SCRS Conference Proceedings on Intelligent Systems最新文献

A Review on Quality of Image during CBIR Operations and Compression CBIR操作与压缩过程中图像质量的研究进展

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-19

D. Singla, D. Kumar, Sakshi Dhingra

{"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}

引用次数: 0

Performance Evaluation for Detection of Cardiovascular Disease using Different Methods 不同方法检测心血管疾病的性能评价

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-18

Neerajkumar S Sathawane, U. M. Gokhale, D. Padole

引用次数: 0

CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules 简练:一种挖掘正负非冗余关联规则的算法

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-2

Bemarisika Parfait, Totohasina André

引用次数: 0

Artificial Intelligence: The Future of Employment 人工智能:就业的未来

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-17

Anisha Tandon, Shalu Tandon

引用次数: 0

Development of Multiple Combined Regression Methods for Rainfall Measurement 降雨测量中多元组合回归方法的发展

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-7

Nusrat Jahan Prottasha, M. J. Uddin, M. Kowsher, Rokeya Khatun Shorna, N. Murshed, Boktiar Ahmed Bappy

{"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}

引用次数: 0

Performance Comparison of ML Regression Algorithms in Predicting Supermarket Sales ML回归算法在超市销售预测中的性能比较

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-16

Balaji Jayakrishnan, Gunja Pandey, Nitika Verma, Ritika Sarkar, Muskan Dhingra, Palak D. Tandel

引用次数: 0

Automated Attendance System with Facial Recognition Using Python 使用Python的面部识别自动考勤系统

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-10

Abhinav Chauhan, Shashwat Pandey, S. Bathla

引用次数: 0

Development of Artificial Intelligence based Chatbot Using Deep Neural Network 基于深度神经网络的人工智能聊天机器人开发

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-12

Dammavalam Srinivasa Rao, K. Lakshman Srikanth, J. Noshitha Padma Pratyusha, M. Sucharitha, M. Tejaswini, T. Ashwini

{"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}

引用次数: 1

Fake News Detection Using Machine Learning Technique 利用机器学习技术检测假新闻

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-5

D. S. Rao, N. Rajasekhar, D. Sowmya, D. Archana, T. Hareesha, S. Sravya

{"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}

引用次数: 0

Digital Building Blocks using Perceptrons in Neural Networks 在神经网络中使用感知器的数字构建块

SCRS Conference Proceedings on Intelligent Systems Pub Date : 1900-01-01 DOI: 10.52458/978-93-91842-08-6-8

Shilpa Mehta

引用次数: 0