Post-Pandemic Travel : Decoding the Trends and Challenges for Indian Travellers

The Indian tourism industry has reached a great scale over the years. With the over-increasing internet penetration, more travellers are booking online travel in India. However, the world and in particular the tourism industry has seen an unprecedented shutdown due to Covid-19 affecting 2020 due to the absence of a universal vaccination at the moment. It is important to understand the current scenario of Indian travel patterns prior to the impact of Corona Virus and the factors which will be influencing the decision-making process of Indian Travellers in the future. Hence, this paper attempts to study and decode the decision-making process of Indian Travellers through extensive review of contemporary academic literature on post-pandemic tourism emerging with COVID-19 crisis. This study area is important because it addresses a pressing problem of comprehending the post pandemic travel and the research outcome suggests practical solutions to overcome the critical barriers arising out of Covid-19 for Indian Travellers and learn to practice a new way of travelling in the future

A Case Study on the Prospects of Customer-Brand Relationship in Tourism

The travel and tourism industry is struggling to gain markets' share and sustain profitability in today's fiercely competitive and economically demanding environment. The industry should develop new ways to manage their customer relationship to optimize customer loyalty and revenues. Customer retention is an integral aspect of sales planning. It is a known fact in the marketing circles that it costs companies three to four times more to find new customers than to retain existing ones. This research paper deals with a case study of the prospects of customer brand relationship scenario of some popular tour operators in Bangalore analysed with the help of qualitative interviews, Literature findings and through a constructive approach to understand Customer Brand Relationship in touris

Graphical Analysis on Text Mining Unstructured Data Using D-Matrix

Fault dependency (D-matrix) is used as a diagnostic model that identifies the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between identified failure modes and symptoms related to a system. Constructing such D-matrix fault detection model is time overwhelming task .A system is proposed that describes associate ontology based text mining on unstructured data using D-matrix for automatically constructing D-matrix by mining many repair verbatim text data (typically written in unstructured text) collected throughout the identification process. And also graphical model generation for each generated D-matrix. Initially we construct fault diagnosis ontology and then text mining techniques are applied to spot dependencies among failure modes and identified symptom. D-matrix is represented in graph so analysis gets easier and faulty parts becomes simply detectable. The proposed methodology are implemented as a prototype tool and validated by using real-life information collected from the automobile domain

Secret key extraction using Bluetooth wireless signal strength measurements

pre-printBluetooth has found widespread adoption in phones, wireless headsets, stethoscopes, glucose monitors, and oximeters for communication of, at times, very critical information. However, the link keys and encryption keys in Bluetooth are ultimately generated from a short 4 digit PIN, which can be cracked off-line. We develop an alternative for secure communication between Bluetooth devices using the symmetric wireless channel characteristics. Existing approaches to secret key extraction primarily use measurements from a fixed, single channel (e.g., a 20 MHzWiFi channel); however in the presence of heavy WiFi traffic, the packet exchange rate in such approaches can reduce as much as 200. We build and evaluate a new method, which is robust to heavy WiFi traffic, using a very wide bandwidth (B 20 MHz) in conjunction with random frequency hopping. We implement our secret key extraction on two Google Nexus One smartphones and conduct numerous experiments in indoor-hallway and outdoor settings. Using extensive real-world measurements, we show that outdoor settings are best suited for secret key extraction using Bluetooth. We also show that even in the absence of heavy WiFi traffic, the performance of secret key generation using Bluetooth is comparable to that of WiFi while using much lower transmit power

DANTE: Deep AlterNations for Training nEural networks

We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convexity to cast training a neural network as a bi-quasi-convex optimization problem. We show that for neural network configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations effectively in this formulation. DANTE can also be extended to networks with multiple hidden layers. In experiments on standard datasets, neural networks trained using the proposed method were found to be promising and competitive to traditional backpropagation techniques, both in terms of quality of the solution, as well as training speed.Comment: 19 page

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Secure and Reliable Data Transfer across Multiple Entities by Using LIME

A data distributor has given precise data to a set of evidently trusted agents. Some of the data are leaked and found in an unjustified place. The distributor must assess the probability that the splitted data came from one or more agents, as opposed to having been individually collected by others. We suggest data allocation techniques which can enhance the chance of identifying split. These strategies do not build on changes of the outsourced data. While sending data through the network there is a lot of dishonest user looking to hack useful data. A proper security should be provided to data which is send to network. To avoid this data leakage, we used the data lineage mechanism. We develop and analyze novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer and robust Watermarking

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

A study of prevalence of thyroid dysfunction in patients with metabolic syndrome

Background: The study was undertaken with an objective to study the thyroid functions in patients with metabolic syndrome diagnosed as per International Diabetes Federation (IDF) criteria and to know the spectrum of thyroid dysfunction.Methods: A total of 300 patients with metabolic syndrome diagnosed as per IDF criteria were included in the study. A detailed history regarding symptoms of hypothyroidism and examination was done in all patients and all these patients underwent thyroid profile tests.Results: A total of 300 patients with metabolic syndrome were included in this study. Thyroid dysfunction was present in 45% of the patients. Hypothyroidism was noted in 43 patients, subclinical hypothyroidism was noted in 114 patients, subclinical hyperthyroidism in 6 patients and hyperthyroidism in 1 patient. This study included 166 males and 134 females. 10% of the patients had symptoms related to hypothyroidism. 7% had goiter on examination. Thyroid dysfunction was seen in 68% females compared to that of 47% in males. Females had higher incidence compared to males. Elderly females (42%) and males in the age group of 40-50 years (41%) had higher incidence of subclinical hypothyroidism compared to others.Conclusions: Prevalence of thyroid disorders in diabetics was 45%. Elderly population had more incidences. Sub- clinical hypothyroidism was more common among females.