20 research outputs found
Benchmarking Urban Sustainable Efficiency: A Case of Indian Cities
No full textAbstractIncreasing urbanization, in terms of growth in population as well as geographical spread, in the developing countries has significant implications for the transport sector. First is the increase in traffic volume and second, energy consumption. With constraints on energy resource availability, threat to climate change and transport infrastructure inadequacies, the above two implications need immediate attention of the planners and policy makers. However, there is inadequate research in understanding effect of urban system on energy dimension. The present work aims to understand this effect. For this study, urban system is considered as a close boundary system with residential and mobility as major contributors towards transport energy consumption. A set of Indian cities are considered for the study and Data Envelopment Analysis (DEA), a non-parametric technique is applied to study residential and mobility subsystem efficiency against energy subsystem
A numerical study on the dynamics of SIR epidemic model through Genocchi wavelet collocation method
No full textAbstract Epidemic models can play a major role in understanding the spread of diseases and their control. These mathematical models have plenty of significance in various scientific domains, including public health, to investigate disease propagation and ecology. This article explains the dynamics of SIR epidemic model of arbitrary order with aid of a precise numerical approach called Genocchi wavelet collocation method. The main purpose of this investigation is to explore and discover the results for system of nonlinear ordinary differential equations arising in the considered mathematical model and to investigate the dynamical aspects of SIR model via Caputo fractional derivative which is non-local in behaviour. The projected method depicts rapid algorithms and is extremely precise, reliable, and uses fewer computational resources. Also, this method is simpler than the other traditional numerical methods as it merges the operational matrix with the collocation method in order to transform fractional-order problem into algebraic equations which enables to obtain satisfactory results. The approximate solution obtained using proposed algorithm exposes the nature of their interactions. Furthermore, the numerical outcomes are represented through graphs for different fractional order and compared the results with Runge–Kutta method and residual power series method. The projected technique is very effective, accurate, free from controlling parameters and consume less time to investigate nonlinear complications arising in diverse fields of epidemical and biological models. Ultimately, the current study help to inspect the wild class of models and their performance which are occurring in real world
Classification of Bharatanatyam postures using tailored features and artificial neural network
No full textBharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures
Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection
No full textAbstractIntrusion detection system plays an important role in network security. Intrusion detection model is a predictive model used to predict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest. These algorithms are tested with NSL-KDD data set. Experimental results shows that Random Forest Classifier out performs the other methods in identifying whether the data traffic is normal or an attack
A Clinical Study on Standardization of Siddha Diagnostic Methodology, line of Treatment and Dietary Regimen for Kudivery Noi (Alcohol Dependence)
No full textAlcoholism is one of the major threatening problem in the society like caste, race religious and social discremination. The human being who consumes little quantity of alcohol as social drinkers in the beginning and turns into alcohol dependence in the latter stage. Hence it is inferred that, one should abstrain from the habit of alcohol at any time. Man should realise that the alcohol damages the body and the soul. Alcohol grasps everything including his health, wealth, fame and all. To help mankind disentangle himself from the euphoria of alcohol addiction and to return to normally, the author has implemented the diagnostic methods of the siddhars for early diagnosis and intervention of the state and has elaborated the methods in the following study.
In kudiveri Noi, the drug (Alcohol) is substance, other than food, Which when consumed produces changes in the physical or mental functioning of the individual. This state occurs when a drug (Alochol) is so central to person‘s thought, emotions and activities, that is extremely difficult to stop or even stop thinking, psychological dependence is marked by an intense craving for the drug. AIM : The aim of the study is to standardize the siddha Diagnositc Methodology, Line of Treatment and Dietary Regimen of - Kudiveri Noi‖ OBJECTIVES: Primary:- To study the clinical course of the disease - Kudiveri Nir‖ with keep observation on the Aetilogy pathology, Diagnosis, Prognosis, Complications and the Treatment by making use of siddha concept. To establish the unique diagnostic methods mentioned by siddhars to know how the disease - Kudiveri Noi‖ alters the normal conditions in Ennvagai thenvugal. To have an idea about incidence of the disease with Age, Sex, Socioeconomic status, Habit, Family history and life events. Secondary:
To observe the clinical presentation of this disease.
To document the Naadi Balachandra adangal, Thegyin Ilakkamam in ―Kudiveri Noi‖.
To document the shape of the Nekuri in ―Kudiveri Noi‖
To establish the dietary regimen for this disease
To follow the line of treatment of this disease.
Conclusion: In kudiveri noi study subjects “Pitha vatham” Naadi was noted predominently. In neikurin test ―+‖ and ―star‖ shapes (Spreading nature of oil) were noted in (75%) of cases. The study proved that the siddha diagnostic techniques are less time taking, cost effective, easy to perform and non invasive. In varma point of view Balachandra adangal pulse was felt in most of the cases. It was noted that middle age group (30-39) were affected by kudiveri noi. And their mental and physical health were affected, directly results in socio-ocupational functions. In future studies tha author is determined to study and doucument the results elaborately and extensively about kudiveri noi
Two Decades of TB Drug Discovery Efforts—What Have We Learned?
No full textAfter several years of limited success, an effective regimen for the treatment of both drug-sensitive and multiple-drug-resistant tuberculosis is in place. However, this success is still incomplete, as we need several more novel combinations to treat extensively drug-resistant tuberculosis, as well newer emerging resistance. Additionally, the goal of a shortened therapy continues to evade us. A systematic analysis of the tuberculosis drug discovery approaches employed over the last two decades shows that the lead identification path has been largely influenced by the improved understanding of the biology of the pathogen Mycobacterium tuberculosis. Interestingly, the drug discovery efforts can be grouped into a few defined approaches that predominated over a period of time. This review delineates the key drivers during each of these periods. While doing so, the author’s experiences at AstraZeneca R&D, Bangalore, India, on the discovery of new antimycobacterial candidate drugs are used to exemplify the concept. Finally, the review also discusses the value of validated targets, promiscuous targets, the current anti-TB pipeline, the gaps in it, and the possible way forward
