ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization

A picture is best way to explore the tourist destination by visual content. The content-based image classification of tourist destinations makes it possible to understand the tourism liking by providing a more satisfactory tour. It also provides an important reference for tourist destination marketing. To enhance the competitiveness of the tourism market in India, this research proposes an innovative tourist spot identification mechanism by identifying the content of significant numbers of tourist photos using convolutional neural network (CNN) approach. It overcomes the limitations of manual approaches by recognizing visual information in photos. In this study, six thousand photos from different tourist destinations of India were identified and categorized into six major categories to form a new dataset of Indian Trajectory. This research employed Transfer learning (TF) strategies which help to obtain a good performance measure with very small dataset for image classification.VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50 and AlexNet CNN model with pretrained weight from ImageNet dataset was used for initialization and then an adapted classifier was used to classify tourist destination images from the newly prepared dataset. Hybrid hyperparameter optimization employ to find out hyperparameter for proposed Atithi model which lead to more efficient model in classification. To analyse and compare the performance of the models, known performance indicators were selected. As compared to the AlexNet model (0.83), MobileNetV2(0.93), VGG-19(0.918), InceptionV3(0.89), ResNet-50(0.852) the VGG16 model has performed the best in terms of accuracy (0.95). These results show the effectiveness of the current model in tourist destination image classification

Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field

Key Issues on Tourism Strategies

After an institutional request, strategic planning is usually promoted by teams coordinated by one expert in the field, by a firm or by a University. The day it is delivered there is a general feeling of frustration with the outcome. This feeling is most likely due to an incomplete diffusion process or/and to some difficulties to measure long term and intangible outcomes. In this paper we intend to overcome some of these misinterpretations, reflecting on the mostly theoretical questions popping up from recent study cases; it is essentially centred upon the lived experiences and the methodological issues that only future will assess. This paper is also an academic exercise to share with the regional science peers the life experiment we had during PETUR (Strategic Plan for Tourism in Serra Estrela - Portugal), the acronym of the work team I coordinated, which rose a number of practical questions that one should reflect upon under recent theoretical developments in social sciences involving decision and collective action. The paper is structured as follows: (1) a context introduction; (2) an international and national literature review considering then in more detail (3) some recent developments on innovation diffusion theories. The (4) section illustrates some of the initiatives we took in the case study for a the specific region in Serra da Estrela, an internal small region located in between the Portuguese Atlantic coast and the Spanish border; the (5) section is devoted to the main focus of the paper - key issues in tourism Strategies. The paper will close with the concluding remarks where private-public partnership is mostly considered a complex learning process in order to excel in innovative diffusion processes. JEL Classification: R58; L83; O22; L26; C61; H77Keywords: tourism economics; planning methodology; organizational complexity; cluster strategy; Portugal

Detecting and locating trending places using multimodal social network data

This paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by project “Desarrollo de un ecosistema de datos abiertos para transformar el sector turístico” (GVA-COVID19/2021/103) funded by Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana, “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21) and the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning. We also would like to thank Nvidia for their generous hardware donations that made these experiments possible

Cumulative and Combinatorial Knowledge Dynamics: Their Role for Continuity and Change in Regional Path Development

This thesis addresses the question of how regions can adapt to technological and social changes. In the face of recent economic rises, disruptive digitalization, and the need to transition towards sustainability, processes of diversification, transformation, and renewal have become increasingly important to strengthen the dynamics development of regions. While the creation or the lock-in of regional development paths has received a lot of attention by economic geographers, studies on the transformation of existing paths are relatively scarce. Instead, in recent decades scientific, political, and economic actors alike have promoted a logic of specialization to support regional competitiveness. Yet the processes outlined above require that these specializations are combined with novel, external knowledge inputs. In order to capture these processes of the accumulation and combination of knowledge, this thesis introduces and applies the concept of knowledge dynamics. It differentiates interaction processes by the degree of institutional and cognitive distance that actors have to bridge. In the theoretical framework of this thesis, these knowledge dynamics are brought together with path development research of the Evolutionary Economic Geography and Innovation Systems literature strands. At the center of this thesis stand the questions of how different knowledge dynamics influence, and are influenced by, the elements and institutions of regional economic landscapes. In four papers based on empirical material from different contexts, these interfaces are explored. Thereby the focus is laid on knowledge dynamics in symbolic knowledge bases of music scenes, as the field of non-technological innovation is relatively underexplored despite its increasing economic significance. In order to quantitatively measure innovation and knowledge dynamics in this creative industry, this thesis employs so-called resonance indicators derived from digital social data. The findings of this thesis show that the interplay of cumulative and combinatorial knowledge dynamics leads to processes of path modernization and branching. While cumulative knowledge dynamics guide the direction of potential diversification routes, combinatorial knowledge dynamics affect the creation and transformation of new organizations and institutions. In order to promote these dynamics, path plasticity and the openness to extralocal knowledge sources should be promoted

Visual Question Answering: A Survey of Methods and Datasets

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.Comment: 25 page

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years