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Abstract : Effect of Different Synthetic Pesticides Against Pink Bollworm Pectinophora gossypiella (Saund.) On Bt. and non-Bt. Cotton Crop
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Abstract: The field studies were conducted at the farmer’s field in 2015-2016 to determine the effect of three different insecticides (triazon, radiant and polytrin C) on Bt. and non-Bt. cotton varieties against pink bollworm. The results revealed that triazon was observed the most effective pesticide against PBW on both cotton varieties. The mortality reduction percent of 33.99 to 30.45% was recorded at triazon, 27.72 to 26.95% at radiant and 24.68 to 14.48% at polytrin C respectively, in 2015. However, in 2016 the mortality reduction percent decreased but effective trend of these selected pesticides were observed same with mortality reduction percent of 28.15 to 25.46% at triazon, 21.95 to 23.52% at radiant and 19.96 to 16.37% at polytrin C in Bt. and non-Bt. cotton varieties. In present investigation, triazon was observed the most effective pesticide than radiant and polytrin C on larvae of PBW in both Bt. and non-Bt. varieties. Keywords: Pesticides, pink bollworm, Bt. and non-Bt. Cotton. |
Abstract : Classification Techniques in Machine Learning: Applications and Issues
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Abstract: Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques. Later we discuss some major types of classification method including Bayesian networks, decision tree induction, k-nearest neighbor classifier and Support Vector Machines (SVM) with their strengths, weaknesses, potential applications and issues with their available solution. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning. This work will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods. Keywords: Machine learning, classification, classification review, classification applications, classification algorithms, classification issues. |
Abstract : Analyzing Diabetes Datasets using Data Mining
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Abstract: Data mining techniques explore critical information in various domains (for example in CRM (customer relationship management), HR (Human Resource), GIS (Geographic Information System) etc.) but most importantly in medical domain. In medical domain, data mining can assist in minimizing the risk of developing some stereotyped diseases such as cancer, heart diseases, diabetes etc. In this paper, authors have focused data of Diabetic patients. Diabetic patient’s body lacks ability to manage the glucose level in blood which can affect the other body mechanism. This can lead to the dysfunctioning of other physiological and psychological parameters such as reduced weight, skin folding. These parameters may be a valuable data source for the research. Diabetes mellitus placed 4th among Noncommunicable diseases-NCDs, caused 1.5 million global deaths each year worldwide [1]. The increase in digital information has elevated numerous challenges especially when it comes to automated content analysis and to make use of some machine learning techniques to aid mankind for predicting the non-communicable diseases like diabetics. . In this research different classifying algorithms such as Naïve bayes, MLP, J.48, ZeroR, Random Forest, and Regression were applied to depict the result. The conducted research aims to extract knowledge from the given set of data and to generate comprehensive and intelligent results. Keywords: Data mining, Classification, Algorithm, Diabtes MelitusType II. |
Abstract : Huge and Real-Time Database Systems: A Comparative Study and Review for SQL Server 2016, Oracle 12c & MySQL 5.7 for Personal Computer
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Abstract: Complexity, and Handling of huge data is a crucial target for all management systems. Databases are the backbone, central and core component of a computer application to store data in a logical way, which define the structure and mechanism for manipulation of data. Many Databases are available for handling and saving huge data including commercial and non-commercial like Microsoft SQL Server, Oracle Database, and MYSQL etc. Many vendors are working on modern techniques of databases spatio-temporal, object-relational, parallel databases etc. This novel research evaluates the comparative study and execution performance of top three databases according to their particular scenario and situation, after reading the paper the computer related experts especially developers easily judge, which database is most reliable in particular scenario, choosing the right decision for development of huge computer applications for hospitals, banks, and industries. Keywords: Microsoft SQL Server, Oracle, MySQL, Huge Databases. |
