A. Almarashi, Mohammad S. Alzahrani, Ibrahim M. Hawthari, Meshaal S. Alanazi, Ibrahim Y. Alasiri, Meshal M. Alqurayshah, Ali A. Almalki, Mohanad F. Allehyani, Raghid A. Mahrous, Yahia S. Alamri, Khushnoor Khan
{"title":"TRANSLATION AND VALIDATION OF THE ORGANIZATIONAL COMMITMENT SCALE: SAUDIAN CULTURAL CONTEXT","authors":"A. Almarashi, Mohammad S. Alzahrani, Ibrahim M. Hawthari, Meshaal S. Alanazi, Ibrahim Y. Alasiri, Meshal M. Alqurayshah, Ali A. Almalki, Mohanad F. Allehyani, Raghid A. Mahrous, Yahia S. Alamri, Khushnoor Khan","doi":"10.17654/0973514324008","DOIUrl":"https://doi.org/10.17654/0973514324008","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"41 8","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945642","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}
Calwin S. Parthibaraj, Freeda M. Selvaraj, Anna P Joseph
{"title":"TUMOR CELL CLASSIFICATION: AN APPLICATION OF MULTIVARIATE DATA PROCESSING METHOD","authors":"Calwin S. Parthibaraj, Freeda M. Selvaraj, Anna P Joseph","doi":"10.17654/0973514324007","DOIUrl":"https://doi.org/10.17654/0973514324007","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"17 8","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945904","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}
N. Khan, Azhar Ali Janjua, Muhammad Aslam, P. Jeyadurga, S. Balamurali, M. Albassam
{"title":"DESIGN OF AND CONTROL CHARTS FOR IMPRECISE DATA WITH MEDICAL APPLICATION","authors":"N. Khan, Azhar Ali Janjua, Muhammad Aslam, P. Jeyadurga, S. Balamurali, M. Albassam","doi":"10.17654/0973514324006","DOIUrl":"https://doi.org/10.17654/0973514324006","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"1 1","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139209571","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}
C. B. Praseeja, C. B. Prasanth, C. Subramanian, T. Unnikrishnan
{"title":"CHARACTERISTICS OF SRIMIN-H DISTRIBUTION AND ITS BIOMEDICAL APPLICATION","authors":"C. B. Praseeja, C. B. Prasanth, C. Subramanian, T. Unnikrishnan","doi":"10.17654/0973514324005","DOIUrl":"https://doi.org/10.17654/0973514324005","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"12 1","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230838","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}
Afsana Al Sharmin, H. S. Zulkafli, Nazihah Mohamed Ali
{"title":"ESTABLISHING CUT-OFF POINTS FOR CONSISTENCY IN REPORTING HYPOGLYCEMIA SYMPTOMS AMONG DIABETES PATIENTS","authors":"Afsana Al Sharmin, H. S. Zulkafli, Nazihah Mohamed Ali","doi":"10.17654/0973514324004","DOIUrl":"https://doi.org/10.17654/0973514324004","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"32 1","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252157","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}
This is a descriptive, cross-sectional study to analyze the effect of alcohol and smoking in the hemoglobin present in blood and determining the other factors that affect it. The data was obtained from the national health insurance service in Korea. The multiple linear regression model was performed on the sample size of 65535 individuals, which contain adults aged between 20 to 85 years of both males and females in Korea. This sample covers people who smoke and drink during their lifetime. There is a statistically significant effect of the explanatory variables (Sex, Age, Height, Weight, Smoking state, Drinking state) on the dependent variable (Hemoglobin), with F-stat (10325.983) and P-value (0.000) at 5% level of significant. The variance inflation factor (VIF) ranged between (1.280 to 3.327); is less than 5; which means that there is no collinearity. Also, the R squared (0.486) is less than Durbin Watson statistic (2.006) which means this model is not spurious suggesting that there is no autocorrelation, or partial correlation in the data. The explanatory variables explain 48.6% of the total variation in hemoglobin levels in the blood. Received: September 7, 2023 Accepted: November 2, 2023
{"title":"STATISTICAL ANALYSIS STUDYING THE FACTORS AFFECTING HEMOGLOBIN","authors":"Maysoon A. Sultan","doi":"10.17654/0973514324003","DOIUrl":"https://doi.org/10.17654/0973514324003","url":null,"abstract":"This is a descriptive, cross-sectional study to analyze the effect of alcohol and smoking in the hemoglobin present in blood and determining the other factors that affect it. The data was obtained from the national health insurance service in Korea. The multiple linear regression model was performed on the sample size of 65535 individuals, which contain adults aged between 20 to 85 years of both males and females in Korea. This sample covers people who smoke and drink during their lifetime. There is a statistically significant effect of the explanatory variables (Sex, Age, Height, Weight, Smoking state, Drinking state) on the dependent variable (Hemoglobin), with F-stat (10325.983) and P-value (0.000) at 5% level of significant. The variance inflation factor (VIF) ranged between (1.280 to 3.327); is less than 5; which means that there is no collinearity. Also, the R squared (0.486) is less than Durbin Watson statistic (2.006) which means this model is not spurious suggesting that there is no autocorrelation, or partial correlation in the data. The explanatory variables explain 48.6% of the total variation in hemoglobin levels in the blood. Received: September 7, 2023 Accepted: November 2, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342677","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}
Using the example of the skin glands of adult female Norway rats, a matrix of gray shades has been developed using the Python programming language, each element of which corresponds to a certain level of histoenzymatic activity. The matrix is based on the transformation of the sign form of enzyme activity into RGB coordinates, which formed the basis of an array comprising four enzymes (acid phosphatase, alkaline phosphatase, adenosine triphosphatase and peroxidase) for five topographic areas (nape, mouth corners, upper eyelids, anal area and soles of paws). The resulting matrix can give additional visualization to the results, and can also be used in comparative data analysis to solve various biological problems. Received: August 27, 2023Accepted: October 14, 2023
{"title":"MATRIX VISUALIZATION OF THE DEGREES OF HISTOCHEMICAL ACTIVITY OF ENZYMES IN THE SKIN GLANDS OF NORWAY RATS","authors":"A. B. Kiladze, N. K. Dzhemukhadze","doi":"10.17654/0973514324002","DOIUrl":"https://doi.org/10.17654/0973514324002","url":null,"abstract":"Using the example of the skin glands of adult female Norway rats, a matrix of gray shades has been developed using the Python programming language, each element of which corresponds to a certain level of histoenzymatic activity. The matrix is based on the transformation of the sign form of enzyme activity into RGB coordinates, which formed the basis of an array comprising four enzymes (acid phosphatase, alkaline phosphatase, adenosine triphosphatase and peroxidase) for five topographic areas (nape, mouth corners, upper eyelids, anal area and soles of paws). The resulting matrix can give additional visualization to the results, and can also be used in comparative data analysis to solve various biological problems. Received: August 27, 2023Accepted: October 14, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"16 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935133","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}
Selecting model for classifying target correctly is important. Logistic regression (LR), K-nearest neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB) are base models in classifying target. Tracking ensemble is the method for comparing accuracy in machine learning. Datasets are generated by a code of Python as recommended by Brownlee [1]. Five sample sizes of 1,000, 3,000, 5,000, 7,000, and 10,000 are selected. The number of features is 20 having informative and redundant features, respectively, as 15 and 5. The result shows that support vector machine (SVM) has the highest mean of accuracy and the lowest coefficient of variation of accuracy in all sample sizes. Naïve Bayes (NB) has the lowest mean of accuracy and the highest coefficient of variation of accuracy in all sample sizes. It is recommended to select support vector machine (SVM) for classifying target. Received: August 13, 2023Accepted: October 9, 2023
{"title":"COMPARING ACCURACY OF LOGISTIC REGRESSION, K-NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, AND NAÏVE BAYES MODELS USING TRACKING ENSEMBLE MACHINE LEARNING","authors":"Kuntoro Kuntoro","doi":"10.17654/0973514324001","DOIUrl":"https://doi.org/10.17654/0973514324001","url":null,"abstract":"Selecting model for classifying target correctly is important. Logistic regression (LR), K-nearest neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB) are base models in classifying target. Tracking ensemble is the method for comparing accuracy in machine learning. Datasets are generated by a code of Python as recommended by Brownlee [1]. Five sample sizes of 1,000, 3,000, 5,000, 7,000, and 10,000 are selected. The number of features is 20 having informative and redundant features, respectively, as 15 and 5. The result shows that support vector machine (SVM) has the highest mean of accuracy and the lowest coefficient of variation of accuracy in all sample sizes. Naïve Bayes (NB) has the lowest mean of accuracy and the highest coefficient of variation of accuracy in all sample sizes. It is recommended to select support vector machine (SVM) for classifying target. Received: August 13, 2023Accepted: October 9, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136382199","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}
Duckworth’s test is a well-known non-parametric statistical test used for comparing the medians of two populations. However, the conventional Duckworth’s test, based on classical statistics, is inadequate when dealing with data originating from neutrosophic populations. This paper presents a modified version of Duckworth’s test, specifically designed for neutrosophic statistics. This novel approach enables the application of Duckworth’s test to imprecise, uncertain, or data recorded in indeterminate intervals. The proposed test statistic under neutrosophic statistics is introduced and applied to real-world Covid-19 data. Through comprehensive analysis and simulation studies, the efficacy of the proposed Duckworth’s test under neutrosophic statistics is demonstrated to surpass that of the existing Duckworth’s test under classical statistics. Received: August 7, 2023Accepted: September 25, 2023
{"title":"ANALYSIS OF CORONA PATIENTS USING UNCERTAINTY-BASED NON-PARAMETRIC MEDIAN TEST","authors":"Muhammad Aslam, Muhammad Saleem","doi":"10.17654/0973514323018","DOIUrl":"https://doi.org/10.17654/0973514323018","url":null,"abstract":"Duckworth’s test is a well-known non-parametric statistical test used for comparing the medians of two populations. However, the conventional Duckworth’s test, based on classical statistics, is inadequate when dealing with data originating from neutrosophic populations. This paper presents a modified version of Duckworth’s test, specifically designed for neutrosophic statistics. This novel approach enables the application of Duckworth’s test to imprecise, uncertain, or data recorded in indeterminate intervals. The proposed test statistic under neutrosophic statistics is introduced and applied to real-world Covid-19 data. Through comprehensive analysis and simulation studies, the efficacy of the proposed Duckworth’s test under neutrosophic statistics is demonstrated to surpass that of the existing Duckworth’s test under classical statistics. Received: August 7, 2023Accepted: September 25, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"56 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513055","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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