2022 IEEE World Conference on Applied Intelligence and Computing (AIC)最新文献_第10页

Personality Traits Prediction from Text via Machine Learning 通过机器学习从文本中预测人格特征

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848937

Alessandro Bruno, Gurmeet Singh

引用次数: 3

A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms on Consumer Reviews 有监督与无监督机器学习算法在消费者评论上的比较研究

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848880

Kartika Makkar, Pardeep Kumar, Monika Poriye, Shalini Aggarwal

{"title":"A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms on Consumer Reviews","authors":"Kartika Makkar, Pardeep Kumar, Monika Poriye, Shalini Aggarwal","doi":"10.1109/AIC55036.2022.9848880","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848880","url":null,"abstract":"For any organization involving consumers, reviews and feedbacks are quite important. For this purpose, the bulk of data is generated from various social networking sites in terms of reviews and feedbacks. In order to understand consumer’s perception about an item, this research scrutinizes various supervised and unsupervised machine learning algorithms on two data sets. A comparative analysis is made for deliberating the efficiency of these algorithms on distinct datasets for text classification. This research is an attempt to find the best fit classifier for consumer’s perception using sentiment analysis. So, in order to accomplish this objective, firstly text preprocessing techniques are applied on datasets then feature extraction techniques are applied on the processed data. Thereafter, classification and clustering are applied using supervised and unsupervised machine learning algorithms respectively. Further, these algorithms are evaluated and the result reveals that supervised machine learning algorithms especially Support Vector Machine (SVM) outperforms unsupervised machine learning algorithms for garments dataset. And Naive Bayes (NB), Logistic Regression (LR) outperforms unsupervised machine learning algorithms for restaurant dataset.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133395585","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

Improved Hotel Recommendation System Using Machine Learning Technique 利用机器学习技术改进酒店推荐系统

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848942

Er. Abdul Hafiz, Narinderpal Kaur

引用次数: 1

AIC 2022 Cover Page AIC 2022封面

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/aic55036.2022.9848908

引用次数: 0

Study on Online Review Based Consumer sentimental Analysis using Machine Learning Approaches 基于在线评论的消费者情感分析的机器学习方法研究

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848932

C. S. R. Priya, P. Deepalakshmi

引用次数: 0

Capsule Networks with Chest X-Ray Enhancement for Detection of COVID-19 胸部x线增强胶囊网络检测COVID-19

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848962

Pulkit Sharma, Rhythm Arya, Richa Verma, Bindu Verma

{"title":"Capsule Networks with Chest X-Ray Enhancement for Detection of COVID-19","authors":"Pulkit Sharma, Rhythm Arya, Richa Verma, Bindu Verma","doi":"10.1109/AIC55036.2022.9848962","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848962","url":null,"abstract":"Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus’s transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"639 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122193471","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

IoT Framework for Healthcare: A Review 医疗保健物联网框架:综述

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848923

Mehtab Alam, I. Khan, M. A. Alam, Farheen Siddiqui, Safdar Tanweer

{"title":"IoT Framework for Healthcare: A Review","authors":"Mehtab Alam, I. Khan, M. A. Alam, Farheen Siddiqui, Safdar Tanweer","doi":"10.1109/AIC55036.2022.9848923","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848923","url":null,"abstract":"In the past decade, we have seen an exponential rise in the development and implementation of IoT (Internet of Things) technology space. The main forces driving the rise of the technology has been the increase in production of wireless devices and sensors leading to increased availability and the decrease in cost and maintenance of these devices. Initially, the use of IoT devices was limited to developed cities and countries, with very strong network infrastructures and high GDP value. However, with the above-mentioned factors, the IoT market is reaching out to lesser developed or developing countries, enabling them to implement and make use of the technology. Today, IoT has touched every sphere of living, from streets, to homes, offices to vehicles, tourism to leisure, healthcare to sports etc. A great deal of work has already been carried in the field of IoT in healthcare, but it was recognized that there is a lack of descriptive analysis on IoT framework for healthcare. The aim of the paper is to identify and analyse literature that is peer reviewed and introduces some new knowledge about the use of IoT in producing secure healthcare applications and present the systematic analysis of the identified literature. Further, the paper tries to highlight some challenges as well as future research directions in securing healthcare data using Internet and IoT.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"385 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133817647","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

Robotic Arm with Obstacle Detection Designed for Assistive Applications 为辅助应用而设计的具有障碍物检测的机械臂

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848935

Nate Ruppert, K. George

引用次数: 0

Extreme learning machine tuning by original sine cosine algorithm 极限学习机调整原始正弦余弦算法

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848960

Mohamed Salb, N. Bačanin, M. Zivkovic, Milos Antonijevic, Marina Marjanovic, I. Strumberger

引用次数: 0

IOT Assisted Smart Farming using Data Science Techniques 使用数据科学技术的物联网辅助智能农业

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848867

Vikas Verma, Ramakant, Hemant Mathur, Neha Agarwal

{"title":"IOT Assisted Smart Farming using Data Science Techniques","authors":"Vikas Verma, Ramakant, Hemant Mathur, Neha Agarwal","doi":"10.1109/AIC55036.2022.9848867","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848867","url":null,"abstract":"The rising global population demands a high yield of crop production. At present, farmers grow crops for all the people, but in case of contracting horticultural grounds and exhaustion of limited regular assets due to many reasons, and a massive increase in population, the need to improve ranch yield has turned out to be essential. Nowadays, there are various startups, technology innovators, and steps taken by the government that work to enhance total crop production. All those innovations taken for the farming framework are called smart Farming (SF). Smart Farming includes consolidating data and correspondence advances into apparatus, hardware, and sensors in the rural creation framework. The advancement of technologies must be reduced to convey meaningful information. The economy of nations like India is highly dependent on agricultural production. So disease detection in plants using an efficient algorithm is supposed to be a vital job in the farming field. This research paper is presented in three-fold:(1) Efficient way to detect disease and find cavity area; It presents Image Segmentation Algorithm (2) Using data analysis in different ways which will work for crops in a better way. The paper also presents two analysis methodologies, one is based on using an optical transducer for detecting the presence of Nitrogen (N), Phosphorous (P), and Potash (K) in soil, and the other analysis is based on the moisture content of the soil using sensors. (3)using machine learning algorithms to predict the number of fertilizers based on the collected features of soil samples, which will help farmers in amount prediction.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127090972","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