Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier and XGBoost Classifier are analyzed and compared based on the accuracy delivered by the models

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Not AvailableBacterial blight (BB) disease and submergence due to flash flood are the two major constraints for achieving higher yield from rainfed lowland rice. Marker-assisted backcross breeding was followed to develop submergence tolerant and durable BB resistant variety in the background of popular cultivar ‘Swarna'. Four BB resistance genes viz., Xa4, xa5, xa13, Xa21 and Sub1 QTL for submergence tolerance were incorporated into the mega variety. Foreground selection for the five target genes was performed using closely linked markers and tracked in each backcross generations. Background selection in plants carrying the target genes was performed by using 100 simple sequence repeat markers. Amongst backcross derivatives, the plant carrying five target genes and maximum recurrent parent genome content was selected in each generation and hybridized with recipient parent. Eighteen BC3F2 plants were obtained by selfing the selected BC3F1 line. Amongst the pyramided lines, 3 lines were homozygous for all the target genes. Bioassay of the 18 pyramided lines containing BB resistance genes was conducted against different Xoo strains conferred very high levels of resistance to the predominant isolates. The pyramided lines also exhibited submergence tolerance for 14 days. The pyramided lines were similar to the recurrent parent in 14 morpho-quality traits.Not Availabl

Development of Submergence-Tolerant, Bacterial Blight-Resistant, and High-Yielding Near Isogenic Lines of Popular Variety, ‘Swarna’ Through Marker-Assisted Breeding Approach

The rice variety 'Swarna' is highly popular in the eastern region of India. The farmers of eastern India cultivate mainly rainfed rice and face the adverse effects of climate change very frequently. Rice production in this region is not stable. Swarna variety is highly susceptible to bacterial blight (BB) disease and flash floods, which cause a heavy reduction in the yield. Transfer of five target genes/QTLs was targeted into the variety, Swarna by adopting marker-assisted backcross breeding approach. Direct markers fo

A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market

A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market

Copper ion substituted hercynite (Cu0.03Fe0.97Al2O4): A highly active catalyst for liquid phase oxidation of cyclohexane

Copper ion substituted MAl2O4 (M = Mg, Mn, Fe, Ni and Zn) spinels, CuxM1-xAl2O4 (x = 0.03 and 0.05), have been synthesized by a single step solution combustion method. Of the various compositions studied the 3 at.% copper ion substituted hercynite, Cu0.03Fe0.97Al2O4, reported here for the first time, is shown to be much more active (similar to 92% conversion with similar to 99% selectivity) than other spinet analogues towards liquid phase oxidation of cyclohexane in acetonitrile with H2O2 as oxidant in air. Powder XRD analyses have revealed formation of pure hercynite phases. The least-square refined lattice parameters obtained from XRD data together with microstructural data by HRTEM have indicated copper ion substitution in the spinel lattice. The oxidation state of copper has been established as +2 from XPS analysis and it seem to be primarily substituted in the Fe-site of hercynite. Incorporation of the copper in the spinel structure of FeAl2O4 leading to an ionic interaction is explained to be responsible for the higher oxidation activity observed over the combustion synthesized catalyst than the corresponding impregnated catalyst which contains finely dispersed CuO crystallites. Effect of recycling (repeated thrice) has shown almost no degradation of activity of the copper ion substituted hercynite. In contrast, the analogous impregnated catalyst has shown appreciable loss of activity in the consecutive cycles due to the presence of dispersed CuO crystallites which can agglomerate with ease and subsequently leach out. (C) 2014 Elsevier B.V. All rights reserved

Selective liquid phase benzyl alcohol oxidation over Cu-loaded LaFeO3 perovskite

Copper loaded LaMO3 (M = Mn, Fe and Co) perovskites have been synthesized by a single-step solution combustion method. These materials have been investigated for liquid phase oxidation of benzyl alcohol using tertiary butyl hydrogen peroxide (TBHP) as oxidant in air at 80 degrees C under ambient pressure. Among these, the 10 at% Cu-loaded LaFeO3 has shown the best activity i.e., similar to 99% conversion with complete benzaldehyde selectivity. The formation of perovskite phase was confirmed from XRD and the presence of Cu2+ was confirmed by XPS analysis. The higher activity of the combustion synthesized catalyst has been ascribed to the presence of a poorly defined surface structure containing an amorphous CuO phase wrapping the LaFeO3 particle, as evidenced from the HRTEM analysis. The catalyst recycling tests have shown a negligible loss of activity in the consecutive cycles.Postprint (author's final draft

Selective liquid phase benzyl alcohol oxidation over Cu-loaded LaFeO3 perovskite

Copper loaded LaMO3 (M = Mn, Fe and Co) perovskites have been synthesized by a single-step solution combustion method. These materials have been investigated for liquid phase oxidation of benzyl alcohol using tertiary butyl hydrogen peroxide (TBHP) as oxidant in air at 80 degrees C under ambient pressure. Among these, the 10 at% Cu-loaded LaFeO3 has shown the best activity i.e., similar to 99% conversion with complete benzaldehyde selectivity. The formation of perovskite phase was confirmed from XRD and the presence of Cu2+ was confirmed by XPS analysis. The higher activity of the combustion synthesized catalyst has been ascribed to the presence of a poorly defined surface structure containing an amorphous CuO phase wrapping the LaFeO3 particle, as evidenced from the HRTEM analysis. The catalyst recycling tests have shown a negligible loss of activity in the consecutive cycles

Copper ion substituted hercynite (Cu0.03Fe0.97Al2O4): A highly active catalyst for liquid phase oxidation of cyclohexane

Copper ion substituted MAl2O4 (M = Mg, Mn, Fe, Ni and Zn) spinels, CuxM1-xAl2O4 (x = 0.03 and 0.05), have been synthesized by a single step solution combustion method. Of the various compositions studied the 3 at.% copper ion substituted hercynite, Cu0.03Fe0.97Al2O4, reported here for the first time, is shown to be much more active (similar to 92% conversion with similar to 99% selectivity) than other spinet analogues towards liquid phase oxidation of cyclohexane in acetonitrile with H2O2 as oxidant in air. Powder XRD analyses have revealed formation of pure hercynite phases. The least-square refined lattice parameters obtained from XRD data together with microstructural data by HRTEM have indicated copper ion substitution in the spinel lattice. The oxidation state of copper has been established as +2 from XPS analysis and it seem to be primarily substituted in the Fe-site of hercynite. Incorporation of the copper in the spinel structure of FeAl2O4 leading to an ionic interaction is explained to be responsible for the higher oxidation activity observed over the combustion synthesized catalyst than the corresponding impregnated catalyst which contains finely dispersed CuO crystallites. Effect of recycling (repeated thrice) has shown almost no degradation of activity of the copper ion substituted hercynite. In contrast, the analogous impregnated catalyst has shown appreciable loss of activity in the consecutive cycles due to the presence of dispersed CuO crystallites which can agglomerate with ease and subsequently leach out. (C) 2014 Elsevier B.V. All rights reserved