Birla Institute of Applied science

Naini Tāl, India

Birla Institute of Applied science

Naini Tāl, India
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Bhatt A.,Amity University | Dubey S.K.,Amity University | Bhatt A.K.,Birla Institute of Applied science | Joshi M.,Amity University
Advances in Intelligent Systems and Computing | Year: 2017

This paper presents the experimental analysis of data provided by UCI machine learning repository. Weka open source machine learning tool provided by Waikato University reveals the hidden fact behind the datasets on applying supervised mathematical proven algorithm, i.e., J48 and Naïve Bayes algorithm. J48 is an extension of ID3 algorithm having additional features like continuous attribute value ranges and derivation of rules. The data sets were analyzed using two approaches, i.e., first taken with selected attributes and taken with all attributes. The performance of both the algorithm reveals the accuracy of algorithm and predicting the various reasons behind this increasing problem of cardiovascular diseases. © Springer Nature Singapore Pte Ltd. 2017.


Mishra D.,Birla Institute of Applied science | Bisht D.,Kumaun University | Joshi S.,Kumaun University
International Journal of Green Pharmacy | Year: 2013

Context: Methanolic extract of Leucas hyssopifolia roots was investigated for its anti-bacterial property. Aim: Evaluation of anti-bacterial activity of Leucas hyssopifolia Benth. Settings and Design: Roots of the plant were collected, extracted and finally evaluated for their anti-bacterial activity. Materials and Methods: Paper disc diffusion method and microdilution technique were employed for the determination of zone of inhibition and minimal inhibitory concentration, respectively. Results: The extract showed anti-bacterial activity against all the tested bacterial strains except Escherichia coli. Conclusions: Anti-bacterial activity of extract of Leucas hyssopifolia roots may be due to the presence of secondary plant metabolites like terpenoids, steroids and flavonoids, which are present in the extract. The extract can be further studied for the isolation of chemical compounds and their biological activity.


Bhatt A.,Amity University | Dubey S.K.,Amity University | Bhatt A.K.,Birla Institute of Applied science
International Journal of Intelligent Engineering and Systems | Year: 2017

In today's modern world, Cardiovascular Disease is one of the most lethal one. Sudden Cardiac Death is the result of this heart disease that attacks a person so instantly that it hardly gives seconds to be operated and heart freezes causing death at spot which makes it more sever and complicated for hospitals and medical services. In this paper, we have given our efforts to predict the possibility of occurring of these quick and killing attacks using decision model based predictive analytics techniques, so that we can analyze and find some patterns that are common in the happenings of Sudden Cardiac Death. Unfortunately, most hospitals and medical service organization's data are rarely used in clinical research while these datasets has such a huge potential by applying for predictive analytical approach. Classification Algorithms that comes under the decision model based in order to predict the probability of Sudden Cardiac Attack on heart disease patients. From our results, we came to know that in Hungarian database as well as in Echocardiograph database, Naïve Bayes algorithm outperformed all other algorithms and showed the maximum accuracy. In order to tackle this situation, we have proposed a framework that can be implemented for emergency situations of people with such medical history and these raw datasets are further analyzed at scratch to predict the upcoming life threatening pain that might cause death.


Mishra D.,Birla Institute of Applied science | Savita P.,Government Polytechnic | Mukesh S.L.,Kumaun University
International Journal of Green Pharmacy | Year: 2015

Introduction: Senecio rufinervis D.C. locally known as Bhanwa contains sesquiterpene rich essential oil having analgesic and antimicrobial potential. To the best of our knowledge the plant has never been investigated for its secondary plant metabolites except its essential oil content. The objective of the present study was to isolate and characterize its phytoconstituents. Material and Methods: Petroleum ether extract from the roots of S. rufinervis D.C. was subjected to column chromatography yielded stigmastane type steroids. Results: The structure of the two compounds was assigned on the basis of chemical tests and spectral studies. Conclusion: The plant has never been investigated for the isolation of higher natural products other than essential oil, we have for the first time isolated steroids from the plant and tentatively characterized the isolated stigmastane type steroids, thus the plant has the potential for further isolation and absolute characterization of other natural products.


Bhatt A.K.,Birla Institute of Applied science | Pant D.,Uttarakhand Open University | Singh R.,Birla Institute of Applied science
AI and Society | Year: 2014

The purpose of this paper is to develop Artificial Neural Network (ANN)-based apple classifier. Testing effort is calculated using ANN method. The complete system is divided into two modules. In the first module, input (surface level apple quality parameter) from the different sources is collected by the software developed in Visual Basic through different input device like web camera, weight machine, etc. In the second module, the input data are used by ANN simulator to classify the apple according to their quality. The final result of an ANN model for apple classification is discussed; however, the modeling results showed that there is excellent agreement between the experimental data and predicted values. A low level of error prediction confirmed the fact that the Neural Network model is an effective instrument of the apple quality estimation. There is not any misclassification during testing. The paper presents alternative method for quality assessment of apple and provides consumers with a safer food supply. © 2012 Springer-Verlag London Limited.


Joshi S.,Kumaun University | Mishra D.,Birla Institute of Applied science | Bisht G.,Kumaun University | Khetwal K.S.,Kumaun University
EXCLI Journal | Year: 2011

The essential oil of Lobelia pyramidalis was analyzed by GC and GC-MS. A total of 21 con-stituents comprising 77.88% of the total oil were identified. Perilla ketone constituted 25.61% of the oil followed by camphorquinone (12.16%), dibutyl phthalate (10.66%) and allyl nonanoate (8.47%). The antimicrobial activity of the oil was evaluated using the disc diffusion method and the microdilution technique. The results showed that the oil exhibited moderate antimicrobial activity.


Mishra D.,Birla Institute of Applied science | Joshi S.,Kumaun University | Sah S.P.,Kumaun University | Dev A.,Birla Institute of Technology | Bisht G.,Kumaun University
Indian Journal of Natural Products and Resources | Year: 2011

The chemical composition of the essential oil obtained from the leaves and roots of Senecio rufinervis DC. was analyzed by GC, GC/MS and NMR. Germacrene D was the major constituent in both the oils studied (40.19 and 24.95%, respectively). The antimicrobial activity of the oil was determined by disc diffusion method. Results showed that both the oils exhibited significant antibacterial activity.


Joshi S.,Kumaun University | Mishra D.,Birla Institute of Applied science
Research Journal of Medicinal Plant | Year: 2013

An attempt has been made to analyse macro and micro elements in three medicinal plants (Solidago canadensis, Buddleja asiatica and Leucas hyssopifolia) collected from different regions of Uttarakhand, India. Macro minerals viz., sodium, potassium, calcium, lithium were estimated by Flame Photometer while micro minerals viz., iron, copper, manganese, zinc and cobalt were determined by atomic absorption spectrophotometer. Among all the elements, highest concentration of calcium was recorded in all of the three plants followed by potassium and sodium. Macrominerals such as sodium, potassium, calcium were present in greater amount in L. hyssopifolia while S. canadensis was found to be richer in microminerals. © 2013 Academic Journals Inc.


Bhatt A.K.,Birla Institute of Applied science | Pant D.,Uttarakhand Open University
AI and Society | Year: 2013

This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply. © 2013, Springer-Verlag London.


Mishra D.,Birla Institute of Applied science | Bisht G.,Kumaun University | Mazumdar P.M.,Birla Institute of Technology | Sah S.P.,Kumaun University
Pharmaceutical Biology | Year: 2010

Context: Senecio rufinervis D.C (Asteraceae) is a tall aromatic herb, commonly found in Uttarakhand, India. No investigations on the biological activity of this plant have been published so far. Hence, this plant species became a subject of our scientific interest. Objective: The aim of the study was to investigate the chemical composition and analgesic activity of Senecio rufinervis essential oil in mice using both thermal and chemical models of pain. Materials and methods: Essential oil from dried leaves of Senecio rufinervis was extracted by steam distillation and then subjected to GC-MS analysis. Varying doses of essential oil were given to mice, 30min prior to the induction of abdominal constrictions and determination of mean reaction time in hot-plate maintained at 55° ± 0.5°C. Results: The main component detected in the essential oil of Senecio rufinervis was germacrene D (40.19%) followed by β-pinene (12.23%), β-caryophyllene (6.21%) and β-longipinene (4.15%). Essential oil exhibited significant and dose-dependent analgesic activity against acetic acid-induced writhing in mice. The percentage inhibition in number of writhes produced by 25, 50 and 75mg/kg doses was, respectively, 69, 80 and 85%. The oil, at doses 50 and 75mg/kg, significantly increased the mean latency in the hot-plate after 15 and 30min of drug administration as compared to the control group. Discussion and conclusion: The results depicted both central and peripheral analgesic activity of S. rufinervis essential oil which was attributed to the presence of terpenes. © 2010 Informa Healthcare USA, Inc.

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