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The Emerald Handbook of Destination Recovery in Tourism and Hospitality
Featuring a broad geographical range of examples and pan-disciplinary perspectives, The Emerald Handbook of Destination Recovery in Tourism and Hospitality is an essential reference and illuminating guide on developments in the theory and practice of tourism development post-pandemic. 2023 Priyakrushna Mohanty, Anukrati Sharma, James Kennell and Azizul Hassan. -
COVID-19, religious events, and indian tourism recovery: Prospects and paradoxes
The chapter delves into three objectives. Firstly, the chapter aims to find out the intersectionalities of religious events and the Indian tourism industry. For the second objective, the impact of the COVID-19 disease on religious events will be briefly discussed. Lastly, this work will discuss the various emergent prospects, themes, trends, and challenges that will emerge on the paths of the recovery of religious events and pilgrimage tourism post-COVID-19. This work is theoretical in nature and can be classified as a viewpoint article that follows a conceptual research design. Copyright 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 2023 by IGI Global. All rights reserved. -
Disrupted Diners: Impacts of COVID-19 on Restaurant Service Systems and Technological Adaptations
Measures such as lockdowns and social distancing may have effectively controlled the pandemic, but they have a tremendous detrimental effect on businesses relying heavily on face-to-face communications such as the restaurant and dine-in industry. With the current COVID-19 pandemic, the restaurant and dine-in places had to face the brunt of losing customers due to government-mandated public health measures. The restaurant sector had to look for an overhaul immediately as the disruptions caused by the pandemic has pushed them either on the verge of closure or bad financial health. Nevertheless, an upsurge of technological advancements has come as a lender of last resort to the restaurant industry. This chapter presents the major disruptions caused by the pandemic in the in-person dining sector. It also sheds light on the various methods shaping the future of the restaurant industry. Finally, the chapter deals with the different prospects and challenges awaiting the paths of transformation and draws a framework called The Dining Spectrum as a contribution to the existing literature. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Mobile Apps for Enhanced Bleisure Tourism Experiences: Exploring the Prospects and Challenges
Mobile applications play a pivotal role in enabling and enhancing bleisure travel experiences. These apps offer solutions for communication, itinerary planning, transportation booking, and leisure discovery, reflecting the evolving expectations of modern travelers for efficiency, flexibility, and customized experiences. Despite their benefits, challenges such as data privacy concerns and information overload persist. Looking ahead, the future of bleisure travel is poised for further transformation through advances in mobile technology, including augmented reality and artificial intelligence. However, a research gap exists in understanding the full spectrum of mobile apps catering to bleisure tourists' needs. This chapter aims to address this gap by classifying mobile apps for bleisure tourism, exploring their advantages, and identifying challenges and opportunities for innovation. By doing so, it seeks to contribute to a deeper understanding of the role of mobile technology in shaping the landscape of bleisure tourism in the digital age. 2024 by IGI Global. All rights reserved. -
Sacred gastronomy trails: Exploring the divine fusion of religion, food, and tourism
This study seeks to explain the complex relationships among these three constantly evolving fields, i.e., religion, food, and tourism. The primary objective is to examine the strong link between food and religion by breaking down culinary customs and examining how they influence the formation of gastronomic identities across a range of religious traditions. The second objective explores the connection between food and travel, with a special emphasis on the cultural relevance of pilgrimage food travels. The third goal is to broaden the investigation to include the connection between religion and travel. Through the integration of results from the three aforementioned goals, the research aims to develop a theoretical framework that elucidates the intricate relationship between these components, offering a thorough comprehension of the interdependence of religion, cuisine, and travel in forming personal encounters and cultural environments. 2024 by IGI Global. All rights reserved.. -
Mobile apps in bleisure tourism: Enhancing travel experience, work-life balance, and destination exploration
This study aims to achieve four primary objectives: first, to evaluate how mobile apps improve travel productivity and efficiency by streamlining logistics and simplifying planning for both business and leisure activities; second, to investigate how these apps support the integration of work and leisure by providing tools for remote work, task management, and peer communication; third, to explore how mobile apps enhance the quality and authenticity of bleisure experiences by helping travelers discover new places and immerse themselves in local culture; and finally, to construct a comprehensive framework for mobile apps in bleisure tourism for use by multiple stakeholders, including travelers, travel companies, the hospitality industry, employers, local tourism boards, and app developers. This study highlights the significance of mobile technology in optimizing the bleisure travel experience. 2024 by IGI Global. All rights reserved. -
Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm
Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management. 2022 by the authors. -
Pathway toDetect Cancer Tumor byGenetic Mutation
Cancer detection is one of the challenging tasks due to the unavailability of proper medical facilities. The survival of cancer patients depends upon early detection and medication. The main cause of the disease is due to several genetic mutations which form cancer tumors. Identification of genetic mutation is a time-consuming task. This creates a lot of difficulties for the molecular pathologist. A molecular pathologist selects a list of gene variations to analyze manually. The clinical evidence strips belong to nine classes, but the classification principle is still unknown. This implementation proposes a multi-class classifier to classify genetic mutations based on clinical evidence. Natural language processing analyzes the clinical text of evidence of gene mutations. Machine learning algorithms like K-nearest neighbor, linear support vector machine, and stacking models are applied to the collected text dataset, which contains information about the genetic mutations and other clinical pieces of evidence that pathology uses to classify the gene mutations. In this implementation, nine genetic variations have been taken, considered a multi-class classification problem. Here, each data point is classified among the nine classes of gene mutation. The performance of the machine learning models is analyzed on the gene, variance, and text features. The gene, variance, and text features are analyzed individually with univariate analysis. Then K-nearest neighbor, linear support vector machine, and stacking model are applied to the combined features of a gene, variance, and text. In the experiment, support vector machine gives better results as compared to other models because this model provides fewer misclassification points. Based on the variants of gene mutation, the risk of cancer can be detected, and medications can be given. This chapter will motivate the readers, researchers, and scholars of this field for future investigations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cancer Tumor Detection Using Genetic Mutated Data and Machine Learning Models
Early detection of a disease is a crucial task because of unavailability of proper medical facilities. Cancer is one of the critical diseases that needs early detection for survival. A cancer tumor is caused due to thousands of genetic mutations. Understanding the genetic mutations of cancer tumor is a tedious and time-consuming task. A list of genetic variations is analysed manually by a molecular pathologist. The clinical strips of indication are of nine classes, but the classification is still unknown. The objective of this implementation is to suggest a multiclass classifier which classifies the genetic mutations with respect to the clinical signs. The clinical evidences are text-evidences of gene mutations and analysed by Natural Language Processing (NLP). Various machine learning concepts like Naive Bayes, Logistic Regression, Linear Support Vector Machine, Random Forest Classifier applied on the collected dataset which contain the evidence based on genetic mutations and other clinical evidences that pathology or specialists used to classify the gene mutations. The performances of the models are analysed to get the best results. The machine learning models are implemented and analyzed with the help of gene, variance and text features. Based on the variants of gene mutation, the risk of the cancer can be detected and the medications can be prescribed accordingly. 2022 IEEE. -
Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features
Speech emotion recognition (SER) is a dynamic area of research which includes features extraction, classification and adaptation of speech emotion dataset. There are many applications where human emotions play a vital role for giving smart solutions. Some of these applications are vehicle communications, classification of satisfied and unsatisfied customers in call centers, in-car board system based on information on drivers mental state, human-computer interaction system and others. In this contribution, an improved emotion recognition technique has been proposed with Deep Convolutional Neural Network (DCNN) by using both speech spectral and prosodic features to classify seven human emotionsanger, disgust, fear, happiness, neutral, sadness and surprise. The proposed idea is implemented on different datasets such as RAVDESS, SAVEE, TESS and CREMA-D with accuracy of 96.54%, 92.38%, 99.42% and 87.90%, respectively, and compared with other pre-defined machine learning and deep learning methods. To test the real-time accuracy of the model, it has been implemented on the combined datasets with accuracy of 90.27%. This research can be useful for development of smart applications in mobile devices, household robots and online learning management system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Improving Speaker Gender Detection by Combining Pitch and SDC
Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Sentiment Analysis on Banking Feedback and News Data using Synonyms and Antonyms
Sentiment analysis is crucial for deciphering customers enthusiasm, frustration, and the market mood within the banking sector. This importance arises from financial datas specialized and sensitive nature, enabling a deeper understanding of customer sentiments. In todays digital and social marketing landscape within the banking and financial sector, sentiment analysis is significant in shaping customer insights, product development, brand reputation management, risk management, customer service improvement, fraud detection, market research, compliance regulations, etc. This paper introduces a novel approach to sentiment analysis in the banking sector, emphasizing integrating diverse text features to enable dynamic analysis. This proposed approach aims to assess the sentiment score of distinct words used within a document and classify them as positive, negative, or neutral. After rephrasing sentences using synonyms and antonyms of unique words, the system calculates sentence similarity using a distance control mechanism. Then, the system updates the dataset with the positive, negative, and neutral labels. Ultimately, the ELECTRA model utilizes the self-trained sentiment-scored data dictionary, and the newly created dataset is processed using the SoftMax activation function in combination with a customized ADAM optimizer. The approachs effectiveness is confirmed through the analysis of post-bank customer feedback and the phrase bank dataset, yielding accuracy scores of 92.15% and 93.47%, respectively. This study stands out due to its unique approach, which centers on evaluating customer satisfaction and market sentiment by utilizing sentiment scores of words and assessing sentence similarities. 2023, Science and Information Organization. All rights reserved. -
Whispered Speech Emotion Recognition with Gender Detection using BiLSTM and DCNN
Emotions are human mental states at a particular instance in time concerning ones circumstances, mood, and relationships with others. Identifying emotions from the whispered speech is complicated as the conversation might be confidential. The representation of the speech relies on the magnitude of its information. Whispered speech is intelligible, a low-intensity signal, and varies from normal speech. Emotion identification is quite tricky from whispered speech. Both prosodic and spectral speech features help to identify emotions. The emotion identification in a whispered speech happens using prosodic speech features such as zero-crossing rate (ZCR), pitch, and spectral features that include spectral centroid, chroma STFT, Mel scale spectrogram, Mel-frequency cepstral coefficient (MFCC), Shifted Delta Cepstrum (SDC), and Spectral Flux. There are two parts to the proposed implementation. Bidirectional Long Short-Term Memory (BiLSTM) helps to identify the gender from the speech sample in the first step with SDC and pitch. The Deep Convolutional Neural Network (DCNN) model helps to identify the emotions in the second step. This implementation is evaluated using the wTIMIT data corpus and gives 98.54% accuracy. Emotions have a dynamic effect on genders, so this implementation performs better than traditional approaches. This approach helps to design online learning management systems, different applications for mobile devices, checking cyber-criminal activities, emotion detection for older people, automatic speaker identification and authentication, forensics, and surveillance. (2023), (Iranian Academic Center for Education). All Rights Reserved. -
Determination of Discharge Distribution in Meandering Compound Channels Using Machine Learning Techniques
Accurate flow rate prediction is essential to analyze flood control, sediment transport, riverbank protection, and so forth. The flow rate distribution becomes even more complicated in compound channels due to the momentum transfer between different subsections across the width of the channel. Conventional channel division methods estimate flow distribution at the main channel and floodplains by assuming a division line with zero apparent shear stress. The article attempts to develop a model to calculate the percentage of discharge in the main channel (%Qmc) using techniques such as Group Method of Data Handling - Neural Network (GMDH-NN) and gene-expression programming (GEP) by incorporating the effects of various geometric and hydraulic parameters. The paper proposes a modified channel division method with a variable-inclined interface, with zero apparent shear force distribution at the channel subsections according to the statistical indices employed to assess these models' performance in predicting %Qmc. This variable-inclined interface changes its slope according to the channel parameters. The model's effectiveness is verified by validating with experimental observations by conventional analytical methods. 2021 American Society of Civil Engineers. -
Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach
Accurate prediction of shear stress distribution along the boundary in an open channel is the key to solving numerous critical engineering problems such as flood control, sediment transport, riverbank protection, and others. Similarly, the estimation of flow discharge in flood conditions is also challenging for engineers and scientists. The flow structure in compound channels becomes complicated due to the transfer of momentum between the deep main channel and the adjoining floodplains, which affects the distribution of shear force and flow rate across the width. Percentage sharing of shear force at floodplain (%Sfp) is dependent on the non-dimensional parameters like width ratio of the channel (?) , relative depth (?) , sinuosity (s) , longitudinal channel bed slope (So) , meander belt width ratio (?) , and differential roughness (?). In this paper, various artificial intelligence approaches such as multivariate adaptive regression spline (MARS), group method of data handling Neural Network (GMDH-NN), and gene-expression programming (GEP) are adopted to construct model equations for determining %Sfp for meandering compound channels with relative roughness. The influence of each parameter used in the model for predicting the %Sfp is also analyzed through sensitivity analysis. Statistical indices are employed to assess the performance of these models. Validation of the developed %Sfp model is performed for the experimental observations by conventional analytical models; to verify their effectiveness. Results indicate that the proposed GMDH-NN model predicted the %Sfp satisfactorily with the coefficient of determination (R2) of 0.98 and 0.97 and mean absolute percentage error (MAPE) of 0.05% and 0.04% for training and testing dataset, respectively as compared to GEP and MARS. The developed model is also validated with various sinuous channels having sinuosity 1.343, 1.91 and 2.06. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel
Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5m and 8.63m, respectively. 2020, Springer Nature B.V. -
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Effect of Short Glass Fiber Addition on Flexural and Impact Behavior of 3D Printed Polymer Composites
Fused deposition modeling (FDM), one of the most widely used additive manufacturing (AM) processes, is used for fabrication of 3D models from computer-aided design data using various materials for a wide scope of applications. The principle of FDM or, in general, AM plays an important role in minimizing the ill effects of manufacturing on the environment. Among the various available reinforcements, short glass fiber (SGF), one of the strong reinforcement materials available, is used as a reinforcement in the acrylonitrile butadiene styrene (ABS) matrix. At the outset, very limited research has been carried out till date in the analysis of the impact and flexural strength of the SGF-reinforced ABS polymer composite developed by the FDM process. In this regard, the present research investigates the impact and flexural strength of SGF-ABS polymer composites by the addition of 15 and 30 wt % SGF to ABS. The tests were conducted as per ASTM standards. Increments in flexural and impact properties were observed with the addition of SGF to ABS. The increment of 42% in impact strength was noted for the addition of 15 wt % SGF and 54% increase with the addition of 30 wt % SGF. On similar lines, flexural properties also showed improved values of 44 and 59% for the addition of 15 and 30 wt % SGF to ABS. SGF addition greatly enhanced the properties of flexural and impact strength and has paved the path for the exploration of varied values of reinforcement into the matrix. 2023 The Authors. Published by American Chemical Society -
Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system
The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison. 2022 The Authors -
A Study on the Factors Influencing Customer Satisfaction in Multi-brand Apparel Retail
International Academic Research Journal of Business and Management Vol.1, Issue No. 7 ISSN No. 2227-1287