Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN.

Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process. This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique. By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description. Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi. With the unsupervised machine learning, the extracted features are used for various applications.

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.

Mechanistic investigation on microbial toxicity of nano hydroxyapatite on implant associated pathogens.

The use of atomic scale inorganic nanoparticles (NPs) to fight against pathogenic microorganisms is a recent trend in biomedical area which overcomes the limitations of organic compounds in terms of stability, shelf life and bioactivity. One such Calcium phosphate based biomaterial is hydroxyapatite (HA), considered as potential bioactive compound with excellent biocompatibility, osteointegrity and biodegradability. Osteomyelitis, the implant associated infection, is the major problem worldwide responsible for the majority of implant failure cases. Since HA is used as a coating material of implants, only few reports were available on its antimicrobial activity and cytotoxicity whereas no reports on its possible antimicrobial mechanism. In this present study, the HA-NPs were synthesized by wet chemical precipitation and were characterized using X-ray diffraction (XRD), Transmission Electron Microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). The synthesized HA-NPs were evaluated for antimicrobial activity against implant associated bacterial pathogens. The study also explores the mechanistic action of HA-NPs in killing of bacteria by determining the reactive oxygen species (ROS) generation, DNA fragmentation, Lactate dehydrogenase (LDH) leakage and cellular interaction. In addition the cytotoxicity of HA-NPs was determined by MTT assay and Fluorescence Microscopic analysis. The results revealed that, the synthesized HA-NPs showed good antibacterial activity for tested bacterial species and the possible antibacterial mechanism were due to the lack of membrane integrity and cytotoxic studies shows the concentration dependent changes in cell viability.

Toxic Effects of Couroupita guianensis Against Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae).

Laboratory experiments were conducted to find out the efficacy of different crude extracts and fractions of Couroupita guianensis (Lecythidaceae) against Spodoptera litura (Fabricius). Results revealed that hexane, chloroform and ethyl acetate extracts of C. guianensis showed larvicidal and pupicidal activities against S. litura. Maximum larvicidal activity (68.66%) was observed in hexane extract at 5.0% concentration followed by chloroform and ethyl acetate extracts, and least LC50 value of 2.64% was observed in hexane extract. A 100% pupicidal activity was observed in hexane extract. Based on the efficacy of crude extracts, the effective crude extract (hexane extract) was further fractionated and subjected to screening for biological activities against S. litura. Among the eight fractions isolated from the hexane extract, fraction 8 showed maximum antifeedant activity (81. 8%) and larvicidal activity (76.9%) at 1000-ppm concentration; this fraction showed least LC50 value of 375.92 ppm for larvicidal activity. Cent per cent pupicidal activity was recorded. Reduced midgut and hemolymph protein contents were observed at 1000 ppm of fraction 8. Histopathological studies revealed that fraction 8 severely damaged the midgut cells of S. litura. This fraction could be used to develop botanical formulation to control agricultural pests.

Bioefficacy of Aristolochia tagala Cham. against Spodoptera litura Fab. (Lepidoptera: Noctuidae).

Bioefficacy of leaf and root extracts of Aristolochia tagala Cham. at different concentrations was evaluated at room temperature against Spodoptera litura Fab. Effects on feeding, larvicidal and pupicidal activities and larval-pupal duration were studied. Higher antifeedant activity (56.06%), lethal concentration for feeding inhibition (3.69%), larvicidal (40.66%), pupicidal (28%), total mortality (68.66%) and prolonged larval-pupal duration (12.04-13.08 days) were observed in ethyl acetate leaf extract at 5.0% concentration. Dose dependant effect of test extracts was observed. This plant could be used to isolate active principles and to develop a new botanical formulation in pest management programmes.

Optical and field-emission properties of ZnO nanostructures deposited using high-pressure pulsed laser deposition.

ZnO nanostructures were deposited on GaN (0001), Al2O3 (0001), and Si (100) substrates using a high-pressure pulsed laser deposition (PLD) method. Vertically aligned hexagonal-pyramidal ZnO nanorods were obtained on the Al2O3 and Si substrates whereas interlinked ZnO nanowalls were obtained on the GaN substrates. A growth mechanism has been proposed for the formation of ZnO nanowalls based on different growth rates of ZnO polar and nonpolar planes. Both ZnO nanorods and nanowalls exhibit a strong E2H vibration mode in the micro-Raman spectra. The corresponding fluorescence spectra of ZnO nanorods and nanowalls showed near band emission at 3.28 eV. The ZnO nanorods grown on the Si substrates exhibited better crystalline and optical properties compared with the ZnO structures grown on the GaN and Al2O3 substrates. The high aspect ratio, good vertical alignment, and better crystallinity of the ZnO nanorods with tapered tips exhibited promising field emission performance with a low turn-on field of 2 V/µm, a high current density of 7.7 mA/cm2, and a large field enhancement factor.