Pub Date : 1900-01-01DOI: 10.52458/978-93-91842-08-6-19
D. Singla, D. Kumar, Sakshi Dhingra
The research paper is review the quality of image at the time of CBIR operation. Research is opting to maintain the quality of image even after performing compression. Research work has highlighted the impact of CBIR operation and compression operation over quality of image. The major issue in image processing research is impact of image processing over image quality. Moreover there is need to discuss loss less compression mechanism to retain quality of image. Research paper is considering different methodologies used by existing research paper along with their working mechanism, advantages and disadvantage. Research is considering image processing as process of extracting information from pictures and combining it for use in a variety of applications. Image processing programmes are useful in a variety of situations. A few examples include medical imaging, industrial applications. The remote sensing, spaces as well as military applications are also considered. The application of computer vision mechanism to the graphic content retrieval problem has been considered a challenge in finding digital pictures in huge databases. Existing researches have worked to improve the overall performance during image processing by retaining quality of the image. Moreover, there is increase in accurate decisions making. Research is opting to retain the quality of image during CBIR operations.
{"title":"A Review on Quality of Image during CBIR Operations and Compression","authors":"D. Singla, D. Kumar, Sakshi Dhingra","doi":"10.52458/978-93-91842-08-6-19","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-19","url":null,"abstract":"The research paper is review the quality of image at the time of CBIR operation. Research is opting to maintain the quality of image even after performing compression. Research work has highlighted the impact of CBIR operation and compression operation over quality of image. The major issue in image processing research is impact of image processing over image quality. Moreover there is need to discuss loss less compression mechanism to retain quality of image. Research paper is considering different methodologies used by existing research paper along with their working mechanism, advantages and disadvantage. Research is considering image processing as process of extracting information from pictures and combining it for use in a variety of applications. Image processing programmes are useful in a variety of situations. A few examples include medical imaging, industrial applications. The remote sensing, spaces as well as military applications are also considered. The application of computer vision mechanism to the graphic content retrieval problem has been considered a challenge in finding digital pictures in huge databases. Existing researches have worked to improve the overall performance during image processing by retaining quality of the image. Moreover, there is increase in accurate decisions making. Research is opting to retain the quality of image during CBIR operations.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140664","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-18
Neerajkumar S Sathawane, U. M. Gokhale, D. Padole
In today’s fast-moving world, where people are busy at work and less aware of their health. Our cities become smart cities, and villages connect with them. However, health facilities in villages and remote areas of the country are still not significantly developed. Treatment costs for low-income people are out of budget, so we are trying to create a system where the system can be used remotely. Here we diagnose the arrhythmia’s while capturing the ECG and sending both components to the end-user for diagnosis.
{"title":"Performance Evaluation for Detection of Cardiovascular Disease using Different Methods","authors":"Neerajkumar S Sathawane, U. M. Gokhale, D. Padole","doi":"10.52458/978-93-91842-08-6-18","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-18","url":null,"abstract":"In today’s fast-moving world, where people are busy at work and less aware of their health. Our cities become smart cities, and villages connect with them. However, health facilities in villages and remote areas of the country are still not significantly developed. Treatment costs for low-income people are out of budget, so we are trying to create a system where the system can be used remotely. Here we diagnose the arrhythmia’s while capturing the ECG and sending both components to the end-user for diagnosis.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122128910","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-2
Bemarisika Parfait, Totohasina André
One challenge problem in association rules mining is the huge size of the extracted rule set many of which are uninteresting and redundant. In this paper, we propose an efficient algorithm CONCISE for generating all non-redundant positive and negative association rules. We first introduce an algorithm CMG (Closed, Maximal and Generators) for mining all frequent closed, maximal and their generators itemsets from large transaction databases. We then define four new bases representing non-redundant association rules from these frequent itemsets. We prove that these bases significantly reduce the number of extracted rules. We show the efficiency of our algorithm by computational experiments compared with existing algorithms.
关联规则挖掘中的一个挑战问题是所提取的规则集规模巨大，其中许多是无趣的和冗余的。本文提出了一种高效的生成所有非冗余正关联规则和负关联规则的简洁算法。首先介绍了一种CMG (Closed, maximum and Generators)算法，用于从大型事务数据库中挖掘所有频繁的Closed, maximum及其生成器项集。然后，我们定义了四个新的基，表示来自这些频繁项集的非冗余关联规则。我们证明了这些基显著地减少了提取规则的数量。通过计算实验与现有算法进行了比较，证明了算法的有效性。
{"title":"CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules","authors":"Bemarisika Parfait, Totohasina André","doi":"10.52458/978-93-91842-08-6-2","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-2","url":null,"abstract":"One challenge problem in association rules mining is the huge size of the extracted rule set many of which are uninteresting and redundant. In this paper, we propose an efficient algorithm CONCISE for generating all non-redundant positive and negative association rules. We first introduce an algorithm CMG (Closed, Maximal and Generators) for mining all frequent closed, maximal and their generators itemsets from large transaction databases. We then define four new bases representing non-redundant association rules from these frequent itemsets. We prove that these bases significantly reduce the number of extracted rules. We show the efficiency of our algorithm by computational experiments compared with existing algorithms.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125202039","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-17
Anisha Tandon, Shalu Tandon
Artificial Intelligence is the theory and growth of computer systems, which can do jobs that generally, needed human intelligence, such as decision-making, visual perception, speech recognition, and translation between languages. In this paper, an essential matter regarding AI has been taken up. There is rising concern among people that AI will be taking up jobs as it is doing an outstanding job in every field. A typical example is chatbots; chatbots do the work of the individual working as Customer Care and doing partial jobs on behalf of humans. In few companies, it is handling the whole career of the humans. A simple example is Digi bank. If we do not learn about AI in the coming Future, Futrell not be surprising that the outcome of a specific business will suffer, or we may not have jobs, as AI will be a better substitute than we will. Therefore, here in this paper, the idea of promoting the study of AI is discussed using AI teaching centers.
{"title":"Artificial Intelligence: The Future of Employment","authors":"Anisha Tandon, Shalu Tandon","doi":"10.52458/978-93-91842-08-6-17","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-17","url":null,"abstract":"Artificial Intelligence is the theory and growth of computer systems, which can do jobs that generally, needed human intelligence, such as decision-making, visual perception, speech recognition, and translation between languages. In this paper, an essential matter regarding AI has been taken up. There is rising concern among people that AI will be taking up jobs as it is doing an outstanding job in every field. A typical example is chatbots; chatbots do the work of the individual working as Customer Care and doing partial jobs on behalf of humans. In few companies, it is handling the whole career of the humans. A simple example is Digi bank. If we do not learn about AI in the coming Future, Futrell not be surprising that the outcome of a specific business will suffer, or we may not have jobs, as AI will be a better substitute than we will. Therefore, here in this paper, the idea of promoting the study of AI is discussed using AI teaching centers.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117185473","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-7
Nusrat Jahan Prottasha, M. J. Uddin, M. Kowsher, Rokeya Khatun Shorna, N. Murshed, Boktiar Ahmed Bappy
Rainfall forecast is imperative as overwhelming precipitation can lead to numerous catastrophes. The prediction makes a difference for individuals to require preventive measures. In addition, the expectation ought to be precise. Most of the nations in the world is an agricultural nation and most of the economy of any nation depends upon agriculture. Rain plays an imperative part in agribusiness so the early expectation of rainfall plays a vital part within the economy of any agricultural. Overwhelming precipitation may well be a major disadvantage. It’s a cause for natural disasters like floods and drought that unit of measurement experienced by people over the world each year. Rainfall forecast has been one of the foremost challenging issues around the world in the final year. There are so many techniques that have been invented for predicting rainfall but most of them are classification, clustering techniques. Predicting the quantity of rain prediction is crucial for countries' people. In our paperwork, we have proposed some regression analysis techniques which can be utilized for predicting the quantity of rainfall (The amount of rainfall recorded for the day in mm) based on some historical weather conditions dataset. we have applied 10 supervised regressors (Machine Learning Model) and some preprocessing methodology to the dataset. We have also analyzed the result and compared them using various statistical parameters among these trained models to find the bestperformed model. Using this model for predicting the quantity of rainfall in some different places. Finally, the Random Forest regressor has predicted the best r2 score of 0.869904217, and the mean absolute error is 0.194459262, mean squared error is 0.126358647 and the root mean squared error is 0.355469615.
{"title":"Development of Multiple Combined Regression Methods for Rainfall Measurement","authors":"Nusrat Jahan Prottasha, M. J. Uddin, M. Kowsher, Rokeya Khatun Shorna, N. Murshed, Boktiar Ahmed Bappy","doi":"10.52458/978-93-91842-08-6-7","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-7","url":null,"abstract":"Rainfall forecast is imperative as overwhelming precipitation can lead to numerous catastrophes. The prediction makes a difference for individuals to require preventive measures. In addition, the expectation ought to be precise. Most of the nations in the world is an agricultural nation and most of the economy of any nation depends upon agriculture. Rain plays an imperative part in agribusiness so the early expectation of rainfall plays a vital part within the economy of any agricultural. Overwhelming precipitation may well be a major disadvantage. It’s a cause for natural disasters like floods and drought that unit of measurement experienced by people over the world each year. Rainfall forecast has been one of the foremost challenging issues around the world in the final year. There are so many techniques that have been invented for predicting rainfall but most of them are classification, clustering techniques. Predicting the quantity of rain prediction is crucial for countries' people. In our paperwork, we have proposed some regression analysis techniques which can be utilized for predicting the quantity of rainfall (The amount of rainfall recorded for the day in mm) based on some historical weather conditions dataset. we have applied 10 supervised regressors (Machine Learning Model) and some preprocessing methodology to the dataset. We have also analyzed the result and compared them using various statistical parameters among these trained models to find the bestperformed model. Using this model for predicting the quantity of rainfall in some different places. Finally, the Random Forest regressor has predicted the best r2 score of 0.869904217, and the mean absolute error is 0.194459262, mean squared error is 0.126358647 and the root mean squared error is 0.355469615.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689063","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 ability of regression algorithms to reliably identify the influencing factors of any data on the desired result is irrefutable. With the available techniques, we can investigate the main reason behind the influence of distinguishing factors on a supermarket’s sales. We’ll be building a machine learning model that can accurately predict the sales in millions of units for a given product. Our work will investigate the ability of some of the most popular ML regression algorithms to provide this information. Seven regression algorithms will be trained using data collected through supermarket sales. To gain key insights, the algorithms are compared along two axes, prediction quality and usefulness of output. This class of algorithms produces models that can be used to predict performance in sales and indicate the sources of potential market problems and quantify the potential gain.
{"title":"Performance Comparison of ML Regression Algorithms in Predicting Supermarket Sales","authors":"Balaji Jayakrishnan, Gunja Pandey, Nitika Verma, Ritika Sarkar, Muskan Dhingra, Palak D. Tandel","doi":"10.52458/978-93-91842-08-6-16","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-16","url":null,"abstract":"The ability of regression algorithms to reliably identify the influencing factors of any data on the desired result is irrefutable. With the available techniques, we can investigate the main reason behind the influence of distinguishing factors on a supermarket’s sales. We’ll be building a machine learning model that can accurately predict the sales in millions of units for a given product. Our work will investigate the ability of some of the most popular ML regression algorithms to provide this information. Seven regression algorithms will be trained using data collected through supermarket sales. To gain key insights, the algorithms are compared along two axes, prediction quality and usefulness of output. This class of algorithms produces models that can be used to predict performance in sales and indicate the sources of potential market problems and quantify the potential gain.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968423","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-10
Abhinav Chauhan, Shashwat Pandey, S. Bathla
Face Recognition is among the most useful picture handling applications and plays a significant part in the specialized field. Recognition of the human face is a functioning issue for verification purposes explicitly with regards to participation of understudies. Participation framework utilizing face recognition is a method of perceiving understudies by utilizing face biostatistics dependent on the top quality observing and other PC advances. The advancement of this framework is intended to achieve digitization of the customary process for gauging participation by calling names and keeping up with pen-paper records. Current participation methodologies are drawn-out and tedious. Participation records can be handily controlled by manual recording. The customary course of making participation and present biometric frameworks are powerless against intermediaries. This paper is accordingly proposed to handle this multitude of issues.
{"title":"Automated Attendance System with Facial Recognition Using Python","authors":"Abhinav Chauhan, Shashwat Pandey, S. Bathla","doi":"10.52458/978-93-91842-08-6-10","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-10","url":null,"abstract":"Face Recognition is among the most useful picture handling applications and plays a significant part in the specialized field. Recognition of the human face is a functioning issue for verification purposes explicitly with regards to participation of understudies. Participation framework utilizing face recognition is a method of perceiving understudies by utilizing face biostatistics dependent on the top quality observing and other PC advances. The advancement of this framework is intended to achieve digitization of the customary process for gauging participation by calling names and keeping up with pen-paper records. Current participation methodologies are drawn-out and tedious. Participation records can be handily controlled by manual recording. The customary course of making participation and present biometric frameworks are powerless against intermediaries. This paper is accordingly proposed to handle this multitude of issues.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889936","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-12
Dammavalam Srinivasa Rao, K. Lakshman Srikanth, J. Noshitha Padma Pratyusha, M. Sucharitha, M. Tejaswini, T. Ashwini
No matter how well-known colleges are, there will always be concerns that people have during the application process and even after they have been accepted. The college hosts a variety of events, ranging from departmental activities to club activities. Not everyone is likely aware of all events. Chatbot bridges gap between people and information. The world is becoming more automated, and people expect services to become more automated as well. A chatbot is software that responds to user questions and provides information from a knowledge base. The purpose of this project is to create a chatbot for VNRVJIET that will answer queries raised about fests, departmental activities, events, clubs, infrastructure, placement data, admission procedure, and others. The proposed methodology consists of a chatbot built using Deep Neural Networks and speech recognition capabilities. The information is delivered in both speech and text modes using the proposed methodology. Data is collected and formatted in JSON format initially. The prepared data is preprocessed and then the bag of words algorithm is applied to it. The bag of words algorithm is most influential method for object categorization. The key aspect of using this algorithm is for converting the word vector to a numerical data set for machine to do a deeper analysis. A deep neural network is created using tensor flow API, and the speech recognition function is defined for the input query and output response. Finally, chatbot function is defined and utilized for generating responses for any given query.
{"title":"Development of Artificial Intelligence based Chatbot Using Deep Neural Network","authors":"Dammavalam Srinivasa Rao, K. Lakshman Srikanth, J. Noshitha Padma Pratyusha, M. Sucharitha, M. Tejaswini, T. Ashwini","doi":"10.52458/978-93-91842-08-6-12","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-12","url":null,"abstract":"No matter how well-known colleges are, there will always be concerns that people have during the application process and even after they have been accepted. The college hosts a variety of events, ranging from departmental activities to club activities. Not everyone is likely aware of all events. Chatbot bridges gap between people and information. The world is becoming more automated, and people expect services to become more automated as well. A chatbot is software that responds to user questions and provides information from a knowledge base. The purpose of this project is to create a chatbot for VNRVJIET that will answer queries raised about fests, departmental activities, events, clubs, infrastructure, placement data, admission procedure, and others. The proposed methodology consists of a chatbot built using Deep Neural Networks and speech recognition capabilities. The information is delivered in both speech and text modes using the proposed methodology. Data is collected and formatted in JSON format initially. The prepared data is preprocessed and then the bag of words algorithm is applied to it. The bag of words algorithm is most influential method for object categorization. The key aspect of using this algorithm is for converting the word vector to a numerical data set for machine to do a deeper analysis. A deep neural network is created using tensor flow API, and the speech recognition function is defined for the input query and output response. Finally, chatbot function is defined and utilized for generating responses for any given query.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131480272","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-5
D. S. Rao, N. Rajasekhar, D. Sowmya, D. Archana, T. Hareesha, S. Sravya
People got to know about the world from newspapers to today’s digital media.From 1605 to 2021 the topography of news has evolved at an immense. People forgotten about newspapers and habituated to digital devices so that they can view it at anytime and anywhere soon it became a crucial asset for people. From the past few years fake news also evolved and people always being believed by the available fake news who are being shared by fake profiles in digital media. Currently numerous approaches for detecting fake news by neural networks in one-directional model. We proposed BERT- Bidirectional Encoder Representations from Transformers is the bidirectional model where it uses left and right content in each word so that it is used for pre-train the words into two-way representations from unlabeled words it shown an excellent result when dealt with fake news it attained 99% of accuracy and outperform logistic regression and K-Nearest Neighbors. This method became a crucial in dealing with fake news so that it improves categorization easily and reduces computation time. Through this proposal, we are aiming to build a model to spot fake news present across various sites. The motivation behind this work to help people improve the consumption of legitimate news while discarding misleading information relationship in social media. Classification accuracy of fake news may be improved from the utilization of machine learning ensemble methods.
{"title":"Fake News Detection Using Machine Learning Technique","authors":"D. S. Rao, N. Rajasekhar, D. Sowmya, D. Archana, T. Hareesha, S. Sravya","doi":"10.52458/978-93-91842-08-6-5","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-5","url":null,"abstract":"People got to know about the world from newspapers to today’s digital media.From 1605 to 2021 the topography of news has evolved at an immense. People forgotten about newspapers and habituated to digital devices so that they can view it at anytime and anywhere soon it became a crucial asset for people. From the past few years fake news also evolved and people always being believed by the available fake news who are being shared by fake profiles in digital media. Currently numerous approaches for detecting fake news by neural networks in one-directional model. We proposed BERT- Bidirectional Encoder Representations from Transformers is the bidirectional model where it uses left and right content in each word so that it is used for pre-train the words into two-way representations from unlabeled words it shown an excellent result when dealt with fake news it attained 99% of accuracy and outperform logistic regression and K-Nearest Neighbors. This method became a crucial in dealing with fake news so that it improves categorization easily and reduces computation time. Through this proposal, we are aiming to build a model to spot fake news present across various sites. The motivation behind this work to help people improve the consumption of legitimate news while discarding misleading information relationship in social media. Classification accuracy of fake news may be improved from the utilization of machine learning ensemble methods.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129579037","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 : 1900-01-01DOI: 10.52458/978-93-91842-08-6-8
Shilpa Mehta
Most microprocessors and microcontrollers are based on Digital Electronics building Blocks. Digital Electronics gives us a number of combinational and sequential circuits for various arithmetic and logical operations. These include Adders, Subtracters, Encoders, Decoders, Multiplexers, DE multiplexers and Flip Flops. These further combine into higher configurations to perform advanced operations. These operations are done using logic circuits in digital electronics. But in this paper, we explore the human reasoning approach using artificial neural networks. We will look into neural implementations of logic gates implemented with SLP (Single layer perceptron) and MLP (Multi-Layer Perceptron). We will also look into recurrent neural architectures to make basic memory elements, viz. Flip Flops which use feedback and may involve in one or more neuron layers.
{"title":"Digital Building Blocks using Perceptrons in Neural Networks","authors":"Shilpa Mehta","doi":"10.52458/978-93-91842-08-6-8","DOIUrl":"https://doi.org/10.52458/978-93-91842-08-6-8","url":null,"abstract":"Most microprocessors and microcontrollers are based on Digital Electronics building Blocks. Digital Electronics gives us a number of combinational and sequential circuits for various arithmetic and logical operations. These include Adders, Subtracters, Encoders, Decoders, Multiplexers, DE multiplexers and Flip Flops. These further combine into higher configurations to perform advanced operations. These operations are done using logic circuits in digital electronics. But in this paper, we explore the human reasoning approach using artificial neural networks. We will look into neural implementations of logic gates implemented with SLP (Single layer perceptron) and MLP (Multi-Layer Perceptron). We will also look into recurrent neural architectures to make basic memory elements, viz. Flip Flops which use feedback and may involve in one or more neuron layers.","PeriodicalId":247665,"journal":{"name":"SCRS Conference Proceedings on Intelligent Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124775761","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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