
Kanak Kalyani, Abhiyant Gwalani, Varun Kalbhore, Shreya Rai
Plagiarism is stealing someone’s ideas and presenting them as yours. University students’ use of plagiarism is a serious problem that compromises their preparation and the university’s attempts to produce qualified graduates. Universities attempt to combat this issue with strong ethics regulations, but in order to put these policies into effect; they need the appropriate plagiarism detection tools at reasonable prices. In this article, we introduce PlagCheck, a high-volume, quick, and affordable plagiarism detection system created using the word embeddingsmodel and intended for use on text-based student assignments (essays, theses, homework). We go over the benefits of this approach in terms of cost, accuracy, and speed. This software will help educators and reduce their workload by helping them check plagiarism for each student’s assignment quickly and efficiently.
{"title":"PlagCheck: An efficient way to identify Plagiarism using BERT","authors":"Kanak Kalyani, Abhiyant Gwalani, Varun Kalbhore, Shreya Rai","doi":"10.47164/ijngc.v14i1.1030","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1030","url":null,"abstract":"Plagiarism is stealing someone’s ideas and presenting them as yours. University students’ use of plagiarism is a serious problem that compromises their preparation and the university’s attempts to produce qualified graduates. Universities attempt to combat this issue with strong ethics regulations, but in order to put these policies into effect; they need the appropriate plagiarism detection tools at reasonable prices. In this article, we introduce PlagCheck, a high-volume, quick, and affordable plagiarism detection system created using the word embeddingsmodel and intended for use on text-based student assignments (essays, theses, homework). We go over the benefits of this approach in terms of cost, accuracy, and speed. This software will help educators and reduce their workload by helping them check plagiarism for each student’s assignment quickly and efficiently. ","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"9 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79460305","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1037
P. Sonsare, Rushabh Jain, Rutuj Runwal, Kunal Dave, Ashutosh Banode
Scene text detection has been one of the most important topics for research in computer vision. With constant development and rise in deep learning, computer vision technology has undergone an impactful transformation. In the era before deep learning, there existed algorithms and technologies for scene text detection, but the performance was mediocre. In recent years, deep learning technology has remarkably transformed scene text detection. Researchers have witnessed notable advancements in the approach, methodology, and overall performance of the newly discovered techniques. In this paper, the predominant focus is on summarizing and analysing the significant progress in scene text detection through deep learning. This paper covers an introduction to scene text detection, steps to perform scene text recognition and detection, technique before deep-learning, recent techniques and their insights, some results, and an overview by comparing the algorithms. We will also emphasize the criteria that make a search algorithm a good choice for performing scene text detection and recognition, the notable differences incorporated by deep learning, and analyse the drawbacks of the techniques used before deep learning. This paper would be helpful to understand the key differences that have changed this field and also some remaining challenges.
{"title":"Novel Study on Localization in Scene Text Detection","authors":"P. Sonsare, Rushabh Jain, Rutuj Runwal, Kunal Dave, Ashutosh Banode","doi":"10.47164/ijngc.v14i1.1037","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1037","url":null,"abstract":"Scene text detection has been one of the most important topics for research in computer vision. With constant development and rise in deep learning, computer vision technology has undergone an impactful transformation. In the era before deep learning, there existed algorithms and technologies for scene text detection, but the performance was mediocre. In recent years, deep learning technology has remarkably transformed scene text detection. Researchers have witnessed notable advancements in the approach, methodology, and overall performance of the newly discovered techniques. In this paper, the predominant focus is on summarizing and analysing the significant progress in scene text detection through deep learning. This paper covers an introduction to scene text detection, steps to perform scene text recognition and detection, technique before deep-learning, recent techniques and their insights, some results, and an overview by comparing the algorithms. We will also emphasize the criteria that make a search algorithm a good choice for performing scene text detection and recognition, the notable differences incorporated by deep learning, and analyse the drawbacks of the techniques used before deep learning. This paper would be helpful to understand the key differences that have changed this field and also some remaining challenges.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"27 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72928007","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}
Identification of the denomination of the currency note to pay physically without UPI is the first step of paying to the seller by the consumer. In this project, we have proposed an approach to detect denominations of Indian currency using Convolutional Neural Networks. Computer Vision and object detection is an area of great interest for research in today’s world. It has several applications like detection of defects in machinery, intruder detection, computer vision for code and character recognition among many others. Through the work we have done, we explored something that could be of great help to people in day-to-day life. In this project we have tried to investigate the approaches to detect currency denominations using Convolutional Neural Networks. The objective is to build a model that would be able to detect Indian currency denominations efficiently. Typically the model will be useful for people with vision impairment. The experimental results show that the use of Convolutional Neural Networks is a good way and the model can further be improved if it is trained in such a way that it could also identify the regions of interest.
{"title":"Automatic Detection of Indian Currency Denominations using Deep Learning","authors":"Yash Patel, Ramakant Chhangani, Sarang Deshpande, Ramchand Hablani, Sweta Jain","doi":"10.47164/ijngc.v14i1.1028","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1028","url":null,"abstract":"Identification of the denomination of the currency note to pay physically without UPI is the first step of paying to the seller by the consumer. In this project, we have proposed an approach to detect denominations of Indian currency using Convolutional Neural Networks. Computer Vision and object detection is an area of great interest for research in today’s world. It has several applications like detection of defects in machinery, intruder detection, computer vision for code and character recognition among many others. Through the work we have done, we explored something that could be of great help to people in day-to-day life. In this project we have tried to investigate the approaches to detect currency denominations using Convolutional Neural Networks. The objective is to build a model that would be able to detect Indian currency denominations efficiently. Typically the model will be useful for people with vision impairment. The experimental results show that the use of Convolutional Neural Networks is a good way and the model can further be improved if it is trained in such a way that it could also identify the regions of interest.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"14 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78547052","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1041
P. Pardhi, Navya Verma, Nikunj Loya, Kartik Agrawal
A tumor is a mass of tissue generated by the aggregation of aberrant cells that continue to grow, and the brain is the most essential organ in the human body, responsible for controlling and regulating all critical life activities for the body. A brain tumor is either formed in the brain or has migrated. Yet, no reason has been found for developing brain tumors. Though brain tumors are uncommon (approximately 1.8 percent of all reported cancers), the death risk of malignant brain tumors is particularly high due to the tumor’s location in the body’s most essential organ. To reduce the mortality rate, it is critical to accurately detect brain tumors at an early stage. As a result, we’ve proposed a computer-assisted radiology method for assessing brain tumors from MRI scans forbrain tumor diagnostic management. In this research paper, we developed a model that uses the Watershed technique to segment images, extract features, and then use deep learning to detect cancers with high accuracy.
{"title":"A Novel Approach to Detect Brain Tumor Using CNN model of Deep Learning","authors":"P. Pardhi, Navya Verma, Nikunj Loya, Kartik Agrawal","doi":"10.47164/ijngc.v14i1.1041","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1041","url":null,"abstract":"A tumor is a mass of tissue generated by the aggregation of aberrant cells that continue to grow, and the brain is the most essential organ in the human body, responsible for controlling and regulating all critical life activities for the body. A brain tumor is either formed in the brain or has migrated. Yet, no reason has been found for developing brain tumors. Though brain tumors are uncommon (approximately 1.8 percent of all reported cancers), the death risk of malignant brain tumors is particularly high due to the tumor’s location in the body’s most essential organ. To reduce the mortality rate, it is critical to accurately detect brain tumors at an early stage. As a result, we’ve proposed a computer-assisted radiology method for assessing brain tumors from MRI scans forbrain tumor diagnostic management. In this research paper, we developed a model that uses the Watershed technique to segment images, extract features, and then use deep learning to detect cancers with high accuracy. ","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"11 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75816251","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 : 2023-02-15DOI: 10.47164/ijngc.v14i1.1003
Rutuj Runwal, Shrawan J Purve, Mohit Chandak
Development of a handwriting classifier using deep learning approach that can classify handwritten numbers and digits on the web. It is a deep-learning based system that uses modern algorithms and focuses on creating a portable web application that aims to classify handwritten numbers powered by the MNIST dataset
{"title":"Classification of Handwritten Digits on the web using Deep Learning","authors":"Rutuj Runwal, Shrawan J Purve, Mohit Chandak","doi":"10.47164/ijngc.v14i1.1003","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1003","url":null,"abstract":"Development of a handwriting classifier using deep learning approach that can classify handwritten numbers and digits on the web. It is a deep-learning based system that uses modern algorithms and focuses on creating a portable web application that aims to classify handwritten numbers powered by the MNIST dataset","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"11 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73701086","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 main idea of the research paper is to detect pneumonia from the patient’s chest x- rays. Pneumonia is the infection that causes inflammation of the air sacs in one or both the lungs. The air sacs are filled with purulent material (pus) causing breath shortness, cough, fever, chills.A variety of bacteria, viruses, and fungi can cause pneumonia. In this paper, we used machine learning algorithms to process x-ray images to determine whether or not the patient has pneumonia. This Experiment focusses on the use of deep learning algorithms with VGG16 pre-processing, keras and adams in order to build a model with high accuracy.
{"title":"Detection of Pneumonia Using Deep Learning","authors":"Nishant Borkar, Atharva Zararia, Riddhi Gangbhoj, Prashant Kumar, Vaishnavi Bhaiyya","doi":"10.47164/ijngc.v14i1.1023","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1023","url":null,"abstract":"The main idea of the research paper is to detect pneumonia from the patient’s chest x- rays. Pneumonia is the infection that causes inflammation of the air sacs in one or both the lungs. The air sacs are filled with purulent material (pus) causing breath shortness, cough, fever, chills.A variety of bacteria, viruses, and fungi can cause pneumonia. In this paper, we used machine learning algorithms to process x-ray images to determine whether or not the patient has pneumonia. This Experiment focusses on the use of deep learning algorithms with VGG16 pre-processing, keras and adams in order to build a model with high accuracy.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"33 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74848547","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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