Computational Intelligence and Machine Learning最新文献_第4页

Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review 软计算下5G认知无线网络混合频谱感知技术综述

Computational Intelligence and Machine Learning Pub Date : 2021-04-20 DOI: 10.36647/ciml/02.01.a006

Nishat Nabila Haque Neshe, Manwinder Singh

引用次数: 2

Parametric optimization of Solar Parabolic Collector using metaheuristic Optimization 基于元启发式优化的太阳能抛物面集热器参数优化

Computational Intelligence and Machine Learning Pub Date : 2021-04-20 DOI: 10.36647/ciml/02.01.a004

P. Dutta, Supradip Kumar Biswas, Madhurima Majumder

引用次数: 6

Sentiment Analytics on Chinese Product Boycott from Multiple Data Sources 基于多数据源的中国产品抵制情绪分析

Computational Intelligence and Machine Learning Pub Date : 2021-04-20 DOI: 10.36647/ciml/02.01.a003

D. Mane, D. S. Kotrappa, Kiran Shibe

引用次数: 0

Comparative Analysis of Stock Price Prediction by ANN and RF Model 基于神经网络和射频模型的股票价格预测的比较分析

Computational Intelligence and Machine Learning Pub Date : 2021-04-20 DOI: 10.36647/ciml/02.01.a001

Lopamudra Hota, P. Dash

{"title":"Comparative Analysis of Stock Price Prediction by ANN and RF Model","authors":"Lopamudra Hota, P. Dash","doi":"10.36647/ciml/02.01.a001","DOIUrl":"https://doi.org/10.36647/ciml/02.01.a001","url":null,"abstract":"The elementary goal of this paper is to predict the best model for estimation of stock market. Machine Learning is a blooming field in computer science that has contributed to many predictions and analysis-based algorithm in Financial and economical field. Some of the algorithms used for predictions are Random Forest (RF), Support vector machine (SVM), Long-Short Term Memory (LSTM), Artificial Neural Networks (ANN). Random Forest is an ensemble supervised learning algorithm for classification problems with high accuracy factor. ANN has matured to a great extend over the past years. With the advent of high-performance computing ANN has assumed tremendous significance and huge application potentials in recent years. The innovation of ANN technology mimics the large interconnections and networking that exists between the nerve cells to process complex task. The paper has presented ANN and RF model for stock price estimation based on historical data and computed the future price, with comparative result analysis of their performance. Further, a candlestick model is designed of the stock to show the variation in price of stock over a stipulated period of time. Keyword: Random Forest, Candle-stick, ANN, RNN, CNN, Support Vector Machine, Deep Learning","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570544","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

Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System 协同模糊聚类机制与数据驱动模糊神经系统

Computational Intelligence and Machine Learning Pub Date : 2020-08-21 DOI: 10.36647/ciml/01.01.a001

Rudra Bhanu Satpathy, Siddth Kumar Chhajer

{"title":"Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System","authors":"Rudra Bhanu Satpathy, Siddth Kumar Chhajer","doi":"10.36647/ciml/01.01.a001","DOIUrl":"https://doi.org/10.36647/ciml/01.01.a001","url":null,"abstract":"In this Research, Fuzzy Logic guideline move system for self-developing Fuzzy neural deduction systems is anticipated. Highlights of proposed strategy, named (CFCM-DDNFS)Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System mechanismwere; (1) Fuzzy guidelines are produced simply by Fuzzy c-means (FCM) and afterward adjusted by the (PCFC)preprocessed synergistic Fuzzy clustering method, and (2) Boundary & Structured learning are accomplished at the same time without choosing the underlying boundaries. The CFCM-DDNFS could implemented to manage enormous information issues by the goodness of the PCFC method, which is fit for managing colossal data-sets while saving the protection and security of data-sets. At first, the whole data-set is composed into two individual data-sets for the PCFC system, where each of the data-set is bunched independently. The information on model factors (bunch focuses) and the lattice of only one divide of data-set across synergistic strategy are sent. CFCM-DDNFS can accomplish consistency within the sight of aggregate information on the PCFC and lift the framework demonstrating procedure by boundary learning capacity of oneself developing neural Fuzzy induction systems (SONFIN). Proposed strategy beats existing strategies for time arrangement forecast issues. Keyword : Fuzzy system,Neural network, Big Data, On-line learning framework,Privacy & security, Time arrangement expectation, Collaborative strategy.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129790106","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

Electronic Mail Classification System Based on Machine Learning approach 基于机器学习方法的电子邮件分类系统

Computational Intelligence and Machine Learning Pub Date : 2020-08-21 DOI: 10.36647/ciml/01.01.a003

Subhrajyoti Ranjan Sah, S. J

引用次数: 0

Deception Recognition Method Based on Machine Learning 基于机器学习的欺骗识别方法

Computational Intelligence and Machine Learning Pub Date : 2020-08-21 DOI: 10.36647/ciml/01.01.a002

Siddth Kumar Chhajer, Rudra Bhanu Satpathy

{"title":"Deception Recognition Method Based on Machine Learning","authors":"Siddth Kumar Chhajer, Rudra Bhanu Satpathy","doi":"10.36647/ciml/01.01.a002","DOIUrl":"https://doi.org/10.36647/ciml/01.01.a002","url":null,"abstract":"Money extortion is a developing issue with far results in the budgetary business and keeping in mind that numerous procedures have been found. Information removal is effectively functional to back records to computerize the investigation of colossal volumes of multifaceted information. Information removal has additionally assumed a notable job in the location of Visa deception in online exchanges. Deception recognition in credit card is an information mining issue, it gets testing because of two significant reasons–first, the profiles of typical and deceitful practices change much of the time and besides because of the reason that Mastercard extortion informational collections are exceptionally slanted. This paper examines and analyze the presence of the Decision tree, Random Forest, SVM, and strategic regression on exceptionally slanted credit card extortion information. Dataset of Visa exchanges is sourced from European cardholders containing 274,335 exchanges. These function are used to crude and preprocessed information. The presentation of the strategies is assessed dependent on exactness, affectability, explicitness, accuracy. The outcomes demonstrate the ideal accuracy for logistic regression, decision tree, Random Forest and SVM classifiers are 96.8%, 94.4%,99.5%, and 96.6%. Keyword : Credit Card,Decision Tree,Deception Recognition, and Support Vector Machine.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116859338","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

Recurrent Neural Networks for Recommender Systems 推荐系统的递归神经网络

Computational Intelligence and Machine Learning Pub Date : 2020-08-21 DOI: 10.36647/ciml/01.01.a004

Ankit Rath, S. Sahu

引用次数: 0

Artificial Intelligence Role in Individual Decision Making 人工智能在个人决策中的作用

Computational Intelligence and Machine Learning Pub Date : 2020-08-21 DOI: 10.36647/ciml/01.01.a005

S. J, S. S

{"title":"Artificial Intelligence Role in Individual Decision Making","authors":"S. J, S. S","doi":"10.36647/ciml/01.01.a005","DOIUrl":"https://doi.org/10.36647/ciml/01.01.a005","url":null,"abstract":"By the partial blast in large facts and the constant obligation for growth, AI is cutting available a better advert in our common community. Over the situation terminal based abilities, it transmits additional opportunities to switch numerous problems inside suggestions. It additionally raises new difficulties about its utilization and cutoff points. This theory expects to offer a higher ability of the occupation of people and AI in the hierarchical Decision-Making method. An examination centres on information serious firms. The fundamental exploration question that directs our examination is the accompanying one: In what capacity can AI re-plan and build up the procedure of authoritative Decision-Making inside information serious firms? We defined three increasingly point by point inquiries to direct us: (1) what are the jobs of people and AI in the Decision-Making procedure? (2) How can authoritative structure boost the Decision-Making procedure using Artificial Intelligence? (3) How can AI help to defeat the difficulties experienced by leaders inside information concentrated firms and what are the new difficulties that emerge from the utilization of AI in the Decision-Making procedure?. Keyword : Artificial Intelligence, Decision makers, Decision making process, Knowledge-concentrated firms, Organizational structure, Organizational test, Smart decisions.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126394693","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

A Review on Artificial Intelligence – Assisted CCTA Imaging for CAD Diagnosis 人工智能辅助CCTA成像在CAD诊断中的研究进展

Computational Intelligence and Machine Learning Pub Date : 2020-04-20 DOI: 10.36647/ciml/03.01.a004

J. A., A. Bevi

引用次数: 0