It is impossible to overstate the necessity of a strategic and practical approach in the workplace in order to maximize productivity these days. Teamwork is one of the best ways to adapt to the changes that have occurred in today's environment throughout time. In every industry, the optimum performance arrangement for realizing visions, carrying out plans, and accomplishing objectives is teamwork. It is also one of the most crucial components of systems for continuous improvement since it makes information exchange, issue resolution, and the growth of employee accountability easier. Teams function as a grouping of people with complementary talents who work together rather than against one another. They are held accountable for their strategic methods and use them to achieve a shared objective. The Supervised Learning technique was used in this work to simulate team performance utilizing an intelligent coaching agent. Through the use of an automated performance assessment and weighted scores for each task, this study was able to create a system that will remove biases from performance evaluation. As soon as a worker does the task, they will obtain a score. The purpose of this study was to demonstrate an event-based performance approach by developing and utilizing an intelligent coaching agent in a supervised learning team training framework. The goal was successfully met, and the result shows positive impacts on the team's performance.
{"title":"Agent Based Intelligent System for Enhanced Teamwork Performance","authors":"Chidi Betrand, Oluchukwu Ekwealor, Chinwe Onukwugha, Christopher Ofoegbu, Obinna Aliche, Evelyn Ezuruka, Chukwuemeka Okafor","doi":"10.11648/j.ijdst.20241002.11","DOIUrl":"https://doi.org/10.11648/j.ijdst.20241002.11","url":null,"abstract":"It is impossible to overstate the necessity of a strategic and practical approach in the workplace in order to maximize productivity these days. Teamwork is one of the best ways to adapt to the changes that have occurred in today's environment throughout time. In every industry, the optimum performance arrangement for realizing visions, carrying out plans, and accomplishing objectives is teamwork. It is also one of the most crucial components of systems for continuous improvement since it makes information exchange, issue resolution, and the growth of employee accountability easier. Teams function as a grouping of people with complementary talents who work together rather than against one another. They are held accountable for their strategic methods and use them to achieve a shared objective. The Supervised Learning technique was used in this work to simulate team performance utilizing an intelligent coaching agent. Through the use of an automated performance assessment and weighted scores for each task, this study was able to create a system that will remove biases from performance evaluation. As soon as a worker does the task, they will obtain a score. The purpose of this study was to demonstrate an event-based performance approach by developing and utilizing an intelligent coaching agent in a supervised learning team training framework. The goal was successfully met, and the result shows positive impacts on the team's performance.\u0000","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992216","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 : 2024-02-20DOI: 10.11648/j.ijdst.20241001.12
Chidi Ukamaka Betrand, C. Onukwugha, Christopher ifeanyi Ofoegbu, Obinna Banner Aliche, Douglas Allswell Kelechi
Firms can save operating expenses and improve customer satisfaction by managing their logistics well. Delivering goods and services to customers with the highest standards while reducing operating costs is the aim of the logistics management philosophy. As a result, logistics management is a crucial component of the supply chain process, which also includes other tasks including organizing, directing, planning, storing, communicating, and providing support. Web applications tracking allow easy access to goods and services over the internet. It allows for easy detection of the state, location of goods and services at any given instance. This web application gives the users easy accessisibility to the platform. The logistics web application for the tracking of parcels was developed using Angular Js, Node and Express Js, and MongoDB. Hosted on Heroku. The aim of the project which is to meet the demands of the users while offering real-time visibility, efficient route optimization, as well as the overall streaming of the supply chain process was achieved. With this application, users can finally be able to know the current and real time location of their packages so long as they have access to the internet.
{"title":"Logistics Web Application for the Tracking of Parcels","authors":"Chidi Ukamaka Betrand, C. Onukwugha, Christopher ifeanyi Ofoegbu, Obinna Banner Aliche, Douglas Allswell Kelechi","doi":"10.11648/j.ijdst.20241001.12","DOIUrl":"https://doi.org/10.11648/j.ijdst.20241001.12","url":null,"abstract":"Firms can save operating expenses and improve customer satisfaction by managing their logistics well. Delivering goods and services to customers with the highest standards while reducing operating costs is the aim of the logistics management philosophy. As a result, logistics management is a crucial component of the supply chain process, which also includes other tasks including organizing, directing, planning, storing, communicating, and providing support. Web applications tracking allow easy access to goods and services over the internet. It allows for easy detection of the state, location of goods and services at any given instance. This web application gives the users easy accessisibility to the platform. The logistics web application for the tracking of parcels was developed using Angular Js, Node and Express Js, and MongoDB. Hosted on Heroku. The aim of the project which is to meet the demands of the users while offering real-time visibility, efficient route optimization, as well as the overall streaming of the supply chain process was achieved. With this application, users can finally be able to know the current and real time location of their packages so long as they have access to the internet.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"41 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449043","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 : 2024-02-20DOI: 10.11648/j.ijdst.20241001.11
Abed Matini, Stanley Lekata, Boniface Kabaso
In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer's intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers' purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.
{"title":"The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites","authors":"Abed Matini, Stanley Lekata, Boniface Kabaso","doi":"10.11648/j.ijdst.20241001.11","DOIUrl":"https://doi.org/10.11648/j.ijdst.20241001.11","url":null,"abstract":"In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer's intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers' purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"355 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447964","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 : 2023-03-20DOI: 10.11648/j.ijdst.20230901.11
Meresa Hiluf Gebrehiwot, Michael Melese
: With the ever-increasing amounts of textual material such as web pages, news articles, blogs, microblogs
随着网页、新闻、博客、微博等文本材料的不断增加
{"title":"Extractive Text Summarization Using Deep Learning for Tigrigna Language","authors":"Meresa Hiluf Gebrehiwot, Michael Melese","doi":"10.11648/j.ijdst.20230901.11","DOIUrl":"https://doi.org/10.11648/j.ijdst.20230901.11","url":null,"abstract":": With the ever-increasing amounts of textual material such as web pages, news articles, blogs, microblogs","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"482 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132616023","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 : 2021-09-04DOI: 10.11648/J.IJDST.20210703.13
Oganga Caneble, A. Wanjoya, Anthony Ngunyi
In emerging countries, such as Kenya, the foreign exchange market is an important aspect in the economic development of a country. The currency exchange rate market, like the rest of the world's financial markets, has been marked by considerable instabilities over the last decade. The objective of this paper is to model the volatility of the KSH/USD exchange rate prices using and calculate the VaR using the GARCH-EVT model. In particular, this article uses the two-stage GARCH-EVT approach to estimate the value at risk of the Kenyan Shilling against the US dollar., particularly the one-day ahead Value-at-Risk forecast in risk control. The conditional and unconditional coverage test are used to back test the model. We compare the performance of the GARCH-EVT with the daily log returns of key currency in addition to modelling the value at risk in the Kenyan Foreign Exchange market (US dollar) foreign currencies from the period November 2004 – June 2021 for trading days with the exception of holidays and weekends. The mean equation that was best fitting for the data was ARMA (4,2). The optimal GARCH model for the returns of the KSH/USD exchange rate is the GARCH (1,3) with student-t innovations. The results of the backtesting show that GARCH-EVT can be utilized to estimate and forecast VaR at both 5% and 1% level of significance.
{"title":"Modelling the Volatility of Central Bank of Kenya Currency Exchange Rates","authors":"Oganga Caneble, A. Wanjoya, Anthony Ngunyi","doi":"10.11648/J.IJDST.20210703.13","DOIUrl":"https://doi.org/10.11648/J.IJDST.20210703.13","url":null,"abstract":"In emerging countries, such as Kenya, the foreign exchange market is an important aspect in the economic development of a country. The currency exchange rate market, like the rest of the world's financial markets, has been marked by considerable instabilities over the last decade. The objective of this paper is to model the volatility of the KSH/USD exchange rate prices using and calculate the VaR using the GARCH-EVT model. In particular, this article uses the two-stage GARCH-EVT approach to estimate the value at risk of the Kenyan Shilling against the US dollar., particularly the one-day ahead Value-at-Risk forecast in risk control. The conditional and unconditional coverage test are used to back test the model. We compare the performance of the GARCH-EVT with the daily log returns of key currency in addition to modelling the value at risk in the Kenyan Foreign Exchange market (US dollar) foreign currencies from the period November 2004 – June 2021 for trading days with the exception of holidays and weekends. The mean equation that was best fitting for the data was ARMA (4,2). The optimal GARCH model for the returns of the KSH/USD exchange rate is the GARCH (1,3) with student-t innovations. The results of the backtesting show that GARCH-EVT can be utilized to estimate and forecast VaR at both 5% and 1% level of significance.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490302","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 : 2021-08-27DOI: 10.11648/J.IJDST.20210703.12
Shaolin Hu, Xiaomin Huang, Naiqian Su, Shihua Wang
Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.
{"title":"Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series","authors":"Shaolin Hu, Xiaomin Huang, Naiqian Su, Shihua Wang","doi":"10.11648/J.IJDST.20210703.12","DOIUrl":"https://doi.org/10.11648/J.IJDST.20210703.12","url":null,"abstract":"Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"84 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129621690","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 : 2021-08-02DOI: 10.11648/j.ijdst.20210702.12
Arinze Anikwue, Boniface Kabaso
There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models that specify generic knowledge in different topics. These models are used in conjunction with an inference algorithm to enable decision makers especially during uncertain situations. Extensive knowledge in fields such as statistics, machine learning and probability theories are employed in the development of these probabilistic models. Thus, it is usually a difficult undertaking. Probabilistic programming was introduced to simplify and enable development of complex models. Again, decision makers often need to use knowledge from historic data as well as current data to make cogent decisions. Thus, the necessity to unify processing of historic and real-time data with low latency. The Lambda architecture was introduced for this purpose. This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture. Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable real-time reasoning on big data. Thus, demonstrating the effectiveness of the framework. Finally, results of this evaluation are presented in this paper. The Kognitor framework can be used to steer effective and easier implementation of complicated real-life situations as probabilistic models. This will be beneficial in the big data processing domain and for decision makers. Kognitor ensures cost-effectiveness using contemporary big data tools and technology on commodity hardware. Kognitor framework will also be beneficial in academia with respect to the use of probabilistic programming.
{"title":"Kognitor: Big Data Real-Time Reasoning and Probabilistic Programming","authors":"Arinze Anikwue, Boniface Kabaso","doi":"10.11648/j.ijdst.20210702.12","DOIUrl":"https://doi.org/10.11648/j.ijdst.20210702.12","url":null,"abstract":"There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models that specify generic knowledge in different topics. These models are used in conjunction with an inference algorithm to enable decision makers especially during uncertain situations. Extensive knowledge in fields such as statistics, machine learning and probability theories are employed in the development of these probabilistic models. Thus, it is usually a difficult undertaking. Probabilistic programming was introduced to simplify and enable development of complex models. Again, decision makers often need to use knowledge from historic data as well as current data to make cogent decisions. Thus, the necessity to unify processing of historic and real-time data with low latency. The Lambda architecture was introduced for this purpose. This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture. Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable real-time reasoning on big data. Thus, demonstrating the effectiveness of the framework. Finally, results of this evaluation are presented in this paper. The Kognitor framework can be used to steer effective and easier implementation of complicated real-life situations as probabilistic models. This will be beneficial in the big data processing domain and for decision makers. Kognitor ensures cost-effectiveness using contemporary big data tools and technology on commodity hardware. Kognitor framework will also be beneficial in academia with respect to the use of probabilistic programming.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124777267","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 : 2021-02-02DOI: 10.11648/J.IJDST.20210701.12
A. Olowe, J. K. Olorundare, Temitope Phillips
Financial exclusion remains a significant challenge in developing economies. It has been shown that access to credit facilities is a strong predictor of financial inclusion. Credit reporting and scoring remain effective tools for both traditional and alternative lenders, however, access to credible credit data and scoring mechanisms is one of the biggest roadblocks that alternative lenders in developing economies face. While some lenders have developed systems that leverage social media analytics and data harvested from smartphones in order to create a scoring system, the poor and vulnerable are still excluded from such scoring systems. There have been significant advances in the use of telecoms data for credit scoring, making it a promising alternative to credit bureau data. However, readily available data is still an issue. With the increase in the development and use of open APIs, telecoms data could be made readily available for credit scoring, while addressing privacy and other issues. This paper is a conceptual paper that proposes a model for the use of Open APIs from telco data for credit scoring that will ultimately increase access to credit, and ultimately financial inclusion in Africa.
{"title":"Using Open APIs To Drive Financial Inclusion Via Credit Scoring Built on Telecoms Data","authors":"A. Olowe, J. K. Olorundare, Temitope Phillips","doi":"10.11648/J.IJDST.20210701.12","DOIUrl":"https://doi.org/10.11648/J.IJDST.20210701.12","url":null,"abstract":"Financial exclusion remains a significant challenge in developing economies. It has been shown that access to credit facilities is a strong predictor of financial inclusion. Credit reporting and scoring remain effective tools for both traditional and alternative lenders, however, access to credible credit data and scoring mechanisms is one of the biggest roadblocks that alternative lenders in developing economies face. While some lenders have developed systems that leverage social media analytics and data harvested from smartphones in order to create a scoring system, the poor and vulnerable are still excluded from such scoring systems. There have been significant advances in the use of telecoms data for credit scoring, making it a promising alternative to credit bureau data. However, readily available data is still an issue. With the increase in the development and use of open APIs, telecoms data could be made readily available for credit scoring, while addressing privacy and other issues. This paper is a conceptual paper that proposes a model for the use of Open APIs from telco data for credit scoring that will ultimately increase access to credit, and ultimately financial inclusion in Africa.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123251047","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 : 2020-01-16DOI: 10.11648/J.IJDST.20200601.16
L. Abate, M. Tadesse
Pneumonia is among the major killer diseases in under-five children in the world. In developing countries 3 million children die each year due to pneumonia. Ethiopia is one of the 15 pneumonia high burden countries. The aim of this study was to examine the risk factors of the survival time of under-five pneumonia patients using Bayesian approach analysis. Total of 281 under-five pneumonia patients included in this study. The parametric survival models such as Weibull, Lognormal and Log-logistic baseline distributions were used to fit the datasets by introducing prior distributions. The DIC value was used to compare the baseline distributions, and based on the DIC value the Weibull baseline distribution was selected as good model to fit under-five pneumonia dataset well. The results obtained from the Weibull survival model showed that patients from urban residence and patients who were admitted during patient nurse ratio (PNR) was small; were prolong timing death of under-five pneumonia patients, while patients who admitted during Spring and summer season, patients who suffer comorbidity and severe acute malnutrition (SAM) were shorten timing of death of under-five pneumonia patients. Factors such as sex, residence, Season of Diagnosis, Comorbidity, Severe Acute Malnutrition (SAM), Patient refer status and Patient to Nurse Ratio (PNR) were associated with the survival time of under-five pneumonia in this study. The concerned body should give attention for the factors identified in these study to prevent the mortality of under-five children due to pneumonia.
{"title":"Application of Bayesian Approach Survival Analysis of Under-five Pneumonia Patients in Tercha General Hospital, South West Ethiopia","authors":"L. Abate, M. Tadesse","doi":"10.11648/J.IJDST.20200601.16","DOIUrl":"https://doi.org/10.11648/J.IJDST.20200601.16","url":null,"abstract":"Pneumonia is among the major killer diseases in under-five children in the world. In developing countries 3 million children die each year due to pneumonia. Ethiopia is one of the 15 pneumonia high burden countries. The aim of this study was to examine the risk factors of the survival time of under-five pneumonia patients using Bayesian approach analysis. Total of 281 under-five pneumonia patients included in this study. The parametric survival models such as Weibull, Lognormal and Log-logistic baseline distributions were used to fit the datasets by introducing prior distributions. The DIC value was used to compare the baseline distributions, and based on the DIC value the Weibull baseline distribution was selected as good model to fit under-five pneumonia dataset well. The results obtained from the Weibull survival model showed that patients from urban residence and patients who were admitted during patient nurse ratio (PNR) was small; were prolong timing death of under-five pneumonia patients, while patients who admitted during Spring and summer season, patients who suffer comorbidity and severe acute malnutrition (SAM) were shorten timing of death of under-five pneumonia patients. Factors such as sex, residence, Season of Diagnosis, Comorbidity, Severe Acute Malnutrition (SAM), Patient refer status and Patient to Nurse Ratio (PNR) were associated with the survival time of under-five pneumonia in this study. The concerned body should give attention for the factors identified in these study to prevent the mortality of under-five children due to pneumonia.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"81 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130873133","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 : 2019-12-31DOI: 10.11648/J.IJDST.20190504.11
E. Bryndin
Artificial intelligence is a revolutionary technology that is designed to transform the life of the world community: to optimize business processes, to provide valuable information, to increase creative service to citizens. The importance of integrating artificial intelligence into the infrastructure of the future has already been recognized. The Government AI Readiness Index has been created, which reflects the readiness of Governments to support the development of artificial intelligence technology. The coming years will take to improve security and standardize the development and use of intelligent agents that ensure their compatibility. Intelligent agents can be combined at the software level through a standard interface to communicate with them based on mental real mathematics. Compatibility will allow produce from them intelligent ensembles with cognitive creative and behavioral abilities of the person for service services. It will also allow produce intelligent production high-tech complexes. Standardizing the cooperation of intelligent agents will help to ensure the interface, compatibility and synergy of their safe application in various sectors of economy, industry and service. Creative ensembles of intelligent interoperable agents, which implement technological, production, service, commercial, research and other creative processes, are an incentive for a breakthrough in the field of artificial intelligence for the sustainable development of society. In the future, creative ensembles of intellectual interoperable agents will qualitatively change the life of the world community.
{"title":"Collaboration of Intelligent Interoperable Agents Via Smart Interface","authors":"E. Bryndin","doi":"10.11648/J.IJDST.20190504.11","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190504.11","url":null,"abstract":"Artificial intelligence is a revolutionary technology that is designed to transform the life of the world community: to optimize business processes, to provide valuable information, to increase creative service to citizens. The importance of integrating artificial intelligence into the infrastructure of the future has already been recognized. The Government AI Readiness Index has been created, which reflects the readiness of Governments to support the development of artificial intelligence technology. The coming years will take to improve security and standardize the development and use of intelligent agents that ensure their compatibility. Intelligent agents can be combined at the software level through a standard interface to communicate with them based on mental real mathematics. Compatibility will allow produce from them intelligent ensembles with cognitive creative and behavioral abilities of the person for service services. It will also allow produce intelligent production high-tech complexes. Standardizing the cooperation of intelligent agents will help to ensure the interface, compatibility and synergy of their safe application in various sectors of economy, industry and service. Creative ensembles of intelligent interoperable agents, which implement technological, production, service, commercial, research and other creative processes, are an incentive for a breakthrough in the field of artificial intelligence for the sustainable development of society. In the future, creative ensembles of intellectual interoperable agents will qualitatively change the life of the world community.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"60 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132086163","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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