Impact of integrated nutrient management on some important physical and chemical attributes of soil vis-a-vis performance of bitter gourd

An experiment has been conducted under AICRP on Soil Test Crop Response (STCR) at the Central Research Farm (Gayeshpur), Bidhan Chandra Krishi Viswavidyalaya, West Bengal to find out the effect of integrated nutrient management in ArkaHarit variety of bitter gourd. The treatments contain different organic and inorganic fertilizer viz. Control (T1), NPK @ 90:60:60 kg/ha (T2), Vermicompost @ 12t/ha (T3), NPK+ Vermicompost @ 3t/ha (T4), FYM @ 25t/ha (T5), NPK+FYM @ 6.25t/ha (T6), Mustard oil cake (MOC) @ 7t/ha (T7), NPK+MOC @ 1.75t/ha (T8). Application of organic and inorganic sources in an integrated manner has resulted higher in yield, physical and chemical parameter such as seed yield (2815 kg/ha), aggregate ratio (0.69), mean weight diameter (0.593 mm), geometric mean weight diameter (0.679 mm), organic carbon (1.28 %), CEC (12.88 meq/100g), available nitrogen (208 kg/ha), phosphorus (62 kg/ha), potassium (167 kg/ha) in higher magnitude as compare to the single application of inorganic fertilizer. In maximum cases, the chemical parameters is highest in harvesting stage rather than other stage. Quality characters such as Vitamin A, C, crude fibre are nourished in favourable way due to integrated appli-cation of organic and inorganic fertilizers. Based on the performance, it was found that treatment combination of NPK+MOC @ 1.75t/ha (T8) was best among all treatments in most cases for yield, productivity and nutritional as-pects of ArkaHarit variety of bitter gourd

Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product

Feedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi

Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models

For predicting the shelf life of processed cheese stored at 7-8 C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese

Application of simulated neural networks as Non-Linear Modular Modeling Method for predicting shelf life of processed cheese / Sumit Goyal and Gyanendra Kumar Goyal

This paper presents the capability of simulated neural network (SNN) models for predicting the shelf life of processed cheese stored at ambient temperature 30o C. Processed cheese is a dairy product generally made from medium ripened Cheddar cheese. Elman and Linear Layer(Train) SNN models were developed. Body & texture, aroma & flavour, moisture, free fatty acids were used as input variables and sensory score as the output. Neurons in each hidden layers varied from 1 to 40. The network was trained with single as well as double hidden layers up to 100 epochs, and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were used for testing prediction potential of the developed models. Results showed a 4201 topology was able to predict the shelf life of processed cheese exceedingly well with R2 as 0.99992157. The corresponding RMSE for this topology was 0.003615359. From this study it is concluded that SNN models are excellent tool for predicting the shelf life of processed cheese

Text Independent Open-Set Cell phone Identification

This paper discusses the application of speech signals that convey various pieces of information such as the identity of its speaker, the language spoken, and the linguistic information about the text being spoken etc. The rapid developments in technologies related to cell-phones have resulted in their much broader usage than mere talking devices used for making and receiving phone calls. User-generated audio recordings from cell phones can be very helpful in a number of forensic applications. This thesis proposes a novel system for open-set cell-phone identification from speech samples recorded using the cell-phone. The proposed system uses different features based on original speech recordings and classifies them using sequential minimal optimization (SMO) based Support vector machine (SVM) and Vector Quantization (VQ). The performance of the proposed system is tested on a customised databases extracted from pre-recorded speech content of twenty-two cell phones of different manufacturers. Closed-set cell-phone recognition systems abound, and the overwhelming majority of research in cell-phone recognition in the past has been limited to this task. A realistically viable system must be capable of dealing with the open-set task. This effort attacks the open-set task, identifying the best features to use, and proposes the use of a fuzzy classifier followed by hypothesis testing as a model for text-independent, open-set cell-phone recognition

Artificial Neural Expert Computing Models for Determining Shelf Life of Processed Cheese

Time-delay single and multi layer models were developed for predicting shelf life of processed cheese stored at 30oC. Processed cheese is very nutritious dairy product, rich in milk proteins and milk fat. For developing computational neuroscience models,experimental data relating to body & texture, aroma & flavour, moisture, free fatty acids were taken as input variables, while sensory score as output variable. Mean Square Error, Root Mean Square Error, Coefficient of determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction performance of the developed computational models. The results of the study established excellent correlation between experimental data and the predicted values, with a high determination coefficient. From the study it was concluded that artificial neural expert time-delay models are good for predicting the shelf life of processed cheese.DOI:http://dx.doi.org/10.11591/ijece.v2i3.35

Genetic analysis of grain yield and its contributing traits for their implications in improvement of bread wheat cultivars

Genetic analysis was carried out in 55 genotypes (10 parents and 45 F1s) through diallel mating design excluding reciprocals in bread wheat. Analysis of variance showed appreciable variability among the breeding material for almost all the traits under study. The highest value of phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) was found for flag leaf area (PCV=18.82, GCV=17.74), biological yield (PCV=12.98, GCV=11.70), grain yield (PCV=11.90, GCV=10.39) and harvest index (PCV=10.39, GCV=10.05). Highest heritability with highest genetic advance was estimated for flag leaf area (h2=52.24, GA=34.64), biological yield (h2=15.04, GA=21.71), harvest index (h2=18.19, GA=20.01), peduncle length (h2=31.72, GA=15.96) and spikelets per spike (h2=34.92, GA=12.96), therefore selection will be effective based on these traits. Grain yield was found significantly correlated (at <1% level of significance) with productive tillers (gr=0.3283**, pr=0.4347**), spike length (gr=0.1959**, pr=0.2203**), spikelets per spike (gr=0.4342**, pr=0.3813**), grains per spike (gr=0.7188**, pr=0.4918**), biological yield (gr=0.6101**, pr=0.6616**), harvest index (gr=0.3518**, pr=0.3227**) and thousand grain weight (gr=0.5232**, pr=0.3673**). Similarly path coefficient analysis estimates for biological yield (g=1.0524, p=1.0554), harvesting index (g=0.8862, p=0.8291), thousand grain weight (g=0.0588, p=0.0269), grains per spike (g=0.0496, p=0.0074), spike length (g=0.0209, p=0.0289), days to maturity (g=0.0142, p=0.0127), productive tillers (g=0.0186, p=0.0147), peduncle length (g=0.0123, p=0.0157), days to 50% flowering (g=0.0093, p=0.0072) and plant height (g=0.0042, p=0.0020) showed high positive direct effects on grain yield indicating that due importance should be given to these traits during selection for high yield

Simulated Neural Network Intelligent Computing Models for Predicting Shelf Life of Soft Cakes

This paper highlights the potential of simulated neural networks for predicting shelf life of soft cakes stored at 30o C. Elman and self organizing simulated neural network models were developed. Moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input parameters and overall acceptability score was output parameter. Neurons in each hidden layers varied from 1 to 30. The network was trained with single as well as double hidden layers with 1500 epochs and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. The shelf life predicted by simulated neural network model was 20.57 days, whereas as actual shelf life was 21 days. From the study, it can be concluded that simulated neural networks are excellent tool in predicting shelf life of soft cakes