Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847880
S. Selvi, Nikhil Agarwal, Paarth Barkur, Yash Mishra, Abhsihek Kumar
The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection.
{"title":"A Novel Deep Learning Algorithm for Covid Detection and Classification","authors":"S. Selvi, Nikhil Agarwal, Paarth Barkur, Yash Mishra, Abhsihek Kumar","doi":"10.1109/CONIT55038.2022.9847880","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847880","url":null,"abstract":"The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147999","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848104
S. Anand, Neha Manjunath
With the help of IoT (Internet of Things) devices, the world is becoming more connected. To accomplish this, a vast amount of data must be safely stored and accessed, yet IoT devices have limited memory and computing time. As a result, a huge storage room with secure space for storage is required. SDN (Software-Defined Networking) is a revolutionary network technology that incorporates a new paradigm of unsecured apps and Internet-of- Things (IoT) services. Enemies hoping to upset the activity of an IoT framework can use the malevolent bundle change assault (MP A), a basic however powerful assault that has recently been found in loT in light of remote sensor organizations. We offer a strategy for securing and dependably conveying information within the sight of dynamic aggressors to oppose MP As that takes advantage of SDN's programmability and flexibility. Our method ensures that loT devices are aware of any changes. The suggested solution's effectiveness and performance were assessed in a series of extensive tests using a prototype implementation. The findings show that even if malicious forwarding devices only modify a small percentage of the data, they may be reliably and promptly identified and circumvented. We examined the exhibition of our proposed framework utilizing OMNeT++ to recreate our whole situation and affirmed that the framework is secure and dependable in loT applications.
{"title":"A Reliable Software Defifined Networking based Framework for IoT Devices","authors":"S. Anand, Neha Manjunath","doi":"10.1109/CONIT55038.2022.9848104","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848104","url":null,"abstract":"With the help of IoT (Internet of Things) devices, the world is becoming more connected. To accomplish this, a vast amount of data must be safely stored and accessed, yet IoT devices have limited memory and computing time. As a result, a huge storage room with secure space for storage is required. SDN (Software-Defined Networking) is a revolutionary network technology that incorporates a new paradigm of unsecured apps and Internet-of- Things (IoT) services. Enemies hoping to upset the activity of an IoT framework can use the malevolent bundle change assault (MP A), a basic however powerful assault that has recently been found in loT in light of remote sensor organizations. We offer a strategy for securing and dependably conveying information within the sight of dynamic aggressors to oppose MP As that takes advantage of SDN's programmability and flexibility. Our method ensures that loT devices are aware of any changes. The suggested solution's effectiveness and performance were assessed in a series of extensive tests using a prototype implementation. The findings show that even if malicious forwarding devices only modify a small percentage of the data, they may be reliably and promptly identified and circumvented. We examined the exhibition of our proposed framework utilizing OMNeT++ to recreate our whole situation and affirmed that the framework is secure and dependable in loT applications.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"17 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965318","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}
With recent technological and scientific advancements in the power systems, there has been a tandem need for load forecasting. This paper mainly discusses short-term load forecasting, which refers to the prediction of the system load demand over an interval ranging between minutes ahead to one week ahead. With advent of Machine Learning, the process of demand prediction has become easier and cost effective. The challenge of predicting the future demand can be characterized as a regression problem, hence the method of Support Vector Regression is used, as it has proved to be a robust method in the recent research. Different Neural Networks are also being used in several domains; hence Deep Neural Network has also been used to test the accuracy, The paper discusses the results obtained by two different methods. The comparison between the outcomes of the different algorithms has been discussed, in order to get a thorough understanding. The methods are explained vastly. The paper also discusses the factors affecting load forecasting directly.
{"title":"Short Term Load Forecasting using Machine Learning Techniques","authors":"Sonakshi Dua, Shaurya Gautam, Mahi Garg, Rajendra Mahla, Mrityunjay Chaudhary, S. Vadhera","doi":"10.1109/CONIT55038.2022.9848160","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848160","url":null,"abstract":"With recent technological and scientific advancements in the power systems, there has been a tandem need for load forecasting. This paper mainly discusses short-term load forecasting, which refers to the prediction of the system load demand over an interval ranging between minutes ahead to one week ahead. With advent of Machine Learning, the process of demand prediction has become easier and cost effective. The challenge of predicting the future demand can be characterized as a regression problem, hence the method of Support Vector Regression is used, as it has proved to be a robust method in the recent research. Different Neural Networks are also being used in several domains; hence Deep Neural Network has also been used to test the accuracy, The paper discusses the results obtained by two different methods. The comparison between the outcomes of the different algorithms has been discussed, in order to get a thorough understanding. The methods are explained vastly. The paper also discusses the factors affecting load forecasting directly.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127107408","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848385
Darshana N. Tambe, L. Ragha
Software is the heart of over 99% of all modern-day devices which include smartphones, personal computers, internet of things (IoT) networks, etc. This software is built by a team of engineers which divide the final product into multiple smaller components and these components are integrated together to build the final software, due to which inherent interfacing vulnerabilities & bugs are injected into it. Multiple bugs are also injected into the system due to inexperience or mistakes made by software engineers & programmers. To identify these mistakes, a wide variety of bug prediction & tracing models are proposed by researchers, which assist programmers to predict & track these bugs. But these models have large variations in terms of accuracy, precision, recall, delay, computational complexity, cost of deployment and other performance metrics, due to which it is ambiguous for software designers to identify best bug tracing method(s) for their application deployments. To reduce this ambiguity, a discussion about design of different bug tracing & prediction models and their statistical comparison is done in this paper. This comparison includes evaluation of accuracy, precision, recall, computational complexity and scalability under different scenarios. Based on this comparison, in this paper experiments were performed on five publically available datasets from NASA MDP repository using different algorithms i.e. DRF, LSVM, LR, RF, and kNN. From the results it was observed that kNN algorithm outperforms average 98.8% accuracy on these five datasets and hence kNN were considered to be the most significant with its selected features. In the future, this performance can be improved via use of CNN & LSTM based models, which can utilize the base kNN layer, and estimate highly dense features for efficient classification performance.
{"title":"Analysis of Software Bug Prediction and Tracing Models from a Statistical Perspective Using Machine Learning","authors":"Darshana N. Tambe, L. Ragha","doi":"10.1109/CONIT55038.2022.9848385","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848385","url":null,"abstract":"Software is the heart of over 99% of all modern-day devices which include smartphones, personal computers, internet of things (IoT) networks, etc. This software is built by a team of engineers which divide the final product into multiple smaller components and these components are integrated together to build the final software, due to which inherent interfacing vulnerabilities & bugs are injected into it. Multiple bugs are also injected into the system due to inexperience or mistakes made by software engineers & programmers. To identify these mistakes, a wide variety of bug prediction & tracing models are proposed by researchers, which assist programmers to predict & track these bugs. But these models have large variations in terms of accuracy, precision, recall, delay, computational complexity, cost of deployment and other performance metrics, due to which it is ambiguous for software designers to identify best bug tracing method(s) for their application deployments. To reduce this ambiguity, a discussion about design of different bug tracing & prediction models and their statistical comparison is done in this paper. This comparison includes evaluation of accuracy, precision, recall, computational complexity and scalability under different scenarios. Based on this comparison, in this paper experiments were performed on five publically available datasets from NASA MDP repository using different algorithms i.e. DRF, LSVM, LR, RF, and kNN. From the results it was observed that kNN algorithm outperforms average 98.8% accuracy on these five datasets and hence kNN were considered to be the most significant with its selected features. In the future, this performance can be improved via use of CNN & LSTM based models, which can utilize the base kNN layer, and estimate highly dense features for efficient classification performance.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079192","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847725
S. Bhatlawande, S. Shilaskar, Mohit Agrawal, Varad Ashtekar, Mahesh Badade, Shwetambari Belote, Jyoti Madake
Numerous studies in the field of object detection have been conducted over the past few decades. Several effective methods have been developed. Among various object detection algorithms, Faster RCNN offers excellent results in both detection speed and accuracy. It is a combination of Fast RCNN and RPN layers. This paper conducts a comparative study of object detection using Faster RCNN. The study shows that use of smaller convolutional network called Region Proposal Network improves performance of the system. It shows that object detection using Faster RCNN can give high accuracy and faster performance as compared to other methods and algorithms. It takes only 0.2 seconds to predict a single image. Also, it gives 70% Mean Accuracy Precision (mAP) on the PASCAL VOC 2007 and PASCAL VOC 2012 datasets.
{"title":"Study of Object Detection with Faster RCNN","authors":"S. Bhatlawande, S. Shilaskar, Mohit Agrawal, Varad Ashtekar, Mahesh Badade, Shwetambari Belote, Jyoti Madake","doi":"10.1109/CONIT55038.2022.9847725","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847725","url":null,"abstract":"Numerous studies in the field of object detection have been conducted over the past few decades. Several effective methods have been developed. Among various object detection algorithms, Faster RCNN offers excellent results in both detection speed and accuracy. It is a combination of Fast RCNN and RPN layers. This paper conducts a comparative study of object detection using Faster RCNN. The study shows that use of smaller convolutional network called Region Proposal Network improves performance of the system. It shows that object detection using Faster RCNN can give high accuracy and faster performance as compared to other methods and algorithms. It takes only 0.2 seconds to predict a single image. Also, it gives 70% Mean Accuracy Precision (mAP) on the PASCAL VOC 2007 and PASCAL VOC 2012 datasets.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114348066","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847738
P. B. Niranjane, S. Amdani
In directed and weighted graph, with n nodes and m edges, the K-shortest paths problem involve finding a set of k shortest paths between a defined source and destination pair where the first path is shortest, and the remaining k-1 paths are in increasing lengths. In K-shortest path problem there are two classes, k shortest simple path problem and k shortest non-simple path problem. The first algorithm to solve K shortest simple path problems is Yen's algorithm based on deviation path concept. Later many variants of Yen's algorithm are proposed with improved computational performance. In this paper some of the variants of Yen's algorithm for finding top k simple shortest path are studied and compared.
{"title":"Comparison of Variants of Yen's Algorithm for Finding K-Simple Shortest Paths","authors":"P. B. Niranjane, S. Amdani","doi":"10.1109/CONIT55038.2022.9847738","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847738","url":null,"abstract":"In directed and weighted graph, with n nodes and m edges, the K-shortest paths problem involve finding a set of k shortest paths between a defined source and destination pair where the first path is shortest, and the remaining k-1 paths are in increasing lengths. In K-shortest path problem there are two classes, k shortest simple path problem and k shortest non-simple path problem. The first algorithm to solve K shortest simple path problems is Yen's algorithm based on deviation path concept. Later many variants of Yen's algorithm are proposed with improved computational performance. In this paper some of the variants of Yen's algorithm for finding top k simple shortest path are studied and compared.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165066","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848177
Suman Pokhrel, Laxmi Kanta Dahal, N. Gupta, Rijesh Shrestha, Anshul Srivastava, Akash Bhasney
A brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull, which encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Magnetic resonance imaging (MRI) is a non-invasive method for producing three-dimensional (3D) tomographic images of the human body. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain. Clinically, radiologists qualitatively analyze films produced by MRI scanners. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. We implemented various state of the art Neural Networks like MobileN etV2, MobileNetV3 small, MobileNetV3 large, VGG16, VGG19 and our Custom CNN model. Among these models CNN was able to get the Highest amount of accuracy. Our proposed method consists of a Convolutional Neural Network (CNN) (which is implemented using Keras and Tensor flow) that is integrated to a full featured cross-platform desktop application(which is implemented using PyQt5 and MariaDB) that can be easily used in hospitals as well as local clinics. The main aim of this project is to distinguish between normal and abnormal pixels, and classify a tumor affected brain using real-world datasets.
{"title":"Brain Tumor Detection Application Based On Convolutional Neural Network","authors":"Suman Pokhrel, Laxmi Kanta Dahal, N. Gupta, Rijesh Shrestha, Anshul Srivastava, Akash Bhasney","doi":"10.1109/CONIT55038.2022.9848177","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848177","url":null,"abstract":"A brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull, which encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Magnetic resonance imaging (MRI) is a non-invasive method for producing three-dimensional (3D) tomographic images of the human body. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain. Clinically, radiologists qualitatively analyze films produced by MRI scanners. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. We implemented various state of the art Neural Networks like MobileN etV2, MobileNetV3 small, MobileNetV3 large, VGG16, VGG19 and our Custom CNN model. Among these models CNN was able to get the Highest amount of accuracy. Our proposed method consists of a Convolutional Neural Network (CNN) (which is implemented using Keras and Tensor flow) that is integrated to a full featured cross-platform desktop application(which is implemented using PyQt5 and MariaDB) that can be easily used in hospitals as well as local clinics. The main aim of this project is to distinguish between normal and abnormal pixels, and classify a tumor affected brain using real-world datasets.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145394","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847947
G. Snehalatha, J. Selvakumar, Esther Rani Thuraka
Data converters implemented using CMOS technology play crucial role in electronics which is ever increasing. ADCs find their applications in signal processing and communication applications. Because of small area, low power and low/medium input signals Successive Approximation ADCs are preferred in most of the applications. Machine Learning algorithms are used to fine-tune the Successive Stochastic Approximation Analog to Digital Converter (SSA ADC), which is used in Biomedical applications. Compared to SAR ADC, SSA ADC offers low power and errors caused by DAC can be corrected to maximum possible extent using stochastic process. Various ADCs, SAR ADC and SSA ADC architectures for Biomedical applications have been compared with respect to parameters, methods and tools.
{"title":"Comparative Study and Review on Successive Approximation/Stochastic Approximation Analog to Digital Converters for Biomedical Applications","authors":"G. Snehalatha, J. Selvakumar, Esther Rani Thuraka","doi":"10.1109/CONIT55038.2022.9847947","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847947","url":null,"abstract":"Data converters implemented using CMOS technology play crucial role in electronics which is ever increasing. ADCs find their applications in signal processing and communication applications. Because of small area, low power and low/medium input signals Successive Approximation ADCs are preferred in most of the applications. Machine Learning algorithms are used to fine-tune the Successive Stochastic Approximation Analog to Digital Converter (SSA ADC), which is used in Biomedical applications. Compared to SAR ADC, SSA ADC offers low power and errors caused by DAC can be corrected to maximum possible extent using stochastic process. Various ADCs, SAR ADC and SSA ADC architectures for Biomedical applications have been compared with respect to parameters, methods and tools.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127363021","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 Time-series data is a sequence of data points at regular time intervals indexed in time order. It is also known as time-stamped data. These sequential data characteristics might change during the process. Change points in time series data are substantial statistical property changes in the data. Many applications rely on the detection of these changes for appropriate modeling and prediction. Many vital activities can be monitored with the help of Change-Point Detection (CPD) algorithms, and appropriate actions can be made as a response. There are a variety of methods for detecting CPD in time series, which are divided into supervised and unsupervised categories. This comparative study compares all of the algorithms that have been published in the literature. Many novel algorithms based on the results of deep learning are also evaluated. Finally, we give the community some challenges to ponder.
{"title":"A Comparative Study on Change-Point Detection Methods in Time Series Data","authors":"Aditya Pushkar, Muktesh Gupta, Rajesh Wadhvani, Manasi Gyanchandani","doi":"10.1109/CONIT55038.2022.9848051","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848051","url":null,"abstract":"The Time-series data is a sequence of data points at regular time intervals indexed in time order. It is also known as time-stamped data. These sequential data characteristics might change during the process. Change points in time series data are substantial statistical property changes in the data. Many applications rely on the detection of these changes for appropriate modeling and prediction. Many vital activities can be monitored with the help of Change-Point Detection (CPD) algorithms, and appropriate actions can be made as a response. There are a variety of methods for detecting CPD in time series, which are divided into supervised and unsupervised categories. This comparative study compares all of the algorithms that have been published in the literature. Many novel algorithms based on the results of deep learning are also evaluated. Finally, we give the community some challenges to ponder.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127310709","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 sum of all harmonic components of a waveform relative to the fundamental component of waveform is termed as total harmonic distortion (THD). In this paper we have compared THD of photovoltaic (PV) systems connected with a grid for four different MPPT algorithms which includes artificial neural network (ANN), incremental conductance (INC), perturb and observe (P&O), and fuzzy logic control (FLC). The simulation results clearly present the difference in THD among all four MPPT algorithms. We have also designed a three phase LCL filters to filter out the harmonics in the output signal of the system. These are the specially designed filters to eliminate the harmonics with improved performance as well as it is cost effective and are smaller in size because of lesser values of inductor and capacitors in it.
{"title":"MPPT Algorithms with LCL Filter for Grid Connected PV System","authors":"Shivam Dutt Jha, Siddharth, Siddharth Chowdhary, Kuldeep Singh","doi":"10.1109/CONIT55038.2022.9847960","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847960","url":null,"abstract":"The sum of all harmonic components of a waveform relative to the fundamental component of waveform is termed as total harmonic distortion (THD). In this paper we have compared THD of photovoltaic (PV) systems connected with a grid for four different MPPT algorithms which includes artificial neural network (ANN), incremental conductance (INC), perturb and observe (P&O), and fuzzy logic control (FLC). The simulation results clearly present the difference in THD among all four MPPT algorithms. We have also designed a three phase LCL filters to filter out the harmonics in the output signal of the system. These are the specially designed filters to eliminate the harmonics with improved performance as well as it is cost effective and are smaller in size because of lesser values of inductor and capacitors in it.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133053937","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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