Energy Meter Data Analysis Using Machine Learning Techniques

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics

The Effective Quantitative Analysis for Brain Tumor Diagnosis Using an Efficient Deep Learning Algorithm

In the medical field, imaging analysis is the hottest topic. It has attracted many researchers to accurately analyses the disease severity and predict the outcome. However, if the trained images are more complex, the noise pruning results have decreased, which has tended to gain less prediction exactness score. So, a novel Chimp-based Boosting Multilayer Perceptron (CbBMP) prediction framework has been built in this present study. Moreover, the objective of this study is brain tumor prediction and severity analysis from the MRI brain images. The boosting function is employed to earn the most acceptable error pruning outcome. Henceforth, the feature analysis and the tumor prediction process were executed accurately with the help chimp solution function. The planned framework is tested in the MATLAB environment, and the prediction improvement score is analyzed by performing a comparative analysis. A novel CbBMP model has recorded the finest tumor forecasting rate

BANDGAP ANALYSIS OF NANO CRYSTALLINE L0.1ZY0.9BCCO CERAMICS

Crystalline Ceramic Lanthanum Zirconium Yttrium barium Calcium Copper oxide(L0.1ZY0.9BCCO)  was prepared by a high-energy ball milling process through mechanically assisted synthesis at a high temperature to acquire the desired homogeneity and phase formation. Inorder to study the optical properties like reflectivity, absorptivity, refractive index, the UV-VIS analysis of the above nonstochiometric sample was carried out. The dispersion of refractive index was analyzed by the Wemple-DiDomenico single-oscillator model.The band gap energy of the sample was elucidated from the Tauc plot. The refractive index n was calculated and  the results obtained are plotted  with the wavelength.Â

Short Communication- Stimulation of reserpine biosynthesis in the callus of Rauvolfia tetraphylla L. by precursor feeding

Reserpine is an important indole alkaloid that is used to treat hypertension and various psychiatric diseases by acting as a tranquilizing agent. In pharmaceutical industries, reserpine is in great demand. Chemical synthesis of reserpine is costlier than extracting it from natural resources. So enhancing this alkaloid in the already available system is a beneficial approach. Tryptophan is the starting material in the biosynthesis of reserpine. Callus was induced from leaf explants of Rauvolfia tetraphylla   L. on MS medium supplemented with the combination of 9 μM 2,4-D and 25, 50, 75 and 100 mg/l tryptophan. An increase in the reserpine content was observed at 50 mg/l tryptophan than in other concentrations

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Tomato is an important vegetable crop and India ranks second in area and production of tomato worldwide. During field surveys in Moinabad and Shamshabad Mandals in Ranga Reddy district of Telangana from 2014–2016, symptoms like big bud and phyllody were observed on tomato. The affected plants did not produce any fruit. The phytoplasma strains were detected and characterized using universal and nested primer pairs of phytoplasma with amplification of 16S rRNA and secA genes. BLAST analysis of 1.25 kb 16S rDNA partial sequences of nested PCR products and 880 bp of secA gene products obtained from symptomatic TBB (tomato big bud) samples revealed 99% sequence identity with strains of ‘Ca. Phytoplasma australasia’ (16Sr II group). Phylogenetic analysis and virtual RFLP analysis of 16SrDNA sequences of tomato big bud phytoplasma (TBBP) strain also suggested the closest relationship with ‘Ca. P. australasia’16Sr II-D subgroup related strain. Present study confirmed association of 16Sr II-D subgroup of phytoplasma associated with tomato big bud disease in Telangana State of India