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In this study, the effectiveness of an SPAD-502 portable chlorophyll (Chl) meter was evaluated for estimating the Chl contents in leaves of some medicinal and aromatic plants. To predict the individual chlorophyll concentration indexes of St. John’s wort (Hypericum perforatum L.), mint (Mentha angustifolia L.), melissa (Melissa officinalis L.), thyme (Thymus sp.), and echinacea (Echinacea purpurea L.), models were developed using SPAD value. Multi-layer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and general regression neural network (GRNN) were used for determining the chlorophyll concentration indexes.

Ti–6Al–4V ELI alloy is one of the most familiar materials for orthopedic implants, aeronautical parts, marine components, oil and gas production equipment, and cryogenic vessel applications. Therefore, its appropriate quality of finishing is highly essential for these applications. But the characteristics like lower modulus of elasticity, lesser thermal conductivity, and high chemical sensitivity placed it in the categories of difficult-to-cut metal alloys. Also, tooling cost is one of the prime issues in the machining of this alloy. Therefore, this research is more inclined to use a low-budget uncoated carbide tool in turning the Ti–6Al–4V ELI alloy. Also, the selection of suitable levels of machining parameters is highly indispensable to get the appropriate surface finish with a low tooling cost. So, the $L_{16}$ experimental design is utilized to check the performances of the uncoated carbide tool in the turning tests. The performance indexes like surface roughness (Ra), flank wear of tool (VBc), and material removal rate (MRR) are measured and studied with the help of surface plots and interaction plots. Further, the Firefly Algorithm optimization is employed to find the optimal cutting parameters and cutting response values. The local optimal values of the input parameters a, f, and V_c are estimated as 0.3241mm, 0.0893mm/rev, and 82.41m/min, respectively. Similarly, the global optimal values for the responses Ra, VBc, and MRR are reported as 0.6321μm, 0.09253mm, and 24.61g/min, individually. Additionally, to predict the responses, Generalized Regression Neural Network (GRNN) modeling is employed and the average absolute error for each response is noticed to be less than 1%. Therefore, the GRNN modeling tool is strongly recommended for various machining applications.

Literature abounds with various statistical and machine learning techniques for stock market forecasting. However, Reinforcement Learning (RL) is conspicuous by its absence in this field and is little explored despite its potential to address the dynamic and uncertain nature of the stock market. In a first-of-its-kind study, this research precisely bridges this gap, by forecasting stock prices using RL, in the static as well as streaming contexts using deep RL techniques. In the static context, we employed three deep RL algorithms for forecasting the stock prices: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO) and Recurrent Deterministic Policy Gradient (RDPG) and compared their performance with Multi-Layer Perceptron (MLP), Support Vector Regression (SVR) and General Regression Neural Network (GRNN). In addition, we proposed a generic streaming analytics-based forecasting approach leveraging the real-time processing capabilities of Spark streaming for all six methods. This approach employs a sliding window technique for real-time forecasting or nowcasting using the above-mentioned algorithms. We demonstrated the effectiveness of the proposed approach on the daily closing prices of four different financial time series dataset as well as the Mackey–Glass time series, a benchmark chaotic time series dataset. We evaluated the performance of these methods using three metrics: Symmetric Mean Absolute Percentage (SMAPE), Directional Symmetry statistic (DS) and Theil’s U Coefficient. The results are promising for DDPG in the static context and GRNN turned out to be the best in streaming context. We performed the Diebold–Mariano (DM) test to assess the statistical significance of the best-performing models.

There is an increasing need to apply emerging technologies in knowledge-based management system. This proposed system captures the knowledge of experts and the knowledge acquired for designing a new systems. Wind Tunnel Test data of missiles has been taken into consideration for knowledge management. Interpolation and Extrapolation of missile test data has been attempted using neural network technique General Regression Neural Networks training algorithm. The results produced by neural network training methodologies are validated with the existing test data. The knowledge extraction using neural network is found suitable for interpolation and to some extent for extrapolation, thereby reducing the cost as well as number of test runs in Wind Tunnel Test.

The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we propose a Two-dimensional Principal Component Analysis (2D-PCA) for efficient handwritten digit recognition. 2D-PCA is based on 2D image matrices rather than 1D vectors so that image matrix does not need to be transform into a vector prior to feature extraction as done in PCA.¹ For subsequent classification purpose we have used Generalized Regression Neural Network (GR.NN). A test performed on the MNIST handwritten numeral database showed a better recognition rate is among the best on the MNIST database.