Pub Date : 2021-04-09DOI: 10.47750/CIBG.2021.27.04.015
Bapatu Siva Kumar Reddy, P. Vardhan
Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.
{"title":"Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier","authors":"Bapatu Siva Kumar Reddy, P. Vardhan","doi":"10.47750/CIBG.2021.27.04.015","DOIUrl":"https://doi.org/10.47750/CIBG.2021.27.04.015","url":null,"abstract":"Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73457899","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 : 2021-04-09DOI: 10.47750/CIBG.2021.27.04.021
T. Reddy, A. Deepak
Aim: The current and voltage characteristics of FinFET and single gate MOSFET are simulated by varying their oxide thickness ranging from 2 nm to 20 nm. Materials and Methods: The electrical conductance of FINFET (n= 320) was compared with MOSFET (n=320) by varying oxide thickness ranging from 2 nm to 20 nm in the NANO HUB tool simulation environment. Results: FINFET has significantly higher conductance (2.66*10-4 mho P<0.05) than single gate MOSFET (1.64*10-4 mho). The optimal thickness for maximum conductivity was 2nm for FINFET, and 2 nm for MOSFET. Conclusion: Within the limits of this study, FINFET with oxide thickness of 2 nm offers the best conductivity.
{"title":"Simulation and Comparison of Voltage and Current Characteristics of Novel Finfet by Varying its Oxide Thickness with Single Gate Mosfet for Improved Conductivity","authors":"T. Reddy, A. Deepak","doi":"10.47750/CIBG.2021.27.04.021","DOIUrl":"https://doi.org/10.47750/CIBG.2021.27.04.021","url":null,"abstract":"Aim: The current and voltage characteristics of FinFET and single gate MOSFET are simulated by varying their oxide thickness ranging from 2 nm to 20 nm. Materials and Methods: The electrical conductance of FINFET (n= 320) was compared with MOSFET (n=320) by varying oxide thickness ranging from 2 nm to 20 nm in the NANO HUB tool simulation environment. Results: FINFET has significantly higher conductance (2.66*10-4 mho P<0.05) than single gate MOSFET (1.64*10-4 mho). The optimal thickness for maximum conductivity was 2nm for FINFET, and 2 nm for MOSFET. Conclusion: Within the limits of this study, FINFET with oxide thickness of 2 nm offers the best conductivity.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82378368","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 : 2021-04-09DOI: 10.47750/CIBG.2021.27.04.011
P. Priya, Jenila Rani.D
Aim: The Present study was designed to compare the phytochemical screening of root and bark with leaf of Cardiospermum halicacabum. Materials and Methods: Samples were taken leaf (N=24) root and bark (N=24) based on the total sample size using clinical.com. The leaf, root and bark extract were collected. The phytochemicals were extracted by sequential extraction using three solvents methanol, ethanol and acetone. The quantification of flavonoids and phenols was performed by using Folin-Ciocalteu and quercetin as standard. Quantification of tannins was determined by using an insoluble polyvinyl-polypyrrolidone (PVPP) as standard. Results: Statistical analysis showed that methanol extract of root (0.49mg/ml) has highest phenolic content and acetone extract of root has highest tannin (0.64mg/ml) and flavonoid (1.18mg/ml) content when compared with leaf and bark. There appears to be a statistically significant difference in the mean of root when compared with leaf and bark (p<0.01, independent samples). Conclusion: In this study root appears to have better phytochemical and phenol content when compared with the content in leaf and bark.
{"title":"Comparative Study on Phytochemical Screening of Root and Bark with Leaf of Cardiospermum Halicacabum","authors":"P. Priya, Jenila Rani.D","doi":"10.47750/CIBG.2021.27.04.011","DOIUrl":"https://doi.org/10.47750/CIBG.2021.27.04.011","url":null,"abstract":"Aim: The Present study was designed to compare the phytochemical screening of root and bark with leaf of Cardiospermum halicacabum. Materials and Methods: Samples were taken leaf (N=24) root and bark (N=24) based on the total sample size using clinical.com. The leaf, root and bark extract were collected. The phytochemicals were extracted by sequential extraction using three solvents methanol, ethanol and acetone. The quantification of flavonoids and phenols was performed by using Folin-Ciocalteu and quercetin as standard. Quantification of tannins was determined by using an insoluble polyvinyl-polypyrrolidone (PVPP) as standard. Results: Statistical analysis showed that methanol extract of root (0.49mg/ml) has highest phenolic content and acetone extract of root has highest tannin (0.64mg/ml) and flavonoid (1.18mg/ml) content when compared with leaf and bark. There appears to be a statistically significant difference in the mean of root when compared with leaf and bark (p<0.01, independent samples). Conclusion: In this study root appears to have better phytochemical and phenol content when compared with the content in leaf and bark.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88600576","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 : 2021-04-09DOI: 10.47750/CIBG.2021.27.04.019
M. PranathiSaiPrathyusha, Dr. K. Malathi
Aim: Recognizing the Handwritten Digits to find the best accuracy using Machine learning methods such as Connectionist Temporal Classification (CTC) and Convolutional Neural Network (CNN). Methods and Materials: Accuracy and loss are performed with the MNIST dataset from the Keras library. The two groups Connectionist Temporal classification (N=20) and Convolutional Neural Network algorithms (N=20). Results: A CNN is used for recognizing the innovative handwritten digits. The accuracy is analysed based on correctness of the exact digits of 92.67% where the CTC has the accuracy of 89.07%. The two algorithms CNN and CTC are statistically satisfied with the independent sample T-Test (=.001) value (p<0.05) with confidence level of 95%. Conclusion: Recognizing the handwritten digits significantly seems to be better in CNN than CTC.
{"title":"An Innovative Method to Analyse the Prediction Rate and Accuracy for Handwritten Digit Recognition with Convolutional Neural Network Over Connection Temporal Classification","authors":"M. PranathiSaiPrathyusha, Dr. K. Malathi","doi":"10.47750/CIBG.2021.27.04.019","DOIUrl":"https://doi.org/10.47750/CIBG.2021.27.04.019","url":null,"abstract":"Aim: Recognizing the Handwritten Digits to find the best accuracy using Machine learning methods such as Connectionist Temporal Classification (CTC) and Convolutional Neural Network (CNN). Methods and Materials: Accuracy and loss are performed with the MNIST dataset from the Keras library. The two groups Connectionist Temporal classification (N=20) and Convolutional Neural Network algorithms (N=20). Results: A CNN is used for recognizing the innovative handwritten digits. The accuracy is analysed based on correctness of the exact digits of 92.67% where the CTC has the accuracy of 89.07%. The two algorithms CNN and CTC are statistically satisfied with the independent sample T-Test (=.001) value (p<0.05) with confidence level of 95%. Conclusion: Recognizing the handwritten digits significantly seems to be better in CNN than CTC.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89243158","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 : 2021-04-09DOI: 10.47059/ALINTERI/V36I1/AJAS21086
M. Rajesh, Dr. K. Malathi
Aim: Predicting the Heartdiseases using medical parameters of cardiac patients to get a good accuracy rate using machine learning methods like innovative Decision Tree (DT) algorithm. Materials and Methods: Supervised Machine learning Techniques with innovative Decision Tree (N = 20) and K Nearest Neighbour (KNN) (N = 20) are performed with five different datasets at each time to record five samples. Results: The Decision Tree is used to predict heart disease with the help of various medical conditions, the accuracy is achieved for DT is 98% and KNN is 72.2%. The two algorithms Decision Tree and KNN are statistically insignificant (=.737) with the independent sample T-Test value (p<0.005) with a confidence level of 95%. Conclusion: Prediction and classification of heart disease significantly seem to be better in DT than KNN.
{"title":"An Innovative Method for Predicting and Classifying Inadequate Accuracy in Heart Disease by Using Decision Tree with K-Nearest Neighbors Algorithm","authors":"M. Rajesh, Dr. K. Malathi","doi":"10.47059/ALINTERI/V36I1/AJAS21086","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I1/AJAS21086","url":null,"abstract":"Aim: Predicting the Heartdiseases using medical parameters of cardiac patients to get a good accuracy rate using machine learning methods like innovative Decision Tree (DT) algorithm. Materials and Methods: Supervised Machine learning Techniques with innovative Decision Tree (N = 20) and K Nearest Neighbour (KNN) (N = 20) are performed with five different datasets at each time to record five samples. Results: The Decision Tree is used to predict heart disease with the help of various medical conditions, the accuracy is achieved for DT is 98% and KNN is 72.2%. The two algorithms Decision Tree and KNN are statistically insignificant (=.737) with the independent sample T-Test value (p<0.005) with a confidence level of 95%. Conclusion: Prediction and classification of heart disease significantly seem to be better in DT than KNN.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80054537","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 : 2021-04-09DOI: 10.47059/alinteri/v36i1/ajas21072
N. Pravallika, Dr.K. Sashi Rekha
Aim: To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p<0.05 Independent Sample T-test). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19.
{"title":"Comparative Analysis of Identifying Accuracy of Online Misinformation of Covid-19 Using SVM Algorithm with Decision Tree Classification","authors":"N. Pravallika, Dr.K. Sashi Rekha","doi":"10.47059/alinteri/v36i1/ajas21072","DOIUrl":"https://doi.org/10.47059/alinteri/v36i1/ajas21072","url":null,"abstract":"Aim: To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p<0.05 Independent Sample T-test). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87959124","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 : 2021-04-09DOI: 10.47750/CIBG.2021.27.04.018
G. Reddy, R. Nanmaran, G. Paramasivam
Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).
{"title":"Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter","authors":"G. Reddy, R. Nanmaran, G. Paramasivam","doi":"10.47750/CIBG.2021.27.04.018","DOIUrl":"https://doi.org/10.47750/CIBG.2021.27.04.018","url":null,"abstract":"Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"285 1-2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78478411","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 : 2021-04-04DOI: 10.47059/ALINTERI/V36I1/AJAS21050
Sarvepalli Sailesh Babu, G. Gulothungan
Aim: In this paper, analysis of ventricular arrhythmias are made with respect to the Calcium (Ca2+) ion channel dysfunction (generating improper electrical activity). Many cases can make arrhythmias and most of them are related to generation or conduction of Action Potential (AP) in cardiac myocardium. Materials and method: Human ventricular cell based on the model of the human endocardial cell by Ten Tusscher (TT). The TT model data is modified based on the experimental data of Han, describing the properties of Ca2+ currents and its channel dynamics in human ventricular cells. Euler integration method is used to analyse the human ventricular model for different channel failure conditions in the same group of 50 samples. Results: Our research findings focus with respect to normal and deviant Ca2+ conductance (GCaL). The normal GCaL 0.000175nS and deviant GCaL increase like (10%=0.000218nS, 25%=0.000182nS, 50%=0.000262nS and 100%=0.000350nS) having the normal AP average value ranges between 26.0mV to -74.0mV and 12.0mV to -88.0mV for 10% GCaL, 18.0mV to -78.0mV for 25% GCaL, 18.0mV to -78.0mV for 50% GCaL and 21.0mV to -75.0mV for 100% GCaL deviants. Similarly, deviant GCaL decrease like (10%=0.000158nS, 25%=0.000131nS, 50%=0.000088nS and 100%=0.000001nS) having the deviant AP mean values ranges between 10.0mV to -90.0mV for 10% GCaL, 7.0mV to -92.0mV for 25% GCaL, -9.0mV to -96.0mV for 50% GCaL and -51.0mV to 100.0mV for 100% GCaL. Simultaneously its membrane Ca2+ currents are having significant variations. Conclusion: The results show clearly for the affirmation for Excitation and Coupling (EC) failures. EC failures lead to a systole phase that is more prolonged, that in turns to produce QT syndrome and hypertrophic cardiomyopathy.
{"title":"Computational Model based Approach to Analyse Calcium (Ca2+) Channel in Ventricular Cells for Normal and Cardiac Arrhythmias Using Euler Integration Method – A Simulation Study","authors":"Sarvepalli Sailesh Babu, G. Gulothungan","doi":"10.47059/ALINTERI/V36I1/AJAS21050","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I1/AJAS21050","url":null,"abstract":"Aim: In this paper, analysis of ventricular arrhythmias are made with respect to the Calcium (Ca2+) ion channel dysfunction (generating improper electrical activity). Many cases can make arrhythmias and most of them are related to generation or conduction of Action Potential (AP) in cardiac myocardium. Materials and method: Human ventricular cell based on the model of the human endocardial cell by Ten Tusscher (TT). The TT model data is modified based on the experimental data of Han, describing the properties of Ca2+ currents and its channel dynamics in human ventricular cells. Euler integration method is used to analyse the human ventricular model for different channel failure conditions in the same group of 50 samples. Results: Our research findings focus with respect to normal and deviant Ca2+ conductance (GCaL). The normal GCaL 0.000175nS and deviant GCaL increase like (10%=0.000218nS, 25%=0.000182nS, 50%=0.000262nS and 100%=0.000350nS) having the normal AP average value ranges between 26.0mV to -74.0mV and 12.0mV to -88.0mV for 10% GCaL, 18.0mV to -78.0mV for 25% GCaL, 18.0mV to -78.0mV for 50% GCaL and 21.0mV to -75.0mV for 100% GCaL deviants. Similarly, deviant GCaL decrease like (10%=0.000158nS, 25%=0.000131nS, 50%=0.000088nS and 100%=0.000001nS) having the deviant AP mean values ranges between 10.0mV to -90.0mV for 10% GCaL, 7.0mV to -92.0mV for 25% GCaL, -9.0mV to -96.0mV for 50% GCaL and -51.0mV to 100.0mV for 100% GCaL. Simultaneously its membrane Ca2+ currents are having significant variations. Conclusion: The results show clearly for the affirmation for Excitation and Coupling (EC) failures. EC failures lead to a systole phase that is more prolonged, that in turns to produce QT syndrome and hypertrophic cardiomyopathy.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79436783","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 : 2021-02-05DOI: 10.47059/alinteri/v36i1/ajas21060
K. Harini, K. Rekha
Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.
{"title":"Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students","authors":"K. Harini, K. Rekha","doi":"10.47059/alinteri/v36i1/ajas21060","DOIUrl":"https://doi.org/10.47059/alinteri/v36i1/ajas21060","url":null,"abstract":"Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75422262","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 : 2021-01-01DOI: 10.47059/alinteri/v36i1/ajas21043
Muxitdinov Shuhrat Ziyavitdinovich, Abdullaeva Madina Kamilovna, Jaloliddinov Anvar Jaloliddin Ugli, Begmatova Shakhnoza Adxamovna, Turdikulov Farrukh Ravshanjon Ogli
This article describes the impact of the COVID-19 pandemic on the agricultural sector and food supply chain in Uzbekistan, theoretical aspects of the necessary measures to be taken to provide food to the domestic and foreign markets during epidemics, pandemics and quarantines. The article also provides the necessary recommendations for the widespread implementation of transformation processes through the digitalization of manufacturing enterprises while ensuring supply chains in accordance with the introduction of innovative technologies into the economy.
{"title":"Covid-19 Pandemia in Uzbekistan Agriculture and its Impact on the Supply Chain","authors":"Muxitdinov Shuhrat Ziyavitdinovich, Abdullaeva Madina Kamilovna, Jaloliddinov Anvar Jaloliddin Ugli, Begmatova Shakhnoza Adxamovna, Turdikulov Farrukh Ravshanjon Ogli","doi":"10.47059/alinteri/v36i1/ajas21043","DOIUrl":"https://doi.org/10.47059/alinteri/v36i1/ajas21043","url":null,"abstract":"This article describes the impact of the COVID-19 pandemic on the agricultural sector and food supply chain in Uzbekistan, theoretical aspects of the necessary measures to be taken to provide food to the domestic and foreign markets during epidemics, pandemics and quarantines. The article also provides the necessary recommendations for the widespread implementation of transformation processes through the digitalization of manufacturing enterprises while ensuring supply chains in accordance with the introduction of innovative technologies into the economy.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76731861","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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