A Medical Analysis for Colorectal Lymphomas using 3D MRI Images and Deep Residual Boltzmann CNN Mechanism

In this technological world the healthcare is very crucial and difficult to spend time for the wellbeing. The lifestyle disease can transform in to the life threating disease and lead to critical stages. Colorectal lymphomas are the 3rd most malignancy death in the entire world. The estimation of the volume of lymphomas is often used by Magnetic Resonance Imaging during medical diagnosis, particularly in advanced stages. The research study can be classified in multiple stages. In the initial stages, an automated method is used to calculated the volume of the colorectal lymphomas using 3D MRI images. The process begins with feature extraction using Iterative Multilinear Component Analysis and Multiscale Phase level set segmentation based on CNN model. Then, a logical frustum model is utilized for 3D simulation of colon lymphoma for rendering the medical data. The next stages is focused on tackling the matter of segmentation and classification of abnormality and normality of lymph nodes. A semi supervised fuzzy logic algorithm for clustering is used for segmentation, whereas bee herd optimization algorithm with scale down for employed to intensify corresponding classifier rate of detection. Finally, classification is performed using Deep residual Boltzmann CNN. Our proposed methodology gives a better results and diagnosis prediction for lymphomas for an accuracy 97.7%, sensitivity 95.7% and specify as 95.8% which is superior than the traditional approach

Efficacy of Cereals and Pulses as Feeds for the Post-larvae of the Freshwater Prawn Macrobrachium rosenbergii ï€

Two types of feeds were prepared using cereals (maize, bajra and Italian millet) and pulses (green gram, red gram and cow gram) respectively and fed to the post larvae of M. rosenbergii for a period of 60 days. The efficacy of these feeds on growth performance, biochemical constituents and energy utilization were assessed and compared with commercially available standard Scampi feed. Statistically insignificant differences were seen in weight gain, specific growth rate and conversion rate between control and experiments, and between experiments. However, significant differences (

Predicting Nursing Graduates’ Intention for Continuous Usage of OCP- MALL App: A Benchmark Analysis

Introduction: M- learning has become a great source for nurses to up-skill their practice. Many localized asynchronous m-learning apps are emerging to fill the lacunae in nursing education, which require features to trigger and sustain self-regulated e learning behaviour. The present study takes an inventory of the engaging factors of OCP (Oral case Presentation) MALL app that support continued m - learning Method: The OCP -MALL was analysed with three determiners of continued m-learning engaging qualities bench marked by various m- learning theories I. Cognitive Engagement Quality (Perceived Usefulness, perceived Relevance of Learning, User Satisfaction Quality); II. User Satisfaction Quality (M-learning satisfaction, Perceived level of m-learning Anxiety, Ease of M- Learning App usage); Motivational Engagement Quality (Intrinsic Motivation, Extrinsic Motivation and Game Elements) by comparing the pre-and postusers’ feedback. Results: The analysis indicate that all 57 participants considered OCP - MALL as a useful app with English language training for OCP perceived as a felt need. A significant change in the confidence perceived in the use of vocabulary and fluency to present the case. The user perception survey elicited responses showed a satisfaction in using online resources (z=-4.042b, P =< 0.001), e learning support for academic performance (Z = -2.887b, P= < 0.003) and improved interaction during e - learning (Z=-3.729d, P = < 0.001), satisfaction ( z= -3.834b, P = 0.000) and peer interactions (Z= -4.417d, P = 0.000). A consistent everyday progress on OCP learning observed through the administrator’s progress tracker mode indicated a self-determined regulation to learn, a sign of intrinsic motivation.While employing digital gaming elements ‘unlocking levels of learning and assessment’, ‘receiving rewards, score points’ and ‘badges’ enhanced the extrinsic motivational quality of the app. Conclusion:The findings support that OCP - MALL app has the potential engaging qualities to influence the users’ continued learning. It is evident that asynchronous MALL applications that are developed in local regions could effective provide language learning if developed using the continuous learning factors as antecedents. Further, the identified check list of engaging factors could serve as guide post to the nursing educators who are desirous to develop need-based m- learning applications

Prioritization of the micro-watersheds through morphometric analysis in the Vasishta Sub Basin of the Vellar River, Tamil Nadu using ASTER Digital Elevation Model (DEM) data

The dataset for this article includes morphological analysis of the level to which groundwater potential of the Vasishta River, Salem and Perambalur districts of Tamil Nadu. The method for the computation of morphometric parameters using data Digital Elevation Model (DEM) of the Vasishta River, is also prepared using SRTM (Shuttle Radar Topographic Mission) 90 m resolution data Morphometric parameter linear, aerial and relief limits, such as a bifurcation ratio (Rb), Drainage density (Dd) Stream Frequency (Fs) Elongation ratio (Re), Length of overland flow (Lg), Relief ratio, ruggedness number (Rn) and Slope (sb) of Vasishta Sub Basin (VSB). The relief ratio indicates that the discharge should be considered high priority given to the following micro-watersheds numbers 9,1,15,11 and 10. This data could be very useful to help with sustainable groundwater planning in any similar basins

Enhanced Photocatalytic Activity of Rare Earth Metal (Nd and Gd) doped ZnO Nanostructures2

International audiencePresence of harmful organic pollutants in wastewater effluents causes serious environmental problems and therefore purification of this contaminated water by a cost effective treatment method is one of the most important issue which is in urgent need of scientific research. One such promising treatment technique uses semiconductor photocatalyst for the reduction of recalcitrant pollutants in water. In the present work, rare earth metals (Nd and Gd) doped ZnO nanostructured photocatalyst have been synthesized by wet chemical method. The prepared samples were characterized by X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDS). The XRD results showed that the prepared samples were well crystalline with hexagonal Wurtzite structure. The results of EDS revealed that rare earth elements were doped into ZnO structure. The effect of rare earth dopant on morphology and photocatalytic degradation properties of the prepared samples were studied and discussed. The results revealed that the rare earth metal doped ZnO samples showed enhanced visible light photocatalytic activity for the degradation of methylene blue dye than pure nano ZnO photocatalyst

Unsupervised texture classification using vector quantization and deterministic relaxation neural network

This paper describes the use of a neural network architecture for classifying textured images in an unsupervised manner using image-specific constraints. The texture features are extracted by using two-dimensional (2-D) Gabor filters arranged as a set of wavelet bases. The classification model comprises feature quantization, partition, and competition processes. The feature quantization process uses a vector quantizer to quantize the features into codevectors, where the probability of grouping the vectors is modeled as Gibbs distribution. A set of label constraints for each pixel in the image are provided by the partition and competition processes. An energy function corresponding to the a posteriori probability is derived from these processes, and a neural network is used to represent this energy function. The state of the network and the codevectors of the vector quantizer are iteratively adjusted using a deterministic relaxation procedure until a stable state is reached. The final equilibrium state of the vector quantizer gives a classification of the textured image. A cluster validity measure based on modified Hubert index is used to determine the optimal number of texture classes in the image

A combined neural network approach for texture classification

Abstract--In this article, we present a two-stage neural network structure that combines the characteristics of self-organizing map (SOM) and multilayer perceptron (MLP) for the problem of texture classification. The texture features are extracted using a multichannel approach. The channels comprise of a set of Gabor filters having different sizes, orientations, and frequencies to constitute N-dimensional feature vectors. SOM acts as a clustering mechanism to map these N-dimensional feature vectors onto its M-dimensional output space, where in our experiments, the value of M was taken as two. This, in turn, forms the feature space from which the features are fed into an MLP for training and subsequent classification. It is shown that the disadvantage of using Gabor filters in texture analysis, namely, the higher dimensionality of the Gaborian feature space, is overcome by the reduction in the dimensionality of the feature space achieved by SOM. This results in a significant reduction in the learning time of MLP and hence the overall classification time. It is found that this mechanism increases the interclass distance (average distance among the vectors of different classes) and at the same time decreases the intraclass distance (average distance among the vectors of the same class) in the feature space, thereby reducing the complexity of classification. Experiments were performed on images containing tiles of natural textures as well as image data from remote sensing

Torsion of a Large Ovarian Dermoid Cyst in Third Trimester Pregnancy: A Case Report

Dermoid cysts are the most frequent ovarian tumours among women of reproductive age, constituting a notable 20% of all adult ovarian tumours. They are typically lined by stratified squamous epithelium and contain dermal and epidermal elements. During pregnancy, dermoid cysts are more likely to lead to infection, rupture and torsion. A 23-year-old multigravida (G2P1L1) female at 36 weeks of gestation presented with complaints of left lumbar pain persisting for one day, unrelieved by medication. Ultrasound (USG) revealed a multiloculated septated cystic lesion in the left lumbar region adjacent to the gravid uterus, just anteroinferior to the left kidney. Magnetic Resonance Imaging (MRI) showed a predominantly fat-density multiloculated cystic lesion with twisting of the pedicle. An emergency laparotomy was performed, and the left ovarian cyst was removed, revealing patchy discolored areas of gangrene, and a live healthy foetus was delivered. While torsion of an ovarian cyst is a well-known complication, its presentation during pregnancy is rare. Due to the variable symptoms of ovarian torsion, the clinical presentation can be quite confusing. Therefore, both the obstetrician and radiologist should have a lower threshold for clinical suspicion of torsion in pregnancy, enabling prompt diagnosis and management of such cases to prevent both maternal and foetal mortality