Pandemic Impact on the Travels and Tourism Sector of Nepal

The travel and tours enterprise were badly affected due to pandemics. In the aftermath of high restrictions on human movement, travel-based entrepreneurs were highly impacted due to lockdown. Due to pandemic, highly impacted into earning-saving, lack of supportive working conditions, lower self-capacity, and lack of recovery budget and policies, the travel and tours-based entrepreneurs were highly impacted. The study reflected the impact of pandemics on travel and tours, major constraints, and a possible way forward to sustaining. The research explores what are the major existing practices of sustaining travel and tours entrepreneurs during pandemics, what factors can contribute to building bounce-back capacities of travel and tours entrepreneurs’ sustainability. Above forty-four, snowball-based sampling was done from major travel and tours entrepreneurs, Pokhara-Nepal. A structure-based open-ended questionnaire, key informant interviews, and in-person-based discussion were applied in the method of study. Used the content analysis along with a recap of the research question, undertake bracketing to identify biases, operationalize variables with develop a coding, and code the data with undertaking analysis while qualitative analysis, and multiple regression facilitated on quantitative analysis to finalize the discussion. The study reflects that self-saving, social support, state and financial institutions recovery support, social behavior and change communication, full vaccination practices, and self-accountable tourist behavior are highly expectable conditions to the sustainability of travel and torus entrepreneurship in the learning area. The study concludes that self-saving capacity can contribute to bounce-back capacity for every entrepreneur. Social support and socioeconomic recovery packages were also contributing to sustaining travel and tours in the study area. Self-saving condition and capacity is higher bounce back capacity compared to non-saved entrepreneurs in the study area. Social support, socioeconomic recovery practices, and recovery packages from state and financial institutions were not at the higher level as expected.

Unsupervised Tumor Extraction and Classification

This thesis is concerned with the problem of tumor extraction and classification. The process of tumor detection and classification is a complex and time consuming task since it requires a careful assessment of medical images. In the effort to produce more efficient and accurate results, image analysis techniques are frequently being used. Therefore, developing a system which could accurately segment the tumor affected regions and categorize the tumors in different classes is very important. It is of benefit to develop a computer system which assists radiologists and also reduces the subjectivity and human errors involved in the diagnosis. The aim was to develop reliable methods that contribute towards accurate extraction and classification of tumor from medical images. This thesis contributes in all major steps of a computer aided system i.e. image preprocessing, image segmentation, feature extraction and classification. In image pre-processing, two image fusion techniques for multi-modal medical images based on local features and fuzzy logic are presented. In first scheme, local entropy and variance are used to calculate the information in images. The scheme assigns weights to pixels depending upon the amount of information. The main advantage of the proposed fuzzy logic based image fusion scheme is improvement in fused results. The second scheme uses undecimated wavelet, local features, improved guided filter and weighted maps. This scheme offers less spectral distortion and produce better spatial information than the existing techniques. In image segmentation, two segmentation schemes based on weighted fuzzy active contour are presented. vi In these techniques, weights have been assigned in proportion to the information provided by local features, two fuzzy systems (Mamdani inference and Takagi-Sugeno inference) based systems are used for assigning weights. A method is presented for feature extraction and classification by using texture features, invariant moments and perception based features. Optimal feature combination using fuzzy weights and classification using multi-class support vector machines is performed. Simulation results when analyzed visually and quantitatively depict the significance of the proposed schemes compared to existing schemes. The results of all the proposed image fusion schemes are demonstrated through examples of medical images and results of test against conventional schemes