64 research outputs found
EMERGING TRENDS IN TOURISM: A SUSTAINABLE DEVELOPMENT AND CONSERVATION OF GEOTOURISM, GEOPARK, AND GEO-HERITAGE IN INDIA
No full textBackground of the study: An emerging sustainable tourism sector with global growth is Geotourism (GT). GT is very much beneficial in this context. It can conserve geosites and impart education to visitors, and at the same time, it can improve the local economy. Only little research has been done in this field though tourism promotion has a huge scope. Aim: Geological tourism, which has recently adopted natural and cultural geoheritage, has been understood to be more \u27geographical\u27. This connection betwixt GT along with cultural heritage is largely unexplored. Yet, it signifies an opportunity to offer socio-cultural and economic benefits as GT supports cultural values and interests. Thus, this study examines India\u27s emerging form of Sustainable Development (SD) and conservation of GT, geopark, and geoheritage. Methods: Data has been taken from 322 respondents from various geoparks in India by using a descriptive positivist approach and quantitative technique. Results: As per the findings, in response to visitors\u27 perceptions, the government focuses on conserving geoheritage resources, promoting products, and collaborating with experts and community representatives for culturally sensitive development.
High-order IMEX-WENO finite volume approximation for nonlinear age-structured population model
No full textAn Approach to Analyze Pattern from Large Database of Healthcare
No full textAbstract-To address the problem of knowledge discovery through pattern matching various models are used. There are effective methods for discovering knowledge from temporal data. Like, hidden Markov models (HMM) are a popular approach to discover patterns from temporal data. However, HMMs do not scale favorably with the size of a given dataset, and hence, are not normally used for data mining applications of large sets of temporal data. In this paper, we present a new method in discovering patterns from a large set of unlabeled temporal data. K-mean (KM) and hidden Markov model (HMM) form the core of our proposed approach. These methods are engaged to cluster temporal data by using a novel recursive KM-HMM model. Index Terms -knowledge discovery, pattern discovery, clustering model, KM-HMM
Prospective study of radiation related adverse events and its management in cancer patient at tertiary care teaching hospital
No full textBackground: Radiation therapy is associated with certain adverse events which may cause significant discomfort to patient and may affect patient’s life. The objective of the study was to assess radiation related adverse events in the patients who are on radiation therapy and to prevent and manage these adverse events.Methods: A prospective observational study was conducted on 193 patients receiving radiotherapy in Oncology Department at Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab. One fraction (2 Gray) dose had been given to patients daily for five days in a week and monitor. The collected data was analyzed by applying IBM SPSS v21.Results: The clinical results observed in 193 consecutive patients with follow-up of 7 weeks and graded according to RTOG Acute Radiation Morbidity Scoring Criteria. Majority of events were reported in age group of 41-60 years followed by 61-80 years, 20-40 years. Epidermal, mucosal, Genitourinary and Lower G.I. reactions are graded. The reactions managed by providing symptomatic treatment.Conclusions: Radiation related adverse events have been found frequently in patients with radiotherapy and chemo-radiotherapy both. As the number of doses increase with time the grade of reactions also increases. Appropriate follow-up and management of these events reduces patient burden of treatment.</jats:p
E-learning through e-PG Pathshala Portal in the Digital Age
No full textThis paper presents the importance of an e-learning platform, namely e-PG Pathshala, as a gateway for all the courses in different disciplines at the postgraduate level. e-PG Pathshala is an open courseware initiative of the UGC INFLIBNET Centre that started as an MHRD project, titled National Mission on Education by Way of ICT (NME-ICT) in India. The e-PG Pathshala project provided 25169 e-content modules in different subjects. The key components of educational systems are quality of content. This paper discusses the definition of ICT (Information and Communication Technology), E-Learning through e-PG Pathshala, An Analysis of E-Content modules in e-PG Pathshala, the benefits and drawbacks of learning through e-PG Pathshala portal and paradigm shifts (Transformation from Traditional to Modern learning in Digital Era). It is suggested that an Online Public Access Catalog (OPAC) for searching e-Content modules should be made available on the e-PG Pathshala portal to fastest retrieve course material for e-learners
Development of an Assessment Methodology to Measure Effects of Geometrical Clearance of Bowden Type Shifting Cable on Gearshift Quality in Passenger Car
No full textDermatosis neglecta in schizophrenia: A rare case report
No full textDermatosis neglecta is a chronic, dermatologic disorder results in ignored, neglected body parts due to chronic disability or painful conditions. There is scarcity of literature supporting the existence of dermatosis neglecta in the context of psychiatric illnesses. In this case report, we attempts to highlight, dermatosis neglecta in a homeless patient suffering from schizophrenia
Brutality detection and rendering of brutal frames
No full textThe popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate
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