2021 Emerging Trends in Industry 4.0 (ETI 4.0)最新文献_第7页

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2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/eti4.051663.2021.9619396

引用次数: 0

Energy Conservation Implementation at Cable Manufacturing Industry through ETAP Analysis 基于ETAP分析的电缆制造业节能实施

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619231

A. Shanmuga Sundaram Devi, Awanish Kumar, N. Thanigivelu, L. Ramesh, K. Sundaram

引用次数: 0

Logistic Regression Model for Loan Prediction: A Machine Learning Approach 贷款预测的逻辑回归模型:一种机器学习方法

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619201

Richa Manglani, Anuja Bokhare

{"title":"Logistic Regression Model for Loan Prediction: A Machine Learning Approach","authors":"Richa Manglani, Anuja Bokhare","doi":"10.1109/ETI4.051663.2021.9619201","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619201","url":null,"abstract":"With the advance in the banking space, many individual’s area unit putting up for loans however the banks have its own restricted resources that it must permit to restricted people simply, therefore discovering to whom the advance is conceded which will be a more secure choice for the bank is a normal interaction. Therefore in this study, an attempt to reduce this risk issue behind selecting the protected individual to avoid wasting different bank endeavors and resources. This can be finished by extracting the info of the records of people to whom the credit was conceded antecedently and supported. These records/encounters the machine was ready to utilize the AI model which provides the foremost precise outcome. The main goal of this study to anticipate whether or not delegating the loan to a selected individual are protected or not. During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank faces a minimum of once in its period. If done effectively, it will save loads of manhours at the top of a retail bank.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114748869","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

Detection of Pneumonia Using Deep Learning 利用深度学习检测肺炎

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619218

Pratiksha R. Shetgaonkar, Shrameet Nayak, S. Aswale, Saurabh Vernekar, Ashitosh Tilve, Dhanashri Turi

引用次数: 0

Using XG Boost and Random Forest Classifier Algorithms to Predict Student Behavior 使用XG Boost和随机森林分类器算法预测学生行为

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619217

Ha Thi The Nguyen, Ling-Hsiu Chen, Vani Suthamathi Saravanarajan, H. Pham

引用次数: 1

Efficient Tree Multiplier Design by using Modulo 2n + 1 Adder 基于模2n + 1加法器的高效树乘法器设计

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619220

B. K. Patel, J. Kanungo

引用次数: 0

Improving Smart Contract Transaction Performance in Hyperledger Fabric 提高超级账本结构中的智能合约交易性能

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619202

Swapnil Sinha, S. Anand, K. K

引用次数: 0

Application Workload Characterization using BAT_LSTM Learning algorithm for Asymmetric Architectures 基于非对称架构的BAT_LSTM学习算法的应用负载表征

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619290

Jayanthi E, V. R

{"title":"Application Workload Characterization using BAT_LSTM Learning algorithm for Asymmetric Architectures","authors":"Jayanthi E, V. R","doi":"10.1109/ETI4.051663.2021.9619290","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619290","url":null,"abstract":"Nowadays, asymmetric multicore architectures become everywhere due to its energy efficiency, QoS, and high performance. Though workload characterization on these architectures become a challenging task due to its heterogeneous pipeline structure and execution process that affects the overall performance of the system. To resolve this issue, BAT_LSTM deep learning predictor has been designed and developed to predict appropriate resource for each workload at runtime. Deep learning algorithms are adopted in several applications such as computer vision, smart vehicles, and medical environment in order to classify and predict the unknown. In this work, BAT_LSTM neural network predictor has been designed and compared with random forest algorithms, decision tree, naive bayes and support vector machine for workload characterization. Cost functions of these algorithms are designed and developed in order to detect the optimal processor for each workload execution at runtime. Core mark workloads are initially executed on quad core multicore hardware to analyze the workload characteristics in terms of memory consumption, I/O, CPU usage, instructions type, cache miss ratios and so on. These characteristics are feed forwarded into machine a learning algorithm that identifies the best processor. Performance of proposed algorithms is evaluated using testing workloads in terms of processor prediction accuracy, execution time metrics. Average of 10% in energy consumption reduction and 96.8% in accuracy is achieved through proposed predictors.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116110138","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

Analysis of Online Education System of Bangladesh during COVID-19 Pandemic Based on NLP and Machine Learning: Problem and Prospect 基于NLP和机器学习的新冠疫情期间孟加拉国在线教育系统分析:问题与展望

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619312

Subhra Palit, Shafayet Nur, Zulikha Khatun, Maqsudur Rahman, M. T. Ahmed

{"title":"Analysis of Online Education System of Bangladesh during COVID-19 Pandemic Based on NLP and Machine Learning: Problem and Prospect","authors":"Subhra Palit, Shafayet Nur, Zulikha Khatun, Maqsudur Rahman, M. T. Ahmed","doi":"10.1109/ETI4.051663.2021.9619312","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619312","url":null,"abstract":"The Coronavirus Disease (COVID-19) pandemic has interrupted the education system throughout the world. Bangladesh is no unlike; all educational institutions are shut down across the country. The online teaching method is quite new especially for the developing countries like Bangladesh. Therefore, the main aim of this work is to mine student’s opinions about online class during this COVID-19 pandemic. To achieve this aim, this paper uses a questionnaire survey through the google form to collect Bangladeshi student’s opinion on online class, build a corpus of 5005 data containing both Bangla and Romanized Bangla text. After data pre-processing and extracting the features, machine learning classifiers were deployed. Then performance measurement was done in terms of accuracy, precision, recall and F1 score. In the final evaluation, we achieved highest of 80% accuracy with SVM classifier, where the accuracy achieved by Logistic Regression, Random Forest and Multinomial Naïve Bayes classifier was 78%, 77% and 77% respectively. We tried to predictthe problems faced by students and suggested possible solutions about online class. The result showed that 27.9% student faced financial problem and 25.8% student faced unstable internet problem. 54.8% user suggested stable internet facility in low cost or free and 23.1% suggested financial assistance for online class as the possible solution of aforementioned problems.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"78 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116308655","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

A Review on Various DC-DC Converters for Photo Voltaic Based DC Micro Grids 基于光伏的直流微电网的各种DC-DC变换器综述

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619280

B. Jyothi, P. Bhavana, B. Rao, M. K. Reddy

{"title":"A Review on Various DC-DC Converters for Photo Voltaic Based DC Micro Grids","authors":"B. Jyothi, P. Bhavana, B. Rao, M. K. Reddy","doi":"10.1109/ETI4.051663.2021.9619280","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619280","url":null,"abstract":"Currently the usage of power demand is increased by utility grids. In order to fulfilled the energy demand, tackle ecological concerns producing from the conservative energy sources, then researchers are moving towards the non- conservative (or) renewable energy resources integrated with DC micro grids to deliver power supply with improving system efficiency and especially cost reduction in distribution systems in a superior way. In non-conservative sources, solar energy is the cleanest and abundant green energy source available in nature. So, implementation of solar PV based DC micro grids technology is inexpensive, flexible, and energy-efficient to the end-users. But solar PV panels generate a low DC voltage. By using this low DC voltage as an input to the DC micro grids, definitely this grid does not serve any dc load properly. So, this problem can be erected with the help of DC-DC converters. The main motto of DC-DC converters is to properly produce the output voltage and ripple free output current to the dc load requirements. This paper gives the information regarding the various DC-DC converters applicable for solar PV based DC micro grids and at most, enlists the proposed MSC (Modified SEPIC Converter) is a best DC-DC converter for solar PV based DC micro grids based on the literature review discussed.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772069","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: 3