Pub Date : 2023-04-13DOI: 10.37256/ccds.4220232277
Florian Klocker, Reinhard Bernsteiner, Christian Ploder, Martin Nocker
Market competition leads to shorter cycle times for new or updated products. Therefore, flexibility in reacting to market changes, product development, and all related processes must be accelerated. In this regard, accurate cost estimation in the early stages of product development is critical for assessing the economic viability of a product. However, cost estimation requires data and expertise from several departments. Machine learning approaches could improve the accuracy and reduce the time needed for cost estimation. To investigate the eligibility of machine learning based cost estimation, a case study was conducted on an industrial company that produces plastic molding parts as key components of its products. The study involved training various supervised machine learning algorithms on a dataset of plastic injection molding parts using three different cost calculation methods. The three methods differed in the extent to which they considered the different process steps involved in the production of the parts. Different tree-based machine learning regression models and neural network models were trained to identify the most suitable approach for cost estimation in the given context. The results showed that tree-based machine learning algorithms outperformed neural networks and that individually predicting manufacturing parameters for cost calculation of each manufacturing process step leads to the most accurate cost estimation. This paper demonstrates how machine learning can support cost estimation in the early stages of the product lifecycle, reducing development times and improving cost estimation accuracy.
{"title":"A Machine Learning Approach for Automated Cost Estimation of Plastic Injection Molding Parts","authors":"Florian Klocker, Reinhard Bernsteiner, Christian Ploder, Martin Nocker","doi":"10.37256/ccds.4220232277","DOIUrl":"https://doi.org/10.37256/ccds.4220232277","url":null,"abstract":"Market competition leads to shorter cycle times for new or updated products. Therefore, flexibility in reacting to market changes, product development, and all related processes must be accelerated. In this regard, accurate cost estimation in the early stages of product development is critical for assessing the economic viability of a product. However, cost estimation requires data and expertise from several departments. Machine learning approaches could improve the accuracy and reduce the time needed for cost estimation. To investigate the eligibility of machine learning based cost estimation, a case study was conducted on an industrial company that produces plastic molding parts as key components of its products. The study involved training various supervised machine learning algorithms on a dataset of plastic injection molding parts using three different cost calculation methods. The three methods differed in the extent to which they considered the different process steps involved in the production of the parts. Different tree-based machine learning regression models and neural network models were trained to identify the most suitable approach for cost estimation in the given context. The results showed that tree-based machine learning algorithms outperformed neural networks and that individually predicting manufacturing parameters for cost calculation of each manufacturing process step leads to the most accurate cost estimation. This paper demonstrates how machine learning can support cost estimation in the early stages of the product lifecycle, reducing development times and improving cost estimation accuracy.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"7 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120858785","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 : 2023-01-14DOI: 10.37256/ccds.4120232110
Y. Li, Keyue Yan
Option pricing has become a popular topic in the fields of finance and mathematics with the rapid development of stock and option markets. Now, more and more academics, financial companies and investors are attracted to study and do research about it. The theory of option pricing can also be used to price financial instruments with the similar structure to options and contribute to risk control and management. The Black-Scholes model is the basic and famous method applied for different options pricing with modifications and adjustments, and the results can be solved by some traditional numerical methods such as the binomial model, finite difference method, Monte Carlo method and so on. Machine learning has risen recently and begins to replace some complex work in traditional methods with the evolution of computers and computing power. How to use machine learning methods to predict the option price is a problem worthy to be solved. In this research, using the antithetic Monte Carlo method generates the prices of the up-and-out barrier options without rebate based on the Black-Scholes model. The generated dataset is divided into a training set and a test set for support vector regression, random forest, adaptive boosting and artificial neural networks. We compare the fitting and performance of all machine learning methods and find that random forest and artificial neural network methods fit better than others with fewer errors in predictions.
{"title":"Prediction of Barrier Option Price Based on Antithetic Monte Carlo and Machine Learning Methods","authors":"Y. Li, Keyue Yan","doi":"10.37256/ccds.4120232110","DOIUrl":"https://doi.org/10.37256/ccds.4120232110","url":null,"abstract":"Option pricing has become a popular topic in the fields of finance and mathematics with the rapid development of stock and option markets. Now, more and more academics, financial companies and investors are attracted to study and do research about it. The theory of option pricing can also be used to price financial instruments with the similar structure to options and contribute to risk control and management. The Black-Scholes model is the basic and famous method applied for different options pricing with modifications and adjustments, and the results can be solved by some traditional numerical methods such as the binomial model, finite difference method, Monte Carlo method and so on. Machine learning has risen recently and begins to replace some complex work in traditional methods with the evolution of computers and computing power. How to use machine learning methods to predict the option price is a problem worthy to be solved. In this research, using the antithetic Monte Carlo method generates the prices of the up-and-out barrier options without rebate based on the Black-Scholes model. The generated dataset is divided into a training set and a test set for support vector regression, random forest, adaptive boosting and artificial neural networks. We compare the fitting and performance of all machine learning methods and find that random forest and artificial neural network methods fit better than others with fewer errors in predictions.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128909045","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 : 2022-12-22DOI: 10.37256/ccds.4320232095
Davidson Egirani, Ginikanwa Chidi
The source of high fever and gastrointestinal disorders in humans after groundwater consumption in this part of the delta region in Nigeria is unknown. Spatial data engineered by GIS interpretation of organo-contaminants bound to groundwater and borehole sediment provides baseline data and information on the impact of iron and organo-contaminants on groundwater quality in Aluu. A total of 10 water and sediment samples were collected at a depth of 45 m from 10 boreholes within Aluu and analyzed in triplicate. The choice of 45 m implies the occurrence of sediments bearing groundwater for a deep well. A particle size analyzer was used to perform particle size analyses of the air-dried sediments. The American Public Health Association Method (APHA) was used to perform the chemical analysis of the water samples. Here, a liquid-liquid extraction procedure was conducted on the samples using 30 mL dichloromethane (DCM) as the extraction agent. The results were subjected to statistical validation, spatial data and GIS analysis. The textural characteristics possessed a mean grain size from fine sand (2.03) to medium sand (4.3), poorly sorted of 1.45 to 2.1, skewness of near-symmetrical (0.02), meso-kurtic kurtosis of 0.5 to very platy-kurtic of 2.09. Total petroleum hydrocarbon was 0.033 mg/L to 0.88 mg/L, and total hydrocarbon content and iron were 1.65 mg/L to 3.41 mg/L, and 2.98 mg/L-0.48 mg/L respectively. The results of these contaminants bound to sediments and water were above the acceptable limits of the World Health Organization. The ingress of contaminants into the groundwater was significantly controlled by the characteristics of the borehole sediment.
{"title":"GIS Analysis of Organo-Contaminants and Iron Linked to Groundwater and Sediment at Boreholes in Aluu, Delta Region, Nigeria","authors":"Davidson Egirani, Ginikanwa Chidi","doi":"10.37256/ccds.4320232095","DOIUrl":"https://doi.org/10.37256/ccds.4320232095","url":null,"abstract":"The source of high fever and gastrointestinal disorders in humans after groundwater consumption in this part of the delta region in Nigeria is unknown. Spatial data engineered by GIS interpretation of organo-contaminants bound to groundwater and borehole sediment provides baseline data and information on the impact of iron and organo-contaminants on groundwater quality in Aluu. A total of 10 water and sediment samples were collected at a depth of 45 m from 10 boreholes within Aluu and analyzed in triplicate. The choice of 45 m implies the occurrence of sediments bearing groundwater for a deep well. A particle size analyzer was used to perform particle size analyses of the air-dried sediments. The American Public Health Association Method (APHA) was used to perform the chemical analysis of the water samples. Here, a liquid-liquid extraction procedure was conducted on the samples using 30 mL dichloromethane (DCM) as the extraction agent. The results were subjected to statistical validation, spatial data and GIS analysis. The textural characteristics possessed a mean grain size from fine sand (2.03) to medium sand (4.3), poorly sorted of 1.45 to 2.1, skewness of near-symmetrical (0.02), meso-kurtic kurtosis of 0.5 to very platy-kurtic of 2.09. Total petroleum hydrocarbon was 0.033 mg/L to 0.88 mg/L, and total hydrocarbon content and iron were 1.65 mg/L to 3.41 mg/L, and 2.98 mg/L-0.48 mg/L respectively. The results of these contaminants bound to sediments and water were above the acceptable limits of the World Health Organization. The ingress of contaminants into the groundwater was significantly controlled by the characteristics of the borehole sediment.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127345572","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 : 2022-12-12DOI: 10.37256/ccds.4120232019
C. Yap, Hisyamuddin Hashim, Muhammad Hakim Ainuddin, Mohd Amiruddin Abd Rahman, Khairul Adib Yusof, Norizah Abdul Rahman, Naszroul Haqimee Rahmat, Amir Hamzah Abd. Ghafar, Norlida Md Noor, Siti Hajar Alias, Norzaina Darus, Jivananthan Arumugam, Noor Nashrin Arlina Anas, Bimo Ario Tejo, Wan Mohd Syazwan, Zanariah Abdul Majid, Halimah Mohamed Kamari, Khamirul Amin Matori, Muskhazli Mustafa, Nor Azwady Abd Aziz, Mohd Basyaruddin Abdul Rahman, Khalid Awadh Al-Mutairi, Krishnan Kumar, Geetha Subramaniam, Wan Hee Cheng
Universities' identities and institutional images are showcased on their websites to the rest of the world. Nowadays, many university websites (UW)s have been well-investigated for usability improvement for all users in general. This study aims to review the publications indexed in the Scopus database in 2021 using the search term 'University Websites' and to synthesize the main information being discussed in the manuscripts. Two main reasons why only papers published in 2021 were selected for this study. Firstly, in terms of the number of publications (N = 456) indexed in Scopus from 1996 to 2021, the 2021 publications are the most recent complete year. Secondly, 2021 topped the list of publications along with 2020. For the year 2021, a total of 58 publications were found in the Scopus database as of February 26, 2022. After screening all the papers, only 39 papers were used for this quantitative analysis. The present systematic review presented three major trends. Firstly, the publications on the UWs are expected to be higher in near future aligned with the speed of Industry 4.0 development worldwide. Secondly, there is a total of 24 countries and 1 region (Latin America) found in this review, with Indonesia leading the list with 8 publications. Thirdly, all the papers aimed to identify the obstacles and recommended ways and room for future improvements for all users regarding their UWs. This review paper highlighted the importance of having effective and up-to-date websites from social and economic viewpoints. It can be synthesized here that continual improvements in the knowledge of the effective usability of a UW can sustain a university's reputation and ranking ultimately.
{"title":"Usability of University Websites as Information Sources: A Review and Synthesis Based on 2021 Publications Indexed in Scopus Database","authors":"C. Yap, Hisyamuddin Hashim, Muhammad Hakim Ainuddin, Mohd Amiruddin Abd Rahman, Khairul Adib Yusof, Norizah Abdul Rahman, Naszroul Haqimee Rahmat, Amir Hamzah Abd. Ghafar, Norlida Md Noor, Siti Hajar Alias, Norzaina Darus, Jivananthan Arumugam, Noor Nashrin Arlina Anas, Bimo Ario Tejo, Wan Mohd Syazwan, Zanariah Abdul Majid, Halimah Mohamed Kamari, Khamirul Amin Matori, Muskhazli Mustafa, Nor Azwady Abd Aziz, Mohd Basyaruddin Abdul Rahman, Khalid Awadh Al-Mutairi, Krishnan Kumar, Geetha Subramaniam, Wan Hee Cheng","doi":"10.37256/ccds.4120232019","DOIUrl":"https://doi.org/10.37256/ccds.4120232019","url":null,"abstract":"Universities' identities and institutional images are showcased on their websites to the rest of the world. Nowadays, many university websites (UW)s have been well-investigated for usability improvement for all users in general. This study aims to review the publications indexed in the Scopus database in 2021 using the search term 'University Websites' and to synthesize the main information being discussed in the manuscripts. Two main reasons why only papers published in 2021 were selected for this study. Firstly, in terms of the number of publications (N = 456) indexed in Scopus from 1996 to 2021, the 2021 publications are the most recent complete year. Secondly, 2021 topped the list of publications along with 2020. For the year 2021, a total of 58 publications were found in the Scopus database as of February 26, 2022. After screening all the papers, only 39 papers were used for this quantitative analysis. The present systematic review presented three major trends. Firstly, the publications on the UWs are expected to be higher in near future aligned with the speed of Industry 4.0 development worldwide. Secondly, there is a total of 24 countries and 1 region (Latin America) found in this review, with Indonesia leading the list with 8 publications. Thirdly, all the papers aimed to identify the obstacles and recommended ways and room for future improvements for all users regarding their UWs. This review paper highlighted the importance of having effective and up-to-date websites from social and economic viewpoints. It can be synthesized here that continual improvements in the knowledge of the effective usability of a UW can sustain a university's reputation and ranking ultimately.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117351616","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 : 2022-11-15DOI: 10.37256/ccds.4120231959
Hamed Taherdoost
Businesses are highly dependent on data to make critical decisions, manage operations, and simplify processes. Information systems equip businesses to gain benefits from data and provide easy and timely access to data through storing and processing input data from numerous resources. The majority of managers can deal with large amounts of data without letting it interfere with their ability to plan, organize, and control the organization. The disconnect between static information systems and evolving organizational structures is another primary factor contributing to information vulnerability. Organizational restructuring often necessitated revisions to preexisting information fixed systems to account for changing roles, responsibilities, levels of authority, and data requirements. An effective information system enables decision-makers in businesses to monitor trends, plan, predict measures prior to their competitors. The role of information systems to improve business performance has been investigated in studies considering the importance of relevant, accurate, and timely data. However, to increase the effectiveness of information systems, a comprehensive understanding of its applications and use cases of each type of information systems based on different organizational levels is required. This paper aims to provide concepts of information systems, present different applications of information systems, and discuss the main types of information systems based on their level of application. Specific types, roles, advantages, and limitations of information systems are also highlighted focusing on their impact on business developments. Besides, the impacts of different types of information systems on organizations and processes are provided.
{"title":"The Role of Different Types of Management Information System Applications in Business Development: Concepts, and Limitations","authors":"Hamed Taherdoost","doi":"10.37256/ccds.4120231959","DOIUrl":"https://doi.org/10.37256/ccds.4120231959","url":null,"abstract":"Businesses are highly dependent on data to make critical decisions, manage operations, and simplify processes. Information systems equip businesses to gain benefits from data and provide easy and timely access to data through storing and processing input data from numerous resources. The majority of managers can deal with large amounts of data without letting it interfere with their ability to plan, organize, and control the organization. The disconnect between static information systems and evolving organizational structures is another primary factor contributing to information vulnerability. Organizational restructuring often necessitated revisions to preexisting information fixed systems to account for changing roles, responsibilities, levels of authority, and data requirements. An effective information system enables decision-makers in businesses to monitor trends, plan, predict measures prior to their competitors. The role of information systems to improve business performance has been investigated in studies considering the importance of relevant, accurate, and timely data. However, to increase the effectiveness of information systems, a comprehensive understanding of its applications and use cases of each type of information systems based on different organizational levels is required. This paper aims to provide concepts of information systems, present different applications of information systems, and discuss the main types of information systems based on their level of application. Specific types, roles, advantages, and limitations of information systems are also highlighted focusing on their impact on business developments. Besides, the impacts of different types of information systems on organizations and processes are provided.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131198086","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 : 2022-10-31DOI: 10.37256/ccds.4120231847
Aws Rayyan, Mohammad Ghassan Aburas, Amjed Al-mousa
In the Internet era, there is no doubt that the Internet has helped us in many ways by providing us with a means to communicate with anyone around the world. That is said, some people misuse such technology to conduct malicious behaviors. Many things could be exploited to perform such acts, but this work focuses on exploitation methods that use the uniform resource locator (URL). This paper presents the means to extract features from a raw URL. These are used to predict whether a URL is safe for a user to visit or not. The whole process of extracting the data and preparing it for a model is discussed thoroughly in this paper. Several machine learning (ML) models have been trained using different algorithms, including Catboost, RandomForest, and Decision trees, in addition to using and exploring several feedforward deep neural networks learning models. The best model achieved an accuracy of 95.61% on a test set using a deep learning model.
{"title":"Uniform Resource Locator Classification Using Classical Machine Learning & Deep Learning Techniques","authors":"Aws Rayyan, Mohammad Ghassan Aburas, Amjed Al-mousa","doi":"10.37256/ccds.4120231847","DOIUrl":"https://doi.org/10.37256/ccds.4120231847","url":null,"abstract":"In the Internet era, there is no doubt that the Internet has helped us in many ways by providing us with a means to communicate with anyone around the world. That is said, some people misuse such technology to conduct malicious behaviors. Many things could be exploited to perform such acts, but this work focuses on exploitation methods that use the uniform resource locator (URL). This paper presents the means to extract features from a raw URL. These are used to predict whether a URL is safe for a user to visit or not. The whole process of extracting the data and preparing it for a model is discussed thoroughly in this paper. Several machine learning (ML) models have been trained using different algorithms, including Catboost, RandomForest, and Decision trees, in addition to using and exploring several feedforward deep neural networks learning models. The best model achieved an accuracy of 95.61% on a test set using a deep learning model.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132033134","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 : 2022-10-12DOI: 10.37256/ccds.4320231678
O. Sharifi-Tehrani, M. Ghasemi
Global navigation satellite systems (GNSS) have played an important role in commercial, military and industrial navigation as well as cloud computing, geospatial analysis and digital modeling. Nowadays, with the advancement of science and technology, the capabilities of electronic warfare, including signal jamming, interference, and spoofing, have also advanced. Attacks and threats at simple, intermediate and advanced levels, endanger the security and reliability of GNSS in the commercial, industrial and military fields such as geolocation, geospatial techniques and digital twins. Therefore, coping with this problem and challenge is very important in maintaining security and reliability. At present, various methods and algorithms have been designed and utilized based on statistical properties, moving receiver, artificial array, wavelet transform and etc., each of which has advantages, disadvantages and blind spots. In this paper, the necessity and requirements for dealing with GNSS threats are emphasized, and the most important researches in the field of GNSS threat (jamming/interference/spoofing) detection and mitigation are studied and reviewed. Their advantages and disadvantages are discussed, and improving areas are also proposed.
{"title":"A Review on GNSS-Threat Detection and Mitigation Techniques","authors":"O. Sharifi-Tehrani, M. Ghasemi","doi":"10.37256/ccds.4320231678","DOIUrl":"https://doi.org/10.37256/ccds.4320231678","url":null,"abstract":"Global navigation satellite systems (GNSS) have played an important role in commercial, military and industrial navigation as well as cloud computing, geospatial analysis and digital modeling. Nowadays, with the advancement of science and technology, the capabilities of electronic warfare, including signal jamming, interference, and spoofing, have also advanced. Attacks and threats at simple, intermediate and advanced levels, endanger the security and reliability of GNSS in the commercial, industrial and military fields such as geolocation, geospatial techniques and digital twins. Therefore, coping with this problem and challenge is very important in maintaining security and reliability. At present, various methods and algorithms have been designed and utilized based on statistical properties, moving receiver, artificial array, wavelet transform and etc., each of which has advantages, disadvantages and blind spots. In this paper, the necessity and requirements for dealing with GNSS threats are emphasized, and the most important researches in the field of GNSS threat (jamming/interference/spoofing) detection and mitigation are studied and reviewed. Their advantages and disadvantages are discussed, and improving areas are also proposed.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128718586","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 : 2022-08-24DOI: 10.37256/ccds.4120231653
Hamed Taherdoost
Technology is mainly characterized by being changed rapidly. In other words, it is recognized as the ever-changing playing field. Those who aim to stay in the technology field need to quickly get adapted to such constant changes in this field. Due to the high pace of information technology advances, it is required to identify and implement appropriate technologies by which the organizations can effectively stay and compete in the business through the accurate and real-time efficiency delivered by such technologies as cloud computing, internet of things (IoT), artificial intelligence, blockchain, big data analytics, virtual and augmented reality, 5g network, and, etc. These trends are critically important because turning and adapting to the latest trends in information technology and systems are largely contributing to meeting the consumers' technology-enabled demands. In this paper, the most widely used trends in information systems and technology will be discussed.
{"title":"An Overview of Trends in Information Systems: Emerging Technologies that Transform the Information Technology Industry","authors":"Hamed Taherdoost","doi":"10.37256/ccds.4120231653","DOIUrl":"https://doi.org/10.37256/ccds.4120231653","url":null,"abstract":"Technology is mainly characterized by being changed rapidly. In other words, it is recognized as the ever-changing playing field. Those who aim to stay in the technology field need to quickly get adapted to such constant changes in this field. Due to the high pace of information technology advances, it is required to identify and implement appropriate technologies by which the organizations can effectively stay and compete in the business through the accurate and real-time efficiency delivered by such technologies as cloud computing, internet of things (IoT), artificial intelligence, blockchain, big data analytics, virtual and augmented reality, 5g network, and, etc. These trends are critically important because turning and adapting to the latest trends in information technology and systems are largely contributing to meeting the consumers' technology-enabled demands. In this paper, the most widely used trends in information systems and technology will be discussed.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121498056","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 : 2022-07-14DOI: 10.37256/ccds.3220221488
Maleerat Maliyaem, Nguyen Minh Tuan, Demontray Lockhart, S. Muenthong
With an unprecedented challenge to combat COVID-19, the prediction of confirmed cases is very important to ensure medical aid and healthy living conditions. In order to predict confirmed cases, the current study uses a dataset prepared by the White House Office of Science and Technology Policy which brought together companies and research to address questions concerning COVID-19. The importance of this was to identify factors that seem to affect the transmission rate of COVID-19. The focus of the current research, however, is to predict global cases of COVID-19. There have been many papers written about the prediction of confirmed cases and fatalities, but they failed to show promising results. Our research applies machine learning for predicting fatalities in the world using the COVID-19 Forecasting dataset from Kaggle. After trying several algorithms, our findings reveal that Logistic Regression, Decision Tree, KNeighbors, GaussianNB, and Random Forest algorithms provide the best predictions. Thus, the results show Random Forest as having the highest accuracy followed by Logistic Regression and Decision Tree. The results are promising opening up the door for further research.
{"title":"A Study of Using Machine Learning in Predicting COVID-19 Cases","authors":"Maleerat Maliyaem, Nguyen Minh Tuan, Demontray Lockhart, S. Muenthong","doi":"10.37256/ccds.3220221488","DOIUrl":"https://doi.org/10.37256/ccds.3220221488","url":null,"abstract":"With an unprecedented challenge to combat COVID-19, the prediction of confirmed cases is very important to ensure medical aid and healthy living conditions. In order to predict confirmed cases, the current study uses a dataset prepared by the White House Office of Science and Technology Policy which brought together companies and research to address questions concerning COVID-19. The importance of this was to identify factors that seem to affect the transmission rate of COVID-19. The focus of the current research, however, is to predict global cases of COVID-19. There have been many papers written about the prediction of confirmed cases and fatalities, but they failed to show promising results. Our research applies machine learning for predicting fatalities in the world using the COVID-19 Forecasting dataset from Kaggle. After trying several algorithms, our findings reveal that Logistic Regression, Decision Tree, KNeighbors, GaussianNB, and Random Forest algorithms provide the best predictions. Thus, the results show Random Forest as having the highest accuracy followed by Logistic Regression and Decision Tree. The results are promising opening up the door for further research.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129504977","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 : 2022-07-11DOI: 10.37256/ccds.3220221423
Syed Khasim, Shaik Shakeer Basha
Seeing as Smart Healthcare Systems provide cloud services for storing patient health records, data security and privacy are critical to the company's success, and patients do not want their identities to be revealed. The authentication procedure requires disclosing users' personal data, such as a username and password, on the authentication server in order to protect their identities. The patient's privacy may be invaded if the patient can be observed or linked to by the patient's unfortunate foes. As a result, we propose in this paper a system that gives patients anonymity, protection, and privacy of sensitive healthcare data from the Authorization Service and enemies. A camel-based rotating panel signature program was used in our proposed work to provide anonymity to health records while also adding extra security to the network layer. The effectiveness of the programs was assessed using theoretical analysis, which revealed that the program has a range of security characteristics and is resistant to multiple attacks.
{"title":"An Improved Fast and Secure CAMEL Based Authenticated Key in Smart Health Care System","authors":"Syed Khasim, Shaik Shakeer Basha","doi":"10.37256/ccds.3220221423","DOIUrl":"https://doi.org/10.37256/ccds.3220221423","url":null,"abstract":"Seeing as Smart Healthcare Systems provide cloud services for storing patient health records, data security and privacy are critical to the company's success, and patients do not want their identities to be revealed. The authentication procedure requires disclosing users' personal data, such as a username and password, on the authentication server in order to protect their identities. The patient's privacy may be invaded if the patient can be observed or linked to by the patient's unfortunate foes. As a result, we propose in this paper a system that gives patients anonymity, protection, and privacy of sensitive healthcare data from the Authorization Service and enemies. A camel-based rotating panel signature program was used in our proposed work to provide anonymity to health records while also adding extra security to the network layer. The effectiveness of the programs was assessed using theoretical analysis, which revealed that the program has a range of security characteristics and is resistant to multiple attacks.","PeriodicalId":158315,"journal":{"name":"Cloud Computing and Data Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116310857","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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