Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150782
Rupali Aggarwal, P. Sarangi, A. Sahoo
In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.
{"title":"Credit Card Fraud Detection: Analyzing the Performance of Four Machine Learning Models","authors":"Rupali Aggarwal, P. Sarangi, A. Sahoo","doi":"10.1109/ICDT57929.2023.10150782","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150782","url":null,"abstract":"In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"59 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":"132568851","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151042
D. Praveenadevi, S. Rekha, B. Girimurugan, K. J. Narendra Kumar, B. Hemanjali, B. Lalitvamsi
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.
{"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}
Cities and transportation have expanded together since the earliest significant human settlements. The same factors that tempt people to reside in densely populated areas also fuel the frequently atrocious levels of traffic congestion on city streets. Since the widespread use of vehicles, one of the primary issues modern cities confront is traffic congestion. A quick journey to the convenience store might take up to 30 minutes due to slowness or traffic congestion. Road rage, road bullies, and serious accidents are caused by traffic congestion. To overcome these challenges, we will be creating an app that will allow users to register their concerns so that assistance may be sent as quickly as possible in order to make the traffic management system and commuters' lives more convenient.
{"title":"Smart Tracking System for Traffic using Android based Application","authors":"Abhishek Goyal, Aakriti Singh, Aditi Dubey, Anurag Shukla","doi":"10.1109/ICDT57929.2023.10150852","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150852","url":null,"abstract":"Cities and transportation have expanded together since the earliest significant human settlements. The same factors that tempt people to reside in densely populated areas also fuel the frequently atrocious levels of traffic congestion on city streets. Since the widespread use of vehicles, one of the primary issues modern cities confront is traffic congestion. A quick journey to the convenience store might take up to 30 minutes due to slowness or traffic congestion. Road rage, road bullies, and serious accidents are caused by traffic congestion. To overcome these challenges, we will be creating an app that will allow users to register their concerns so that assistance may be sent as quickly as possible in order to make the traffic management system and commuters' lives more convenient.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"53 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":"124699331","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}
Conversely, cutting-edge innovations, such as the Internet of Things (IoT), virtual reality (VR), artificial intelligence (AI), and 5G wireless connectivity techniques, are indeed being created to address these difficulties in order to increase the patient outcomes and quality healthcare efficiency while lowering total medical costs. It’s not an impossible ideal, since new technologies are already influencing and reconstructing healthcare in insidious ways. Even though the capabilities described above are linked, this study will focus on situations involving the use of 5G wireless connectivity in healthcare settings to transmute a healthiness insurance arrangement that is fading to deal with the weight of modern illnesses and the problem of scale - up towards cumulative inhabitants. We further outline possible roadblocks to the deployment of 5G technology.
{"title":"5G Intrusion for Monitoring Healthcare Services","authors":"Diwan Singh Rawat, Deepti Sharma, Samta Kathuria, Angel Swastik Duggal, Rajesh Singh, Manish Gupta","doi":"10.1109/ICDT57929.2023.10151053","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151053","url":null,"abstract":"Conversely, cutting-edge innovations, such as the Internet of Things (IoT), virtual reality (VR), artificial intelligence (AI), and 5G wireless connectivity techniques, are indeed being created to address these difficulties in order to increase the patient outcomes and quality healthcare efficiency while lowering total medical costs. It’s not an impossible ideal, since new technologies are already influencing and reconstructing healthcare in insidious ways. Even though the capabilities described above are linked, this study will focus on situations involving the use of 5G wireless connectivity in healthcare settings to transmute a healthiness insurance arrangement that is fading to deal with the weight of modern illnesses and the problem of scale - up towards cumulative inhabitants. We further outline possible roadblocks to the deployment of 5G technology.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"18 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":"125219639","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150453
Yash Gupta, Frankly Chauhan, Kanika Singla
Numerous asphalt pavement faults are the major contributor to auto accidents, necessitating corrective action because they put people in grave danger. As a result, there are many algorithms used to detect those road damages so that no further accidents occur in the future. A model is proposed which consist of Convolutional Neural Network and ResNet algorithm to find the accuracy in both sections. First, the training dataset is collected from the RDD2020 dataset, which consists of 7000 images of three different countries then labeling of those images, is done in different categories of cracks like longitudinal, alligator, potholes, and traverse cracks. Furthermore, we implement CNN and ResNet architecture to analyze the accuracy and use a better algorithm to detect road damage in the future. After applying the CNN and ResNet-34, 94.79% and 89.94% accuracies are obtained as an outcome.
{"title":"Analysis of Different Deep Learning Algorithms for Road Surface Damage Detection","authors":"Yash Gupta, Frankly Chauhan, Kanika Singla","doi":"10.1109/ICDT57929.2023.10150453","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150453","url":null,"abstract":"Numerous asphalt pavement faults are the major contributor to auto accidents, necessitating corrective action because they put people in grave danger. As a result, there are many algorithms used to detect those road damages so that no further accidents occur in the future. A model is proposed which consist of Convolutional Neural Network and ResNet algorithm to find the accuracy in both sections. First, the training dataset is collected from the RDD2020 dataset, which consists of 7000 images of three different countries then labeling of those images, is done in different categories of cracks like longitudinal, alligator, potholes, and traverse cracks. Furthermore, we implement CNN and ResNet architecture to analyze the accuracy and use a better algorithm to detect road damage in the future. After applying the CNN and ResNet-34, 94.79% and 89.94% accuracies are obtained as an outcome.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"33 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":"131728308","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150967
Venkateswararao. Podile, Anuradha Averineni, Dhanush Kethineni, Darapaneni Brahma Naidu, Bezawada Venkata Naga Sai Vignesh, M. R. Krishna Reddy
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.
{"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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151083
Aishwarya Prakash, S. Chauhan
Automated feature learning is now possible in various fields, including healthcare, image recognition, and, more recently, feature extraction and classification of simple and complex human activity detection in mobile and wearable sensors, thanks to advances in deep learning and increased computing capabilities. A significant advancement in artificial intelligence has been made as a result of deep learning and cloud technology integration. As a result of cloud computing, organisations now have access to the necessary resources to develop and implement deep learning solutions. Although it is becoming increasingly common in cloud infrastructures, there is limited research on it. This study aims to provide a comprehensive overview of deep learning and discusses the methodologies, their uniqueness, benefits, and limits. Finally, we define and discuss certain open research difficulties that demand more investigation and improvements.
{"title":"A Comprehensive Survey of Trending Tools and Techniques in Deep Learning","authors":"Aishwarya Prakash, S. Chauhan","doi":"10.1109/ICDT57929.2023.10151083","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151083","url":null,"abstract":"Automated feature learning is now possible in various fields, including healthcare, image recognition, and, more recently, feature extraction and classification of simple and complex human activity detection in mobile and wearable sensors, thanks to advances in deep learning and increased computing capabilities. A significant advancement in artificial intelligence has been made as a result of deep learning and cloud technology integration. As a result of cloud computing, organisations now have access to the necessary resources to develop and implement deep learning solutions. Although it is becoming increasingly common in cloud infrastructures, there is limited research on it. This study aims to provide a comprehensive overview of deep learning and discusses the methodologies, their uniqueness, benefits, and limits. Finally, we define and discuss certain open research difficulties that demand more investigation and improvements.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"18 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":"133384504","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150552
Mohd. Altamash, Shailendra Narayan Singh
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.
使用GUI的软件测试基本上包含了不同的测试工具，可以是自动的，也可以是手动的测试工具。市场上已经有很多工具了，所以我们为什么还要再做一个呢?在当前的场景中，软件测试生命周期(STLC)和软件开发生命周期(SDLC)是测试过程中必不可少的参数。Selenium支持Java, C]， Ruby, Python, Perl, PHP和JavaScript等编程语言，QTP仅支持VB脚本。在一个可以被任何编程语言中断的应用程序上工作，甚至应用程序都应该有批处理，这样就可以一次测试多个测试用例。本研究论文背后的主要动机是对一些先进的测试自动化工具进行相关研究，例如Selenium (Open Source Free)和Quick test Professional (QTP)，并对这两个自动化软件测试工具进行评估和分析，以确定它们的操作简单性、易用性、使用范围和有效性。为了解决输出延迟的问题，使其具有鲁棒性，速度更快，精度高的阈值单元。
{"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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150878
Rinku Garg, A. Sandhu, Bobbinpreet Kaur
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.
{"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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151338
D. Praveenadevi, S. Sreekala, B. Girimurugan, K. V. R. Krishna Teja, G. Naga Kamal, Asturi Chetan Chandra
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.
{"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}
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