An Automatic Rice Plant Disease Detection Model Built With Unstructured Data Using IMDT Tiling and CNN Cognitive Object Detection

Nowadays agriculture and processes are getting more intelligent mechanisms to improve the yield and reduce manual work. Smart agriculture provides numerous modern ideologies to farmers. But still, farmers face one important issue crop disease. Many researchers provide plenty of ways to recover and tackle the situation to come out of this problem. Therefore, they proceed with image processing to identify diseases from rice plant images. Farmers mainly face problems to take proper images for classification. Because of various reasons like various environmental factors, farmers ignorance, field size, capturing angle, device limitations, etc. are affecting the quality of the disease detection system, and these factors degrade overall performance. For this problem, introducing the Intelligent multi-dimensional tiling (IMDT) technique with an advanced convolution neural network with cognitive object detection (CNN-COD). IMDT technique developed with an intelligent expert system that adjusts input image size, capturing angles and other factors automatically. This advanced tiling technique supports to do the cropping and fluttering of input images for resizing. And CNN-COD model was used to calculate rice leaf width size and rescaled at the time of image segmentation with the Residual network (ResNet) model. Created dynamic tiled images are uniformly and scaled dimensional objects. These input values are used to train the CNN-COD rice plant disease, prediction model. Our proposed models were appraised on more than 4960 images which contain 8 various types of rice crop diseases. The experimental result portrayed out the CNN-COD model receives significant improvement in objection detection and image classification for the rice plant disease detection system. Mean average precision (MAP) values compared the CNN-COD model with the YOLOv4 model it got improved by 3.7% with the tiled input dataset

A review on marine based nanoparticles and their potential applications

The increasing demands on nanoparticles have wide pertinent in almost all the fields. Marine ecosystem has variety of living resources, which includes prokaryotes like microorganism to eukaryotic organism like higher plants and animals. The present review dealt with the application of marine organisms in nanotechnology. Our discussion mainly focused on what the marine organisms are involved in and what type of nanoparticles is synthesized, including size and, medical and medicinal applications. Based on our observation through this review, it will be a good reference document for the further research on marine ecosystem to develop drug from sea. Keywords: Nanomaterial, marine animals, mangroves, marine microbe

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%

Occurrence of Uranium in Groundwater from Cuddalore District Tamil Nadu Aided by Geospatial and Statistical Techniques

An attempt has been made to examine uranium distribution in groundwater from Cuddalore district, Tamil Nadu, India. Groundwater occurs under porous sedimentary, fractured, and weathered hard rock formations ranging in age from recent sediments to the oldest Archean formations. A total of 186 groundwater samples were collected during Pre- Monsoon (May) and Post-monsoon (January) and analyzed for major cations, anions, and uranium using standard procedures. Major anions and cations follow the order Cl- >H4SiO4>HCO3- >NO3- > Na+> Ca2+> Mg2+>K+>SO42- > F-> PO43- irrespective of seasons. Uranium in groundwater ranges from 0.1 micro gram per liter (µg/l ) to 24.67 µg/l with average 1.82 µg/l. The spatial representation maps isolated areas of higher and lower uranium and statistical analysis inferred uranium sources to the groundwater environment

Nonlinear saturation of electrostatic waves: mobile ions modify trapping scaling

The amplitude equation for an unstable electrostatic wave in a multi-species Vlasov plasma has been derived. The dynamics of the mode amplitude $\rho(t)$ is studied using an expansion in $\rho$; in particular, in the limit $\gamma\rightarrow0^+$, the singularities in the expansion coefficients are analyzed to predict the asymptotic dependence of the electric field on the linear growth rate $\gamma$. Generically $|E_k|\sim \gamma^{5/2}$, as $\gamma\rightarrow0^+$, but in the limit of infinite ion mass or for instabilities in reflection-symmetric systems due to real eigenvalues the more familiar trapping scaling $|E_k|\sim \gamma^{2}$ is predicted.Comment: 13 pages (Latex/RevTex), 4 postscript encapsulated figures which are included using the utility "uufiles". They should be automatically included with the text when it is downloaded. Figures also available in hard copy from the authors ([email protected]

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods

Inhibition of protein ubiquitination by paraquat and 1-methyl-4-phenylpyridinium impairs ubiquitin-dependent protein degradation pathways

Intracytoplasmic inclusions of protein aggregates in dopaminergic cells (Lewy bodies) are the pathological hallmark of Parkinson’s disease (PD). Ubiquitin (Ub), alpha [α]-synuclein, p62/sequestosome 1 and oxidized proteins are major components of Lewy bodies. However, the mechanisms involved in the impairment of misfolded/oxidized protein degradation pathways in PD are still unclear. PD is linked to mitochondrial dysfunction and environmental pesticide exposure. In this work, we evaluated the effect of the pesticide paraquat (PQ) and the mitochondrial toxin 1-methyl-4-phenylpyridinium (MPP+) on Ub-dependent protein degradation pathways. No increase in the accumulation of Ub-bound proteins or aggregates was observed in dopaminergic cells (SK-N-SH) treated with PQ or MPP+, or in mice chronically exposed to PQ. PQ decreased Ub protein content, but not its mRNA transcription. Protein synthesis inhibition with cycloheximide depleted Ub levels and potentiated PQ–induced cell death. Inhibition of proteasomal activity by PQ was found to be a late event in cell death progression, and had no effect on either the toxicity of MPP+ or PQ, or the accumulation of oxidized sulfenylated, sulfonylated (DJ-1/PARK7 and peroxiredoxins) and carbonylated proteins induced by PQ. PQ- and MPP+-induced Ub protein depletion prompted the dimerization/inactivation of the Ub-binding protein p62 that regulates the clearance of ubiquitinated proteins by autophagic. We confirmed that PQ and MPP+ impaired autophagy flux, and that the blockage of autophagy by the overexpression of a dominant-negative form of the autophagy protein 5 (dnAtg5) stimulated their toxicity, but there was no additional effect upon inhibition of the proteasome. PQ induced an increase in the accumulation of α-synuclein in dopaminergic cells and membrane associated foci in yeast cells. Our results demonstrate that inhibition of protein ubiquitination by PQ and MPP+ is involved in the dysfunction of Ub-dependent protein degradation pathways

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MÅ‘ssbauer and Magnetic investigations on OPC blended with Silicafume

          The Mössbauer spectra of optimum amount of (20%) silica fume blended cement paste using CO(Rh) Source have been interpreted for its hydration kinetics using two waters namely DW and EW.  The obtained results were correlated with the setting time, compressive strength and magnetic data measurement using Kappa bridge.  This is preferred as its offers some resistance to sulphate attack observed from their studies