Computational Intelligence and Machine Learning最新文献_第2页

Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers 基于随机森林分类器的足部撞击事件特征选择

Computational Intelligence and Machine Learning Pub Date : 2023-04-14 DOI: 10.36647/ciml/04.01.a005

Meghna Desai, Dr. Viral Kapadia

{"title":"Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers","authors":"Meghna Desai, Dr. Viral Kapadia","doi":"10.36647/ciml/04.01.a005","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a005","url":null,"abstract":"Automated Gait event identification of Foot Strike (FS) and Foot Off (FO) in pathological gait data, can be time saving in comparison to conventional manual annotations done currently. Identification of FS and FO allows breaking walking trials into gait cycles and hence aids in comparison of gait parameters like joint angles, forces and moments across gait cycles. Automated Gait Event Detection is also useful in development of wearable sensor devices and robotic systems that assist gait. Researchers have proposed several automatic gait event detection algorithms based on kinematic parameters and systematic study of the literature suggests specific parameters to have higher contribution in identification of FS event in all common pathological gait patterns. We used Random Forest Classifier Feature selection technique to identify high contributing features in FS event in toe walking pediatric pathological gait dataset and the results suggest high similarity in selected features by the machine learning technique with those suggested by popular event detection algorithms based on kinematic parameters for pathological gait. Hence we conclude that RFC feature selection is suitable for feature selection in toe walkers gait dataset for event detection purpose. Keyword : Feature selection, foot off, foot strike, pathological gait.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132113298","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

Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms 基于潜在语义分析和模糊算法的马拉地语提取文本摘要

Computational Intelligence and Machine Learning Pub Date : 2023-04-14 DOI: 10.36647/ciml/04.01.a008

Virat V. Giri, Dr.M.M. Math, Dr.U.P. Kulkarni

{"title":"Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms","authors":"Virat V. Giri, Dr.M.M. Math, Dr.U.P. Kulkarni","doi":"10.36647/ciml/04.01.a008","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a008","url":null,"abstract":"Extractive text summarization involves the retention of only the most important sentences in a document. In the past, multiple approaches involving both statistical and machine learning-based methods have been used for this task. The crucial step in extractive text summarization is getting the right ranking order of sentences in the document in terms of their importance. Singular value decomposition or SVD algorithm based on latent semantic analysis focuses on recognizing the sections in the document which are related in terms of their semantic nature. Fuzzy algorithms involve reasoning of the priority order of the sentences using fuzzy logic unlike the use of discrete values. While significant work has been done for extractive text summarization in English and other foreign languages, there is ample scope for improving the performance of systems when dealing with Marathi text. In this paper, SVD and fuzzy algorithms are proposed for performing extractive text summarization on Marathi documents. Work is done upon the modeling principle, data flow, and parameters of these algorithms such that they are best suited for the task. An analysis of the characteristics of both these techniques is conducted to compare their benefits and shortcomings. The performance of both the algorithms is evaluated on a document dataset using standard performance metrics including the ROUGE metric. An unbiased comparison of both these techniques is carried out to inform the applicability of them, especially when working with Marathi or in general, non-English text.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131764523","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

Credit Card Fraud Detection Using Machine Learning 使用机器学习的信用卡欺诈检测

Computational Intelligence and Machine Learning Pub Date : 2023-04-14 DOI: 10.36647/ciml/04.01.a009

Vishal Vishal Kumar

{"title":"Credit Card Fraud Detection Using Machine Learning","authors":"Vishal Vishal Kumar","doi":"10.36647/ciml/04.01.a009","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a009","url":null,"abstract":"To make life better, many mechanisms in modern environment are carried out via the Internet. The economy is expanding yet on the other side, there is a lot of illegal and unauthorised activity carried throughout the country that is seriously hampering that progress. Scam instances, which mislead individuals while also causing economic losses, are just one of them. In realistic conditions, fraud involving credit cards surveillance is the main emphasis of this research. Contrary to earlier eras, the number of credit card scammers is drastically increasing right now. Criminals use various forms of innovation, fake documents, and deception to con others and take their cash. Therefore, it is extremely crucial to discover a solution to these frauds. As technology advances, it becomes harder to keep up with the behaviour and trends of illegal activities. Ai technology, machine learning, as well as other relevant data technology fields have advanced to the point that it is currently feasible to expedite this process and reduce the volume of labour-intensive effort needed in recognizing credit card scams. The user-submitted utilization of credit cards database might be collected initially, then using machine learning approach; it would be split into databases for testing and training purposes. This methodical technique could be utilized by researchers once they have evaluated both the larger information collection and the user-provided available data collection. Enhance the accuracy of the outcome statistics after that. Depending on its exactness and precision, a technology's efficiency is assessed. The results show that XG-Boost and Random Forest techniques have the greatest performance.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116209612","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

Use of Machine Learning for Continuous Improvement and Handling Multi-Dimensional Data in Service Sector 机器学习在服务业持续改进和多维数据处理中的应用

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a006

V. S. Lohit, Mohammed Mutahar Mujahid, Galipally Kushal Sai

{"title":"Use of Machine Learning for Continuous Improvement and Handling Multi-Dimensional Data in Service Sector","authors":"V. S. Lohit, Mohammed Mutahar Mujahid, Galipally Kushal Sai","doi":"10.36647/ciml/03.02.a006","DOIUrl":"https://doi.org/10.36647/ciml/03.02.a006","url":null,"abstract":"Machine learning is known as a significant pattern of AI that gives an effective allowance to the software applications to become precise at forecasting outcomes without explicitly programmed in doing that. In addition, machine learning is important as this gives service sectors a suitable view of trends in “business operational patterns” and consumer behaviors. Service sectors are mainly known as the healthcare sectors, tourism sectors, and transportation sectors. In several developed countries, AI is maximizing labor productivity by more than 30% in the coming 15 years. The requirement of showing the usage of machine learning and the way it handles the multi-dimensional data have also been shown in this entire work. Machine learning shows some ways through that it helps in providing improvement to all the service sectors such as enhancing consumer analytics, giving rapid and effective assistance, providing effective personalization, identifying the fraud cases and also enhancing customer experiences. Though, in this research work it has been highlighted that, in terms of implementing ML in service sectors, service sectors are facing several challenges. Moreover, in terms of showing the effectiveness of ML two algorithms with flowcharts have been shown in this work. On the other hand, in this research work, a secondary data collection method has been utilized and a qualitative data analysis method has also been used in this research work. In addition, secondary data resources have been assembled from books, scholarly articles, journals, and newspapers. Index Terms : Machine learning, secondary data resources, AI, Service sectors.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122654103","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 Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd 人群中行人检测的深度学习框架研究

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a002

Shaamili R

{"title":"A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd","authors":"Shaamili R","doi":"10.36647/ciml/03.02.a002","DOIUrl":"https://doi.org/10.36647/ciml/03.02.a002","url":null,"abstract":"In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128778454","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

Satellite Image Processing Using Fuzzy Logic and Modified K-Means Clustering Algorithm for Image Segmentation 基于模糊逻辑和改进k均值聚类算法的卫星图像分割

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a008

Geerisha Jain

{"title":"Satellite Image Processing Using Fuzzy Logic and Modified K-Means Clustering Algorithm for Image Segmentation","authors":"Geerisha Jain","doi":"10.36647/ciml/03.02.a008","DOIUrl":"https://doi.org/10.36647/ciml/03.02.a008","url":null,"abstract":"Satellite images are useful in providing a real time dynamic picture of the earth and its environment. The large assemblage of remote sensing satellites orbiting the earth provide an extensive and periodic coverage of the planet through the capture of live images round the clock, in turn enabling numerous uses for the benefit of mankind. In the field of satellite image processing, image segmentation is one of the vital steps for extracting and gathering huge amount of information from the satellite images. The basic k-means clustering algorithm is simple and fast in terms of dealing with the required segmentation, but the limitation associated with this clustering is its inability to produce the same result for every run, as the resulting clusters depends on the initial random assignments. In this paper, an enhanced modified k-means clustering algorithm is proposed for the effective segmentation of the satellite images with an objective to overcome the demerits of the traditional k-means by combining fuzzy logic with the membership function. The proposed methodology continuously produces the same result for each run. As an outcome, the experimental results proved that the enhanced k-means algorithm is an effective and more efficient process for the precise and accurate segmentation of satellite images. Index Terms : Image Segmentation, Satellite Imagery, Fuzzy logic, K-Means, Clustering.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126219635","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

Rice and Wheat Yield Prediction in India Using Decision Tree and Random Forest 基于决策树和随机森林的印度水稻和小麦产量预测

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a001

Dr. Sagar B M, Dr. Cauvery N K, Dr.Padmashree T, D. R

引用次数: 0

Biomedical Applications using Hand Gesture with Electromyography Control Signal 手势与肌电控制信号的生物医学应用

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a005

Vaishali M. Gulhane, Dr. Amol Kumbhare

引用次数: 0

An Analysis of the Supervised Learning Approach for Online Fraud Detection 在线欺诈检测的监督学习方法分析

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a007

D. H. Reddy

引用次数: 0

A Review of Deep Learning Techniques for Encrypted Traffic Classification 加密流量分类的深度学习技术综述

Computational Intelligence and Machine Learning Pub Date : 2022-10-14 DOI: 10.36647/ciml/03.02.a003

A. Iliyasu, I. Abba, Badariyya Sani Iliyasu, A. Muhammad

引用次数: 0