Pub Date : 2023-08-31DOI: 10.9734/ajpas/2023/v24i2522
N. O. Iheonu, Uzoma K. Achom
Timely delivery of natural and fresh juice to its’ growing customers is the focus of SCUBED 100%. The CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique) techniques were applied in minimizing the expected time duration for juice production at SCUBED 100%. The current expected production time for a batch stood at 656.66 minutes. Modifications were made on the initial model to minimize the expected duration and after two modification processes, an estimated time duration of 458.33 minutes was realized, saving 198.33 minutes (about 3.3 hours) of production time. This study encapsulates the interplay of theoretical insights and practical implementation, confirming the potential of operational research and management techniques in designing real-world outcomes. SCUBED 100%'s journey towards operational excellence demonstrates the transformative potential of time optimization.
{"title":"Project Planning Application to Juice Production Using PERT/CPM Technique: A Case Study","authors":"N. O. Iheonu, Uzoma K. Achom","doi":"10.9734/ajpas/2023/v24i2522","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i2522","url":null,"abstract":"Timely delivery of natural and fresh juice to its’ growing customers is the focus of SCUBED 100%. The CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique) techniques were applied in minimizing the expected time duration for juice production at SCUBED 100%. The current expected production time for a batch stood at 656.66 minutes. Modifications were made on the initial model to minimize the expected duration and after two modification processes, an estimated time duration of 458.33 minutes was realized, saving 198.33 minutes (about 3.3 hours) of production time. This study encapsulates the interplay of theoretical insights and practical implementation, confirming the potential of operational research and management techniques in designing real-world outcomes. SCUBED 100%'s journey towards operational excellence demonstrates the transformative potential of time optimization.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80451013","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 : 2023-08-30DOI: 10.9734/ajpas/2023/v24i2521
Wilson Simon Barguma, O. Achimugu
This research seeks to apply "numerical assessment of water quality using quality control - A case study of Idah, Kogi state, Nigeria.” Secondary data of 500 bags of Idah factory water produced over period of 23 days were analyzed using control chart for fraction and number defectives to monitor the proportions of defective and number of defects in the factory water. It was found using P-CHART that central line (CL), upper control limit (UCL) and lower control limit (LCL) were 0.04, 0.07, and 0.01 respectively. Similarly, the result obtained using NP-CHART in monitoring number of defects in Idah factory water production indicated that , lower control limit (LCL), central line and upper control limit (UCL) were 8, 22 and 36 respectively. In both cases all the points were within the control limits. This implies that the production process is in a state of statistical control. It was therefore, recommended that the current components of the production process should be sustained among other things.
这项研究寻求应用“利用质量控制对水质进行数值评估——以尼日利亚科吉州伊达为例”。对500袋Idah厂用水23天的二次数据进行分析，采用不合格品分数和不合格品数量控制图，监测厂用水不合格品比例和不合格品数量。P-CHART结果显示，中心线(CL)、上控制限(UCL)和下控制限(LCL)分别为0.04、0.07和0.01。同样，利用NP-CHART对Idah厂产水缺陷数的监测结果表明，控制下限(LCL)为8，中线(central line)为22，上限(upper control limit)为36。在这两种情况下，所有的点都在控制范围内。这意味着生产过程处于统计控制状态。因此，有人建议，除其他事项外，应维持生产过程的现有组成部分。
{"title":"Numerical Assessment of Water Quality Using Quality Control - A Case Study of Idah, Kogi State, Nigeria","authors":"Wilson Simon Barguma, O. Achimugu","doi":"10.9734/ajpas/2023/v24i2521","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i2521","url":null,"abstract":"This research seeks to apply \"numerical assessment of water quality using quality control - A case study of Idah, Kogi state, Nigeria.” Secondary data of 500 bags of Idah factory water produced over period of 23 days were analyzed using control chart for fraction and number defectives to monitor the proportions of defective and number of defects in the factory water. It was found using P-CHART that central line (CL), upper control limit (UCL) and lower control limit (LCL) were 0.04, 0.07, and 0.01 respectively. Similarly, the result obtained using NP-CHART in monitoring number of defects in Idah factory water production indicated that , lower control limit (LCL), central line and upper control limit (UCL) were 8, 22 and 36 respectively. In both cases all the points were within the control limits. This implies that the production process is in a state of statistical control. It was therefore, recommended that the current components of the production process should be sustained among other things.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81221114","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 : 2023-08-29DOI: 10.9734/ajpas/2023/v24i2520
Solomon Ntukidem, A. Chukwu, O. Oyamakin, C. James, Ignace Habimana-Kabano
The study aimed to examine the trend of the under-five mortality rate in Nigeria from 2003 to 2018 and the determinants of under-five mortality using the Nigeria Demographic and Health Survey (NDHS) data. The data for the study was the Nigeria Demographic and Health Survey data conducted in 2003, 2008, 2013, and 2018. These four surveys were used to study under-five mortality trends within the study period, while machine learning was applied only to the 2018 dataset being the latest in Nigeria. The data were partitioned into training and testing sets. 30% of the dataset was randomly selected for testing, while 70% was used in training the model. Before applying logistic regression and neural networks, the essential under-five mortality variables were first selected using a random forest classifier. The trend showed that the mortality rates were 200.72, 156.86, 128.05, and 132.02 in 2003, 2008, 2013, and 2018 respectively, per 1,000 live births. This result means that one in every five children died before their fifth birthday in 2003, one in six in 2008, one in eight in 2013, and one in seven in 2018. The forecast result indicated that the under-five mortality rate would likely be 102.17 in 2023. The variable importance result of the random forest showed that breastfeeding (when the child was put to the breast after birth) had the highest contribution to under-five mortality. The breakdown of breastfeeding from the logistic regression result showed that delaying the breastfeeding of a child to 6-23 hours in comparison with 0-5 hours after birth increases by 1.4 fold the likelihood of child death. The accuracy of logistic regression (LR) on the test set was 60%, and that of deep neural network (DNN) was 74%, recall (sensitivity) for LR was 63%, and DNN was 75%), Precision (LR=97%, DNN=95), F1 score (LR=76%, DNN=84%) and area under the curve (AUC) (LR=79%, DNN=77%). Both logistic regression and deep neural network models performed very well in discriminative ability and accuracy. The deep neural network had a better performance than the logistic regression.
{"title":"Trend Analysis and Determinants of under-5 Mortality in Nigeria: A Machine Learning Approach","authors":"Solomon Ntukidem, A. Chukwu, O. Oyamakin, C. James, Ignace Habimana-Kabano","doi":"10.9734/ajpas/2023/v24i2520","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i2520","url":null,"abstract":"The study aimed to examine the trend of the under-five mortality rate in Nigeria from 2003 to 2018 and the determinants of under-five mortality using the Nigeria Demographic and Health Survey (NDHS) data. The data for the study was the Nigeria Demographic and Health Survey data conducted in 2003, 2008, 2013, and 2018. These four surveys were used to study under-five mortality trends within the study period, while machine learning was applied only to the 2018 dataset being the latest in Nigeria. The data were partitioned into training and testing sets. 30% of the dataset was randomly selected for testing, while 70% was used in training the model. Before applying logistic regression and neural networks, the essential under-five mortality variables were first selected using a random forest classifier. \u0000The trend showed that the mortality rates were 200.72, 156.86, 128.05, and 132.02 in 2003, 2008, 2013, and 2018 respectively, per 1,000 live births. This result means that one in every five children died before their fifth birthday in 2003, one in six in 2008, one in eight in 2013, and one in seven in 2018. The forecast result indicated that the under-five mortality rate would likely be 102.17 in 2023. The variable importance result of the random forest showed that breastfeeding (when the child was put to the breast after birth) had the highest contribution to under-five mortality. The breakdown of breastfeeding from the logistic regression result showed that delaying the breastfeeding of a child to 6-23 hours in comparison with 0-5 hours after birth increases by 1.4 fold the likelihood of child death. The accuracy of logistic regression (LR) on the test set was 60%, and that of deep neural network (DNN) was 74%, recall (sensitivity) for LR was 63%, and DNN was 75%), Precision (LR=97%, DNN=95), F1 score (LR=76%, DNN=84%) and area under the curve (AUC) (LR=79%, DNN=77%). \u0000Both logistic regression and deep neural network models performed very well in discriminative ability and accuracy. The deep neural network had a better performance than the logistic regression.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89449490","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 : 2023-08-24DOI: 10.9734/ajpas/2023/v24i1517
Floriane Nsabimana, Hellen Waititu, C. Nyakundi
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{"title":"Modeling Dependence using Copula Garch","authors":"Floriane Nsabimana, Hellen Waititu, C. Nyakundi","doi":"10.9734/ajpas/2023/v24i1517","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i1517","url":null,"abstract":"<jats:p>.</jats:p>","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84593500","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 : 2023-08-24DOI: 10.9734/ajpas/2023/v24i2518
Kerui Wu, Minghan Li, Hongyi Ren
This paper analyzes its role in the composition analysis and identification of ancient glass products by flexible use of statistical methods, and emphasizes four statistical methods: systematic clustering algorithm, K-means algorithm, logistic regression model and grey correlation analysis. Taking the C project of CUMCM in 2022 as an example, this paper systematically introduces these four common data classification and statistical methods to classify and analyze the given data. In this paper, suitable chemical components of high potassium and lead barium glass were selected for subdivision, and the specific division methods and results w ere given. The chemical composition of glass relics of unknown category was analyzed to identify their type. The grey correlation matrix of surface weathering of high-potassium cultural relics was obtained, and the correlation degree of chemical components was analyzed. This greatly promotes the composition analysis and identification of chemical components in ancient relics.
{"title":"Component Analysis and Identification of Ancient Glass Products Based on Statistical Methods","authors":"Kerui Wu, Minghan Li, Hongyi Ren","doi":"10.9734/ajpas/2023/v24i2518","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i2518","url":null,"abstract":"This paper analyzes its role in the composition analysis and identification of ancient glass products by flexible use of statistical methods, and emphasizes four statistical methods: systematic clustering algorithm, K-means algorithm, logistic regression model and grey correlation analysis. Taking the C project of CUMCM in 2022 as an example, this paper systematically introduces these four common data classification and statistical methods to classify and analyze the given data. In this paper, suitable chemical components of high potassium and lead barium glass were selected for subdivision, and the specific division methods and results w ere given. The chemical composition of glass relics of unknown category was analyzed to identify their type. The grey correlation matrix of surface weathering of high-potassium cultural relics was obtained, and the correlation degree of chemical components was analyzed. This greatly promotes the composition analysis and identification of chemical components in ancient relics.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90397274","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 : 2023-08-21DOI: 10.9734/ajpas/2023/v24i1516
Uwaeme, O. R., Akpan, N. P., Orumie, U. C.
Due to the ever growing demand for the development of new lifetime distributions to meet the goodness of fit demand of complex datasets, two-parameter distributions has been proposed in recent times. This study therefore aims to contribute to this demand. We propose a new two-parameter lifetime distribution known as the Copoun distribution. Important mathematical properties of the new distribution such as the moments and other related measures, and moment generating function were derived. Finally, the values of the mean, standard deviation, coefficient of variation, skewness, and kurtosis of the Copoun distribution shows that the distribution has the tendency to shift to higher values overall (increasing mean) and narrow around this increased central tendency (decreasing spread, variation and increasing peakedness).
{"title":"The Copoun Distribution and Its Mathematical Properties","authors":"Uwaeme, O. R., Akpan, N. P., Orumie, U. C.","doi":"10.9734/ajpas/2023/v24i1516","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i1516","url":null,"abstract":"Due to the ever growing demand for the development of new lifetime distributions to meet the goodness of fit demand of complex datasets, two-parameter distributions has been proposed in recent times. This study therefore aims to contribute to this demand. We propose a new two-parameter lifetime distribution known as the Copoun distribution. Important mathematical properties of the new distribution such as the moments and other related measures, and moment generating function were derived. Finally, the values of the mean, standard deviation, coefficient of variation, skewness, and kurtosis of the Copoun distribution shows that the distribution has the tendency to shift to higher values overall (increasing mean) and narrow around this increased central tendency (decreasing spread, variation and increasing peakedness).","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89352608","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 : 2023-08-16DOI: 10.9734/ajpas/2023/v24i1515
K. Dozie, Stephen O. Ihekuna
This study discusses the effect of missing data on Buys-Ballot estimates of trend parameters and seasonal indices. The method adopted in this study is based on the row, column and overall means of the time series arranged in a Buys-Ballot table with m rows and s columns. The method assumes that (1) Only data missing at one point at a time in the Buys-Ballot table is considered. (2) the trending curve is either linear or exponential (3) the decomposition method is either additive or mixed. The article shows that, the estimation of the missing data as they occur consecutively with the errors being normally distributed. Result indicates that, under the stated assumptions, the differences between trend parameters in the presence and absence are insignificant, while that of seasonal indices are significant.
{"title":"The Effect of Missing Data on Estimates of Exponential Trend-Cycle and Seasonal Components in Time Series: Additive Case","authors":"K. Dozie, Stephen O. Ihekuna","doi":"10.9734/ajpas/2023/v24i1515","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i1515","url":null,"abstract":"This study discusses the effect of missing data on Buys-Ballot estimates of trend parameters and seasonal indices. The method adopted in this study is based on the row, column and overall means of the time series arranged in a Buys-Ballot table with m rows and s columns. The method assumes that (1) Only data missing at one point at a time in the Buys-Ballot table is considered. (2) the trending curve is either linear or exponential (3) the decomposition method is either additive or mixed. The article shows that, the estimation of the missing data as they occur consecutively with the errors being normally distributed. Result indicates that, under the stated assumptions, the differences between trend parameters in the presence and absence are insignificant, while that of seasonal indices are significant.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89002490","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 : 2023-08-12DOI: 10.9734/ajpas/2023/v24i1514
P. M. Medugu, Chajire Buba Pwalakino, Yaska Mutah, Dampha Gandada
Determining whether sample differences in central tendency represent real differences in parent populations is a typical issue in applied research. If the conditions of normality, homogeneity of variance, and independence of errors are met, the t-test can be used for a two sample instance (two groups). However, the nonparametric equivalent is taken into account when these presumptions are violated. In order to determine which test is most effective and resilient to a certain distribution and sample size when samples are obtained from separate populations, the study compares the effectiveness and sensitivity of power of four test statistics. These tests were examined under normal and some skew distributions at sample size of 5, 10, 15, 20, 25, 30, 40, 45, and 50 using simulation. The most effective test for a given distribution and sample size was chosen using the power of each test computed. The study found that when data are taken from a normal distribution and tested at small and large sample sizes, respectively, the t-test and Welch test have the highest power, while the Median is the most resistant to uniform and gamma, and the Man-Whitney test is the most reliable for exponential distributions.
{"title":"An Empirical Comparison of Power of Two Independent Population Tests under Different Underlined Distributions","authors":"P. M. Medugu, Chajire Buba Pwalakino, Yaska Mutah, Dampha Gandada","doi":"10.9734/ajpas/2023/v24i1514","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i1514","url":null,"abstract":"Determining whether sample differences in central tendency represent real differences in parent populations is a typical issue in applied research. If the conditions of normality, homogeneity of variance, and independence of errors are met, the t-test can be used for a two sample instance (two groups). However, the nonparametric equivalent is taken into account when these presumptions are violated. In order to determine which test is most effective and resilient to a certain distribution and sample size when samples are obtained from separate populations, the study compares the effectiveness and sensitivity of power of four test statistics. These tests were examined under normal and some skew distributions at sample size of 5, 10, 15, 20, 25, 30, 40, 45, and 50 using simulation. The most effective test for a given distribution and sample size was chosen using the power of each test computed. The study found that when data are taken from a normal distribution and tested at small and large sample sizes, respectively, the t-test and Welch test have the highest power, while the Median is the most resistant to uniform and gamma, and the Man-Whitney test is the most reliable for exponential distributions.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88864013","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 : 2023-08-11DOI: 10.9734/ajpas/2023/v24i1513
Miriam Sitienei, A. Anapapa, A. Otieno
Artificial Intelligence (AI) is the human-like intelligence imbued in machines so that they can perform tasks that normally require human intelligence. Machine learning is an AI technique which carries on the concepts of predictive analytics with one important distinction: the AI system can make assumptions, test hypotheses, and learn independently. XGBoost, Extreme gradient boosting, is a popular machine-learning library for regression tasks. It implements the gradient-boosting decision tree algorithm, which combines several feeble decision trees to produce a robust predictive model. In Boosted Trees, boosting is the process of transforming poor learners into strong learners. It is an ensemble method; a weak learner is a classifier with a low correlation with classification, whereas a strong learner has a high correlation. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. Crop yield measures the seeds or grains produced by a particular plot of land. Typically, it is expressed in kilograms per hectare, bushels per acre, or sacks per acre. This study predicted maize yield in Uasin Gishu, a county in Kenya, using XGBOOST regression algorithm of machine learning. The regression model used the mixed-methods research design, the survey employed well-structured questionnaires comprising of quantitative and qualitative variables, directly administered to selected representative farmers from 30 clustered wards. The questionnaire comprised 30 variables related to maize production from 900 randomly selected maize farmers distributed across 30 wards. XGBOOST machine learning regression model was fitted, and it could predict maize yield and identify the top features or variables that affect maize yield. The model was evaluated using regression metrics Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), which values were 0.4563, 0.2082, 25.2700 and 0.3532, respectively. This algorithm was recommended for maize yield prediction.
{"title":"Application of XGBoost Regression in Maize Yield Prediction","authors":"Miriam Sitienei, A. Anapapa, A. Otieno","doi":"10.9734/ajpas/2023/v24i1513","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v24i1513","url":null,"abstract":"Artificial Intelligence (AI) is the human-like intelligence imbued in machines so that they can perform tasks that normally require human intelligence. Machine learning is an AI technique which carries on the concepts of predictive analytics with one important distinction: the AI system can make assumptions, test hypotheses, and learn independently. XGBoost, Extreme gradient boosting, is a popular machine-learning library for regression tasks. It implements the gradient-boosting decision tree algorithm, which combines several feeble decision trees to produce a robust predictive model. In Boosted Trees, boosting is the process of transforming poor learners into strong learners. It is an ensemble method; a weak learner is a classifier with a low correlation with classification, whereas a strong learner has a high correlation. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. Crop yield measures the seeds or grains produced by a particular plot of land. Typically, it is expressed in kilograms per hectare, bushels per acre, or sacks per acre. This study predicted maize yield in Uasin Gishu, a county in Kenya, using XGBOOST regression algorithm of machine learning. The regression model used the mixed-methods research design, the survey employed well-structured questionnaires comprising of quantitative and qualitative variables, directly administered to selected representative farmers from 30 clustered wards. The questionnaire comprised 30 variables related to maize production from 900 randomly selected maize farmers distributed across 30 wards. XGBOOST machine learning regression model was fitted, and it could predict maize yield and identify the top features or variables that affect maize yield. The model was evaluated using regression metrics Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), which values were 0.4563, 0.2082, 25.2700 and 0.3532, respectively. This algorithm was recommended for maize yield prediction.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90975580","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 : 2023-08-10DOI: 10.9734/ajpas/2023/v23i4512
Kouadio Jean Claude Kouaho, Koffi Yao Modeste N'zi, I. Adoubi
The branching processes form a configuration for modeling tumor cells. Faced with unobserved data on dormant cells, inference based on the branching process is not easy to achieve. In large populations, we construct a new framework for estimating dormant cells and tumor dormancy rates. This inference uses of control theory is based on deterministic process statistics approximating branching process in large populations. Precisely, we use an auxiliary system called an observer whose solutions tend exponentially towards those of the limit deterministic model. This observer uses only available measurable data on tumor cells and provides estimates of the number of dormant cells. In addition, the constructed observer does not use the parameter of the generally unknown tumor dormancy rate. We also derive a method to estimate it using the estimated states. We apply this estimation method using simulated data from the branching process.
{"title":"Estimation of Dormant Cell Population in Cancer Patients: A New Approach","authors":"Kouadio Jean Claude Kouaho, Koffi Yao Modeste N'zi, I. Adoubi","doi":"10.9734/ajpas/2023/v23i4512","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i4512","url":null,"abstract":"The branching processes form a configuration for modeling tumor cells. Faced with unobserved data on dormant cells, inference based on the branching process is not easy to achieve. In large populations, we construct a new framework for estimating dormant cells and tumor dormancy rates. This inference uses of control theory is based on deterministic process statistics approximating branching process in large populations. Precisely, we use an auxiliary system called an observer whose solutions tend exponentially towards those of the limit deterministic model. This observer uses only available measurable data on tumor cells and provides estimates of the number of dormant cells. In addition, the constructed observer does not use the parameter of the generally unknown tumor dormancy rate. We also derive a method to estimate it using the estimated states. We apply this estimation method using simulated data from the branching process.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88640536","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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