2023 International Conference on Emerging Smart Computing and Informatics (ESCI)最新文献_第5页

Popularity Preserving Fair Matching 保持人气公平配对

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099911

Toru Nakamura, T. Isohara

引用次数: 0

Review of Features and Classification for Spoken Indian Language Recognition using Deep Learning and Machine Learning Techniques 使用深度学习和机器学习技术的印度口语识别的特征和分类综述

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099742

Shreyasi Watve, M. Patil, Arun C. Shinde

{"title":"Review of Features and Classification for Spoken Indian Language Recognition using Deep Learning and Machine Learning Techniques","authors":"Shreyasi Watve, M. Patil, Arun C. Shinde","doi":"10.1109/ESCI56872.2023.10099742","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099742","url":null,"abstract":"The most common and organic method of interpersonal communication is speech. Humanity has long aspired to creating a computer with human-like comprehension and communication abilities. To recognize language (words and phrases) using voice signals, multilingual countries like India must be taken into account. More research on speech has been demanded by specialists over the past ten years. Researchers require a specific database, or previously recorded collection of information, for that specific recognition system when they seek to construct it. There are several speech databases available for European languages, but only a small number for Indian languages. The several Speech Databases developed in various Indian languages for Text to Speech, Speaker Identification and Speech Identification systems are discussed in this article. To accurately identify the spoken language, first need to collect information from speech signal. In the initial step of the pre-processing phase, audio feature based approach were used, and then deep learning and machine learning classification methods. This survey will explore a variety of feature extraction methods as well as classification methods.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122866430","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

AI and BIM-based Construction defects, rework, and waste optimization 基于AI和bim的建筑缺陷、返工和废物优化

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099726

Pradnya Desai, S. Sandbhor, A. Kaushik

{"title":"AI and BIM-based Construction defects, rework, and waste optimization","authors":"Pradnya Desai, S. Sandbhor, A. Kaushik","doi":"10.1109/ESCI56872.2023.10099726","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099726","url":null,"abstract":"Any country's economic progress, especially economic expansion, depends heavily on the building industry. The construction industry consumes tremendous amount of money, time and energy. Over the last two decades, many reports and studies have concluded that quality and productivity are decreasing due to defective work. The corrective activities of addressing defects and rework, consume time and cost. Even though there are several reasons for cost and time overrun, rework has a significant effect. It is essential to emphasize the impact of construction rework, construction defects and waste generated through research. Use of soft computing methods is recommended to increase the general efficiency of the construction projects. The goal of this research study is to undertake a bibliographic survey of the relevant literature on construction rework, construction defects and the application of Artificial Intelligence (AI) and Building Information Modeling (BIM) to optimize the output. The time considered for this survey is from the year 2007–2022. This bibliographic analysis contains statistics on citations, important journals, countries, authors contributing to the domain of knowledge based on search engine on Web of Science database. The results of the study highlight the current publication trends emphasizing on the necessity of applying BIM-based AI techniques to defects, rework and waste management. Study shows the quantum of work done in the domain from Indian context, highlighting the need for research.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500708","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

Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis 基于情绪分析的股票价格预测机器学习算法比较

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099875

Anuradha Yenkikar, C. Babu

{"title":"Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis","authors":"Anuradha Yenkikar, C. Babu","doi":"10.1109/ESCI56872.2023.10099875","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099875","url":null,"abstract":"Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272167","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

Spectrum Inference in Cognitive Radio Networks with Machine Learning 基于机器学习的认知无线电网络频谱推断

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099745

Mudassar Husain Naikwadi, K. Patil

{"title":"Spectrum Inference in Cognitive Radio Networks with Machine Learning","authors":"Mudassar Husain Naikwadi, K. Patil","doi":"10.1109/ESCI56872.2023.10099745","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099745","url":null,"abstract":"Wireless radio spectrum is a limited resource. Increasing demand for more spectrum bands has led to the notion of its efficient and intelligent utilization. Cognitive radio technology is the front runner in dynamic spectrum access. Basic spectrum management tasks of sensing, mobility, sharing and decision have been improved by using machine learning techniques. Real time sensing and related operations thereafter involve considerable time delays leading to decreased throughput. Spectrum Inference has emerged as an effective solution to this problem. In this work we have analyzed machine learning based spectrum inference techniques for real world dataset. Spectrum band occupancy prediction has been formulated as a regression problem. Three regression based approaches namely linear regression interactions, SVM based regression and decision tree regression have been evaluated. It has been observed that fine tree regression gives the best performance. To optimize the performance in terms of prediction speed and accuracy we have investigated the use of improved and increased number of features. With addition of a single additional feature the prediction speed has increased by 4.73 times and prediction accuracy by 3%. However the training time has increased by 1.24 times.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123410092","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

Mitigation of Straggler in Virtual Machine Stack Using Supervised Learning Methodology 利用监督学习方法缓解虚拟机堆栈中的掉队者

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099658

Reshma S. Gaykar, V. Khanaa, S. Joshi

{"title":"Mitigation of Straggler in Virtual Machine Stack Using Supervised Learning Methodology","authors":"Reshma S. Gaykar, V. Khanaa, S. Joshi","doi":"10.1109/ESCI56872.2023.10099658","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099658","url":null,"abstract":"Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. The suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual Machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. The whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281640","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 Blockchain-Based Approach to Streamlining Land Registration using Hyperledger Fabric 一种基于区块链的方法，使用超级账本结构简化土地注册

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099592

Limark Dcunha, Larren Dsouza, G. Ramanan, S. Chaudhari

{"title":"A Blockchain-Based Approach to Streamlining Land Registration using Hyperledger Fabric","authors":"Limark Dcunha, Larren Dsouza, G. Ramanan, S. Chaudhari","doi":"10.1109/ESCI56872.2023.10099592","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099592","url":null,"abstract":"A broker creates a connection between the buyer and seller under India's traditional land registration system. For instance, the broker would compile and gather all essential physical paperwork that serves as evidence of the transaction if someone wanted to buy or sell a piece of land. Brokers ensure that the land or property is registered with a legitimate government institution, where all the information is entered in a ledger, and that the full transaction is finalized after which, a sale is made between the two parties. Since anyone with the necessary access rights can readily view or change the papers in this situation, there is a chance that the important documents will be lost or tampered with, thereby endangering the tangible proof of ownership. The process is still largely physical with less digitization and as a result, this sort of system is slow, insecure, and unorganized. In this traditional method, the risk of corruption and fraud during the execution of the necessary transaction is also high. Hence, a new system has been proposed which uses smart contracts to handle transactions of assets among the participants in the network. It has been proposed to implement a permissioned blockchain-based land registration system on Hyperledger Fabric, which enables a decentralised, transparent, and secure way to conduct transactions between the parties. The old method has been examined and analysed while also considering that blockchain technology enables improved transparency, Integrity maintenance, and portability.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129461020","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

Comparative Approach for Early Diabetes Detection with Machine Learning 机器学习与早期糖尿病检测的比较方法

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100186

Shilpi Harnal, Arpit Jain, Anshika, Anurita Singh Rathore, Vidhu Baggan, Gagandeep Kaur, Rajni Bala

{"title":"Comparative Approach for Early Diabetes Detection with Machine Learning","authors":"Shilpi Harnal, Arpit Jain, Anshika, Anurita Singh Rathore, Vidhu Baggan, Gagandeep Kaur, Rajni Bala","doi":"10.1109/ESCI56872.2023.10100186","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10100186","url":null,"abstract":"The detrimental effects of diabetes are currently affecting a sizeable section of the population worldwide, and many of these individuals are not being properly diagnosed. This could eventually lead to significant health issues like kidney failure and vision blindness. Chances of heart attacks and strokes increase by two to three times due to diabetes. Thus, this work has considered a total of 520 instances with included 17 features such as polyuria, gender, age, sudden weight loss, polydipsia, polyphagia, weakness, irritability, genital thrush, itching, vision blurring, muscle stiffness, alopecia, delayed healing, delayed healing, and obesity to classify the type of diabetes at an early stage to avoid such risk. Various Machine Learning (ML) methods can be employed to accurately classify the disease. The objective of this research is to predict diabetes with the help of a variety of machine learning (ML) methods and to identify the most efficient model with the highest accuracy. A total 8 classification algorithms are used for the performance measurement, these are Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree Classifier (DTC), Logistic Regression (LR), Extra Tree Classifier (ETC), K-Nearest Neighbors (KNN), and XGBoost (XGB) because these models gave the highest accuracy for this dataset. After comparative analysis, the results present that Extra Tree Classifier (ETC) has the highest accuracy, i.e., 98.55%, and can be considered the best and efficient ML classification technique for diagnosing diabetes based on mentioned parameters.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"127 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132984248","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

Anomaly Detection on Drone Forensic Timeline with Sigma Rules 基于Sigma规则的无人机取证时间线异常检测

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100018

H. Studiawan, Ahmad Firdaus, B. Pratomo, T. Ahmad

引用次数: 0

Application of Cloud Computing in an Education Sector through Education and Learning as a Service and its Cost Benefit Analysis “教育与学习即服务”模式下云计算在教育领域的应用及其成本效益分析

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099586

K. S. Sagale, Mahadeo D. Kokate, R. Agrawal

引用次数: 0