2023 International Conference on Disruptive Technologies (ICDT)最新文献_第4页

Credit Card Fraud Detection: Analyzing the Performance of Four Machine Learning Models 信用卡欺诈检测:分析四种机器学习模型的性能

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150782

Rupali Aggarwal, P. Sarangi, A. Sahoo

引用次数: 0

Impact of Digitalization on Sustainable Supply Chain Management 数字化对可持续供应链管理的影响

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151042

D. Praveenadevi, S. Rekha, B. Girimurugan, K. J. Narendra Kumar, B. Hemanjali, B. Lalitvamsi

{"title":"Impact of Digitalization on Sustainable Supply Chain Management","authors":"D. Praveenadevi, S. Rekha, B. Girimurugan, K. J. Narendra Kumar, B. Hemanjali, B. Lalitvamsi","doi":"10.1109/ICDT57929.2023.10151042","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151042","url":null,"abstract":"There are only so many resources available; it is essential to put into action strategies that will lead to sustainable growth if one wishes to ensure their continued success over the long run. Despite this, a significant number of scholars have investigated the prospect that digital technologies may be able to increase sustainable performance in this age of digitalization and globalization. This research maintains collaboration and coordination in a digitally connected supply chain (SC) could contribute to sustainability is still in its early phases, and there is still a long way to go before it can be considered complete. Using SC, it is possible to cut down on the amount of energy that is consumed, cut down on the amount of time that is spent traveling, and make better use of the assets that are employed in logistics. Case studies conducted with a variety of manufacturers form the basis of this investigation and will serve as its primary focus. Researchers nevertheless give equal weight to the social and environmental sustainability components, even though the majority of studies in this subject concentrate on the financial aspect of the topic. The research concluded that incorporating SC into logistics and supply chain management led to a moderate improvement in terms of both environmental and social sustainability.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133348028","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

Smart Tracking System for Traffic using Android based Application 基于Android的交通智能跟踪系统应用

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150852

Abhishek Goyal, Aakriti Singh, Aditi Dubey, Anurag Shukla

引用次数: 0

5G Intrusion for Monitoring Healthcare Services 监控医疗保健服务的5G入侵

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151053

Diwan Singh Rawat, Deepti Sharma, Samta Kathuria, Angel Swastik Duggal, Rajesh Singh, Manish Gupta

引用次数: 1

Analysis of Different Deep Learning Algorithms for Road Surface Damage Detection 路面损伤检测中不同深度学习算法的分析

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150453

Yash Gupta, Frankly Chauhan, Kanika Singla

引用次数: 0

An Enhanced Ensemble Machine Learning Methods in Financial Marketing 金融营销中一种增强的集成机器学习方法

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150967

Venkateswararao. Podile, Anuradha Averineni, Dhanush Kethineni, Darapaneni Brahma Naidu, Bezawada Venkata Naga Sai Vignesh, M. R. Krishna Reddy

{"title":"An Enhanced Ensemble Machine Learning Methods in Financial Marketing","authors":"Venkateswararao. Podile, Anuradha Averineni, Dhanush Kethineni, Darapaneni Brahma Naidu, Bezawada Venkata Naga Sai Vignesh, M. R. Krishna Reddy","doi":"10.1109/ICDT57929.2023.10150967","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150967","url":null,"abstract":"In recent years, financial institutions (FIs) have been hesitant when it comes to using supply chain finance (SCF), which is short for supply chain financing. This is because SCF stands for supply chain financing, which is used to address the financing needs of small and medium-sized businesses. One of the most difficult and time-consuming tasks in the industry of financial planning is currently the assessment of the credit risk that is posed by small and medium-sized enterprises (SME). On the other hand, the requirements of such forecasting are not something that can be provided by employing conventional models of credit risk. This article uses a stacking model, which takes into account both technical aspects and macroeconomic data, in order to make predictions regarding the movement of the stock price index in reference to the price that was in effect not too long ago. A recursive application of the cross-validation procedure is carried out in order to produce the input for the second-level classifier. This is done to mitigate the risk of the model being overly constrained by the data. Logistic regression and its regularized version are used as meta-classifiers in the second layer to the fundamental classifier to class learning. The outcome of our research is an exhaustive stacking architecture that has the potential to be applied in the banking sector.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131291432","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 Comprehensive Survey of Trending Tools and Techniques in Deep Learning 深度学习趋势工具和技术的综合调查

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151083

Aishwarya Prakash, S. Chauhan

引用次数: 0

Review on Software Testing using GUI based on QTP 基于QTP的GUI软件测试综述

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150552

Mohd. Altamash, Shailendra Narayan Singh

{"title":"Review on Software Testing using GUI based on QTP","authors":"Mohd. Altamash, Shailendra Narayan Singh","doi":"10.1109/ICDT57929.2023.10150552","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150552","url":null,"abstract":"Software Testing using GUI is basically containing different testing tools which can be automated or manual testing tool. Lots of tools are already available in the market so why we need to make another. In the current scenario, Software Testing lifecycle (STLC) and Software Development lifecycle (SDLC) is an essential parameter in process of testing. Selenium supports programming languages like Java, C], Ruby, Python, Perl, PHP & JavaScript and QTP has Programming language support only for VB script. As working on an application which can interrupt with any programming language and even application should have batch processing so that multiple test cases can be test at one go. The primary motivation behind this research paper is to lead a relative investigation of a few advanced test automation, tools, for example, Selenium (Open Source Free) and Quick Test Professional (QTP), and to assess and analyze these two automated software testing tools to decide their simplicity of operation, ease of use, region of utilization and effectiveness. To resolve the problem of delayed output make it Robust, faster one with high accuracy having threshold unit.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557236","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 Systematic Analysis of Various Techniques for Mango Leaf Disease Detection 芒果叶病各种检测技术的系统分析

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150878

Rinku Garg, A. Sandhu, Bobbinpreet Kaur

{"title":"A Systematic Analysis of Various Techniques for Mango Leaf Disease Detection","authors":"Rinku Garg, A. Sandhu, Bobbinpreet Kaur","doi":"10.1109/ICDT57929.2023.10150878","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150878","url":null,"abstract":"Monitoring plant illnesses was just by vision, is insufficient for recognizing plant diseases. The leaf changes color, revealing blotches such as yellow dots, black spots, or chocolate brown patches, as a result of the symptoms. Diseases like Anthracnose, Powdery Mildew, and Sooty Mold can be found on some leaves. To diagnose the disease, manual observation and pathogen detection are used, which takes longer and costs more money and gives less precision results. Therefore, a superior option to fast and precise identification through image processing techniques can be used, which can be more dependable than some other old traditional ways. Fruit, leaves, stems, and lesions are examples of plant components that may exhibit symptoms. The goal is to accurately find and diagnose the disease based on the leaf photos. Image preprocessing, segmentation, feature extraction, and classification are all necessary phases in the process. This paper will go through how to recognize mango leaf disease. Leaf characteristics such as their axis, including main and minor axes, are acquired, and diagnosed using various classification methods for illness diagnosis.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067744","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: 4

An Enhanced Method on Using Deep Learning Techniques in Supply Chain Management 深度学习技术在供应链管理中的应用

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151338

D. Praveenadevi, S. Sreekala, B. Girimurugan, K. V. R. Krishna Teja, G. Naga Kamal, Asturi Chetan Chandra

{"title":"An Enhanced Method on Using Deep Learning Techniques in Supply Chain Management","authors":"D. Praveenadevi, S. Sreekala, B. Girimurugan, K. V. R. Krishna Teja, G. Naga Kamal, Asturi Chetan Chandra","doi":"10.1109/ICDT57929.2023.10151338","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151338","url":null,"abstract":"One of the most significant issues that supply networks are currently facing is accurately estimating the level of demand for their products. Along with improving stock management and reducing overhead costs, some of the goals of the plan included growing sales, earnings, and customer base. The evaluation of historical data with the purpose of improving demand forecasting can be accomplished with the assistance of several different methods, some of which include methodologies based on machine learning, time series analysis, and deep learning models. This can be done to improve the accuracy of demand forecasting. The purpose of this investigation is to design an insightful strategy for forecasting future demand. In this paper, we develop an enhanced model to support the supply chain management and it uses a deep learning model to improve the process of supply chain management. The deep learning model is trained, tested and validated to improve the process of supplying the products via supply chain. The simulation is carried out in python for a set of objects that to be tracked and the results show that the model achieves higher accuracy of sending the products.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631144","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