Cross Validation Component Based Reduction for Divorce Rate Prediction

Concurring to information from the Centresfor Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result showsthat Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset

Undersampling Aware Learning based Fetal Health Prediction using Cardiotocographic Data

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods

NON INVASIVE COST EFFECTIVE SIDDHA DIAGNOSTIC TOOLS FOR SIDDHA AILMENTS

The medication of normal procedure shall be extra legitimate if the ailment is identified by using it’s possess viewpoint. So the be taught was once aimed to determine the sensitivity and specificity of the Siddha diagnostic methodology. Siddha strongly advocacies every physician to seem into “what type of person is suffering from an ailment is most important than what variety of health problem he has”. This holistic standpoint devises the protocol of each medication in Siddha. This distinctive primary is the delicate force and motive in the back of the existence of this method considering antiquity.Having the above mentioned unique standards in intellect, this paper tried to fully grasp the complexity and core basics of Siddha diagnostics which indeed pave solution to unique therapeutics. The medication of normal system shall be more legitimate if the disorder is diagnosed via its own point of view. So the learn was once aimed to check the sensitivity and specificity of the Siddha diagnostic ways. Eight fold examinations displays particularly pulse studying, tongue, complexion, voice, eyes, physique examination, stool and urine. These instruments provide the framework in phrases of immediate and individualized prognosis and medication to the patient and support to recover from diseases in a timely fashion without leaving any hazardous impact on the physique

Multichannel Approach for Sentiment Analysis Using Stack of Neural Network with Lexicon Based Padding and Attention Mechanism

Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance

Research Paper - Protective effect of a polyherbal drug, ambrex in ethanolinduced gastric mucosal lesions in experimental rats

Objective: To investigate the protective effect of ambrex in ethanol-induced gastric mucosal lesions in rats. Material and Methods: Ethanol-induced gastric mucosal lesions in male Wistar rats were used to evaluate gastric ulcer protective effect of ambrex (40 mg/kg/day p.o. for 15 days). The response to ambrex was assessed from ulcer index, cell proliferation, histopathological changes and alkaline phosphatase (ALP) activity. Results: Ambrex pretreatment showed protection against ethanol-induced gastric mucosal damage, a significant reduction in the ulcer index and ALP activity, and an increase in the DNA content. Conclusion: Ambrex offers protection against ethanol-induced gastric ulcers

A clinical evaluation of optic neuropathy in various aetiologies

Background: Since optic neuropathy is the initial manifestation of various systemic disorders, it is essential to do detailed investigations to arrive at the diagnosis. This aids in timely management of underlying systemic disorders and prevents not only the visual disability but also the complications of underlying disease. Many studies focus on individual aetiologies of optic neuropathy, but only few studies provide information about various aetiologies of optic neuropathy. Aim: To determine the clinical profile of patients diagnosed with optic neuropathy and to evaluate the varied aetiologies of optic neuropathies. Methodology: All clinically diagnosed patients of optic neuropathy with defective vision, visual field defect, colour vision defect and abnormal pupillary response attending the neurology and ophthalmology department in Thanjavur Medical College from January 2019 to May 2020 were taken up for the study. Results: Eighty-three patients of clinically diagnosed optic neuropathy, who presented at Thanjavur Medical College, were studied during the period of January 2019–May 2020. The most common aetiology of optic neuropathy was idiopathic optic neuritis followed by ischemic and traumatic optic neuropathy. Conclusion: This study addressed an increase in the incidence of traumatic and ischemic optic neuropathies when compared to other studies. Thus, ischemic and traumatic optic neuropathies need more attention for the future researches

Concept of standard raw drug substitution in Traditional Siddha Medicine - A Review

Classical Siddha medicine recommends the usage of functionally parallel substitute raw drugs in the scarcity of the original material. The concept of standard raw drug substitution is designated by the Tamil term Matru sarakku, which emphasizes selecting alternatives either in herbal or non-herbal sources based on certain attributes. Functionally similar materials are suggested for balancing the deficit based on such parameters. Literature sources from Siddha medicine and other allied subjects were explored and documented for the inclusive understanding of the concept of ideal substitution. Few of the standard substitutes mentioned in the classical Siddha literature were evaluated in a scientific account for its justification. An outlook on different specimens as described in the ancient texts of Tamil medicine indicates the usage of substitutes from numerous plants, animals, and other metallo-mineral ingredients. The rationale of selecting alternatives primarily depends on the equivalent following traditional attributes like organoleptic entities, potency, division, general properties, specific actions, and medicinal uses of the material shared between the raw drugs. This must undergo a systematic evaluation by pharmacognostic and phytochemical studies to justify current practices of substitution. A systematic document in these lines will give a proper guideline for the effective employment of substitute drugs in the current scenario of dwindling official botanical sources for many Traditional Siddha formulations.&nbsp