INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems. OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted. METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud. RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies. CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.
{"title":"Blockchain based Quantum Resistant Signature Algorithm for Data Integrity Verification in Cloud and Internet of Everything","authors":"P. Shrivastava, Bashir Alam, Mansaf Alam","doi":"10.4108/eetsis.5488","DOIUrl":"https://doi.org/10.4108/eetsis.5488","url":null,"abstract":" \u0000INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems. \u0000OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted. \u0000METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud. \u0000RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies. \u0000CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225763","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}
INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.
{"title":"Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance","authors":"Yue Liu, Wangke Lin","doi":"10.4108/eetsis.5300","DOIUrl":"https://doi.org/10.4108/eetsis.5300","url":null,"abstract":"INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229172","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}
The Chat-Bot utilizes Sequence-to-Sequence Model with the Attention Mechanism, in order to interpret and address user inputs effectively. The whole model consists of Data gathering, Data preprocessing, Seq2seq Model, Training and Tuning. Data preprocessing involves cleaning of any irrelevant data, before converting them into the numerical format. The Seq2Seq Model is comprised of two components: an Encoder and a Decoder. Both Encoder and Decoder along with the Attention Mechanism allow dialogue management, which empowers the Model to answer the user in the most accurate and relevant manner. The output generated by the Bot is in the Natural Language only. Once the building of the Seq2Seq Model is completed, training of the model takes place in which the model is fed with the preprocessed data, during training it tries to minimize the loss function between the predicted output and the ground truth output. Performance is computed using metrics such as perplexity, BLEU score, and ROUGE score on a held-out validation set. In order to meet non-functional requirements, our system needs to maintain a response time of under one second with an accuracy target exceeding 90%.
{"title":"Evaluating Performance of Conversational Bot Using Seq2Seq Model and Attention Mechanism","authors":"Karandeep Saluja, Shashwat Agarwal, Sanjeev Kumar, Tanupriya Choudhury","doi":"10.4108/eetsis.5457","DOIUrl":"https://doi.org/10.4108/eetsis.5457","url":null,"abstract":"The Chat-Bot utilizes Sequence-to-Sequence Model with the Attention Mechanism, in order to interpret and address user inputs effectively. The whole model consists of Data gathering, Data preprocessing, Seq2seq Model, Training and Tuning. Data preprocessing involves cleaning of any irrelevant data, before converting them into the numerical format. The Seq2Seq Model is comprised of two components: an Encoder and a Decoder. Both Encoder and Decoder along with the Attention Mechanism allow dialogue management, which empowers the Model to answer the user in the most accurate and relevant manner. The output generated by the Bot is in the Natural Language only. Once the building of the Seq2Seq Model is completed, training of the model takes place in which the model is fed with the preprocessed data, during training it tries to minimize the loss function between the predicted output and the ground truth output. Performance is computed using metrics such as perplexity, BLEU score, and ROUGE score on a held-out validation set. In order to meet non-functional requirements, our system needs to maintain a response time of under one second with an accuracy target exceeding 90%.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"223 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233465","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}
INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge.OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence.METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements.RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program.CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence.
{"title":"Study on Evaluation of Execution Capability Based on Artificial Intelligence CIPP Model","authors":"Hui Dong","doi":"10.4108/eetsis.5234","DOIUrl":"https://doi.org/10.4108/eetsis.5234","url":null,"abstract":"INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge.OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence.METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements.RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program.CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"17 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240593","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}
INTRODUCTION: The Graph Coloring Problem (GCP) involves coloring the vertices of a graph in such a way that no two adjacent vertices share the same color while using the minimum number of colors possible. OBJECTIVES: The main objective of the study is While keeping the constraint that no two neighbouring vertices have the same colour, the goal is to reduce the number of colours needed to colour a graph's vertices. It further investigate how various techniques impact the execution time as the number of nodes in the graph increases. METHODS: In this paper, we propose a novel method of implementing a Genetic Algorithm (GA) to address the GCP. RESULTS: When the solution is implemented on a highly specified Google Cloud instance, we likewise see a significant increase in performance. The parallel execution on Google Cloud shows significantly faster execution times than both the serial implementation and the parallel execution on a local workstation. This exemplifies the benefits of cloud computing for computational heavy jobs like GCP. CONCLUSION: This study illustrates that a promising solution to the Graph Coloring Problem is provided by Genetic Algorithms. Although the GA-based approach does not provide an optimal result, it frequently produces excellent approximations in a reasonable length of time for a variety of real-world situations.
{"title":"A Solution to Graph Coloring Problem Using Genetic Algorithm","authors":"Karan Malhotra, Karan D. Vasa, Neha Chaudhary, Ankit Vishnoi, Varun Sapra","doi":"10.4108/eetsis.5437","DOIUrl":"https://doi.org/10.4108/eetsis.5437","url":null,"abstract":"INTRODUCTION: The Graph Coloring Problem (GCP) involves coloring the vertices of a graph in such a way that no two adjacent vertices share the same color while using the minimum number of colors possible. \u0000OBJECTIVES: The main objective of the study is While keeping the constraint that no two neighbouring vertices have the same colour, the goal is to reduce the number of colours needed to colour a graph's vertices. It further investigate how various techniques impact the execution time as the number of nodes in the graph increases. \u0000METHODS: In this paper, we propose a novel method of implementing a Genetic Algorithm (GA) to address the GCP. \u0000RESULTS: When the solution is implemented on a highly specified Google Cloud instance, we likewise see a significant increase in performance. The parallel execution on Google Cloud shows significantly faster execution times than both the serial implementation and the parallel execution on a local workstation. This exemplifies the benefits of cloud computing for computational heavy jobs like GCP. \u0000CONCLUSION: This study illustrates that a promising solution to the Graph Coloring Problem is provided by Genetic Algorithms. Although the GA-based approach does not provide an optimal result, it frequently produces excellent approximations in a reasonable length of time for a variety of real-world situations.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"2 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241321","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}
INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability.OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm.METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments.RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms.CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods.
{"title":"Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm","authors":"Lijie Ren, Hyunsuk Kim","doi":"10.4108/eetsis.5233","DOIUrl":"https://doi.org/10.4108/eetsis.5233","url":null,"abstract":"INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability.OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm.METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments.RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms.CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"13 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241639","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}
Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge to the longevity and performance of WSNs. To address this challenge, this paper presents an Optimized Energy Efficient Protocol in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics (OEE-WCRD). This research paper presents a novel approach to cluster head selection in WSNs by harnessing a combination of residual energy and distance metrics. The proposed method aims to significantly enhance the energy efficiency of WSNs by prioritizing nodes with ample residual energy and proximity to their neighbors as cluster heads. Through extensive simulations and evaluations, we demonstrate the effectiveness of this approach in prolonging network lifetime, optimizing data aggregation, and ultimately advancing the energy efficiency of WSNs, making it a valuable contribution to the field of WSNs protocols.
{"title":"OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics","authors":"Lalit Kumar Tyagi, Anoop Kumar","doi":"10.4108/eetsis.4268","DOIUrl":"https://doi.org/10.4108/eetsis.4268","url":null,"abstract":"Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge to the longevity and performance of WSNs. To address this challenge, this paper presents an Optimized Energy Efficient Protocol in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics (OEE-WCRD). This research paper presents a novel approach to cluster head selection in WSNs by harnessing a combination of residual energy and distance metrics. The proposed method aims to significantly enhance the energy efficiency of WSNs by prioritizing nodes with ample residual energy and proximity to their neighbors as cluster heads. Through extensive simulations and evaluations, we demonstrate the effectiveness of this approach in prolonging network lifetime, optimizing data aggregation, and ultimately advancing the energy efficiency of WSNs, making it a valuable contribution to the field of WSNs protocols.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"25 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262953","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}
P. S. Metkewar, Shrivats Sharma, Lubna Hamid Shah, A. Prasanth
When it comes to international trade, India is one of the most important nations. This paper intends to analyze the effect of International Trade on a nation’s GDP growth through the process of visualizing the current trends. For this research, some statistical (economical) data is considered and its effect on the GDP is analyzed for the previous accounting years ranging from 2015 to 2021. The data considered for this include – the monetary value of exports from India (in US$ Millions), the monetary value of imports from India (in US$ Millions), India’s share of exports to the nation out of all the nations, India’s share of imports to the nation out of all the nations in, export growth rate, import growth rate, currency exchange rate, Inflation rate. This paper examines and explains how these economic factors influence a country’s (India’s) GDP growth through the process of visualization.
在国际贸易方面，印度是最重要的国家之一。本文旨在通过对当前趋势的可视化过程，分析国际贸易对国家 GDP 增长的影响。本研究考虑了一些统计（经济）数据，并分析了从 2015 年到 2021 年的前几个会计年度的数据对国内生产总值的影响。考虑的数据包括--印度出口的货币价值（单位：百万美元）、从印度进口的货币价值（单位：百万美元）、印度在所有国家中的出口份额、印度在所有国家中的进口份额、出口增长率、进口增长率、货币汇率、通货膨胀率。本文通过可视化过程研究并解释了这些经济因素如何影响一个国家（印度）的国内生产总值增长。
{"title":"Visualization Process of International Trade and its impact on GDP through Multi-criteria Decision Model: A Case Study of India’s Merchandise Trade","authors":"P. S. Metkewar, Shrivats Sharma, Lubna Hamid Shah, A. Prasanth","doi":"10.4108/eetsis.5296","DOIUrl":"https://doi.org/10.4108/eetsis.5296","url":null,"abstract":"When it comes to international trade, India is one of the most important nations. This paper intends to analyze the effect of International Trade on a nation’s GDP growth through the process of visualizing the current trends. For this research, some statistical (economical) data is considered and its effect on the GDP is analyzed for the previous accounting years ranging from 2015 to 2021. The data considered for this include – the monetary value of exports from India (in US$ Millions), the monetary value of imports from India (in US$ Millions), India’s share of exports to the nation out of all the nations, India’s share of imports to the nation out of all the nations in, export growth rate, import growth rate, currency exchange rate, Inflation rate. This paper examines and explains how these economic factors influence a country’s (India’s) GDP growth through the process of visualization.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266303","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}
Mageshkumar N, Vijayaraj A, S. Chavva, Gururama Senthilvel
INTRODUCTION: The telecommunications industry faces significant challenges due to customer attrition, which directly impacts revenue. To better understand and address this issue, Companies are looking into techniques to determine the internal issues that affect customer churn. OBJECTIVES: This article offers an overview of customer attrition within the telecommunications sector. METHODS: It introduces an advanced churn prediction model harnessing state-of-the-art technologies, including neural networks, machine learning, and other cutting-edge innovations, to achieve remarkably high accuracy rates. By analyzing diverse parameters and datasets collected from multiple telecom companies, valuable insights can be gained. RESULTS: The model's performance on test data can be evaluated using metrics such as Accuracy Score, Area under Curve (AUC), Sensitivity, Specificity, and other performance indicators. CONCLUSION: In order to effectively manage extensive datasets, organizations can leverage Big Data technology. This empowers them to forecast the probability of customer churn and put in place proactive strategies to retain their customer base.
{"title":"Prediction of User Attrition in Telecommunication Using Neural Network","authors":"Mageshkumar N, Vijayaraj A, S. Chavva, Gururama Senthilvel","doi":"10.4108/eetsis.5242","DOIUrl":"https://doi.org/10.4108/eetsis.5242","url":null,"abstract":"INTRODUCTION: The telecommunications industry faces significant challenges due to customer attrition, which directly impacts revenue. To better understand and address this issue, Companies are looking into techniques to determine the internal issues that affect customer churn. \u0000OBJECTIVES: This article offers an overview of customer attrition within the telecommunications sector. \u0000METHODS: It introduces an advanced churn prediction model harnessing state-of-the-art technologies, including neural networks, machine learning, and other cutting-edge innovations, to achieve remarkably high accuracy rates. By analyzing diverse parameters and datasets collected from multiple telecom companies, valuable insights can be gained. \u0000RESULTS: The model's performance on test data can be evaluated using metrics such as Accuracy Score, Area under Curve (AUC), Sensitivity, Specificity, and other performance indicators. \u0000CONCLUSION: In order to effectively manage extensive datasets, organizations can leverage Big Data technology. This empowers them to forecast the probability of customer churn and put in place proactive strategies to retain their customer base.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"24 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140408906","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}
Pranjali, Srividya Ramisetty, Vani B Telagade, S. D. Adiga
As technology continues to advance, data has become an increasingly important element in the sphere of Information Technology. However, enormous data generated by devices presents a major challenge in handling it in real time. Data encryption is a crucial component in ensuring data security and privacy during its transmission in network. Unfortunately, many applications disregard data encryption in order to achieve higher performance. The work proposes a solution to this problem by introducing a data encryption process that is, the Realizable Data Encryption Strategy (RDES) and Deoxyribonucleic Acid (DNA) computing, a revolutionary cryptographic method that improves information security by preventing authorized access to sensitive data, being used. Information security is improved by DNA symmetric cryptography being suggested. The outcomes show that plain-text encryption is a very secure procedure. The RDES approach is designed to improve privacy protection within the constraints of real-time processing. By implementing the RDES approach, data privacy and security can be significantly enhanced without compromising performance.
{"title":"A Realizable Data Encryption Strategy","authors":"Pranjali, Srividya Ramisetty, Vani B Telagade, S. D. Adiga","doi":"10.4108/eetsis.5230","DOIUrl":"https://doi.org/10.4108/eetsis.5230","url":null,"abstract":"As technology continues to advance, data has become an increasingly important element in the sphere of Information Technology. However, enormous data generated by devices presents a major challenge in handling it in real time. Data encryption is a crucial component in ensuring data security and privacy during its transmission in network. Unfortunately, many applications disregard data encryption in order to achieve higher performance. The work proposes a solution to this problem by introducing a data encryption process that is, the Realizable Data Encryption Strategy (RDES) and Deoxyribonucleic Acid (DNA) computing, a revolutionary cryptographic method that improves information security by preventing authorized access to sensitive data, being used. Information security is improved by DNA symmetric cryptography being suggested. The outcomes show that plain-text encryption is a very secure procedure. The RDES approach is designed to improve privacy protection within the constraints of real-time processing. By implementing the RDES approach, data privacy and security can be significantly enhanced without compromising performance.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423191","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：