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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. -
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. -
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. -
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. -
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. -
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 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. -
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. -
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. -
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. -
Introduction
[No abstract available] -
Sustaining livelihoods and culture through tourism development: The case of sriniketan in West Bengal, India
The rural area of Sriniketan in West Bengal, India is full of cultural embodiments that can not only serve as a base to develop tourism but also generate sustainable livelihoods. However, the Sriniketan region suffers from chronic poverty and its unique culture is getting depleted thanks to the lack of awareness and interest among locals. With the help of the DFID Sustainable Livelihood Framework (DFID-SLF), this study tries to analyse the contributions that culture-based tourism can make towards generating sustainable livelihoods at Sriniketan. A modified SLF has been prepared with an added element of cultural capital as a contribution to the existing livelihood literature and guiding sheet for future practitioners. Based on the primary (in-depth interviews of fifteen households and five key respondents) and secondary data collected, this paper concludes that tourism development in Sriniketan can not only aid its cultural preservation but also generate an alternate source of livelihood and thereby, making both (culture and livelihoods) sustainable 2023, IGI Global. All rights reserved. -
Gamification in Tourism Teaching and Learning: Exploring the Emergent Dimensions
First introduced in 2002 by British Programmer Nick Pelling, the term gamification has gained massive momentum in the last decade with extensive applications in teaching and learning (Hebebci & Selahattin, Current Studies in Social Sciences 2021:174, 2021). In its simplest form, gamification refers to the adoption of principles and design elements of various games in nongame situations (Deterding et al. From game design elements to gamefulness: Defining gamification. In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments (pp. 915), 2011). Further, the interaction between the students and technological interventions is rising, opening new avenues for innovative strategies like gamification to promote effective teaching and learning (Kapp, The gamification of learning and instruction: Game-based methods and strategies for training and education. Wiley, 2012). In the context of tourism, works of (Nair, B. B. Endorsing gamification pedagogy as a helpful strategy to offset the COVID-19 induced disruptions in tourism education. Journal of Hospitality, Leisure, Sport & Tourism Education, 30, 100362, 2022.) and Aguiar-Castillo, Herndez-Lez, De SaPez, and Pez-JImez (Journal of Hospitality, Leisure, Sport & Tourism Education, 27, 100267. https://doi.org/10.1016/j.jhlste.2020.100267, 2020) provide insights into the various facets of gamification as a tool for teaching and learning. However, these works hardly address the emerging issues in adoption, implementation, and barriers that have been dealt with in this chapter. Against this backdrop, the first section of this paper ventures into the various implications of gamification in the field of tourism teaching and learning. Next, taking cues from significant land marking studies, this work lists the factors responsible for making gamification an effective teaching and learning tool in tourism. Lastly, attempts have been made to underline the elements that may pose challenges in the path of the rise of gamification as a teaching and learning tool. This work can be primarily classified as a conceptual paper with a systematically drawn inductive approach. Majority of the paper has been drafted based on the review of critical works derived from the search of keywords such as gamification and tourism and teaching and learning on major search engines such as Scopus, Web of Science, and JSTOR. The authors conclude that gamification as a teaching and learning tool in tourism education is still its necesent stages of implementation, and there are an equal number of challenges that need to be addressed before it (gamification) becomes a mainstream tool in tourism teaching and learning. Springer Nature Singapore Pte Ltd 2024. -
Artificial Intelligence in Forecasting: Tools and Techniques
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how its accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Whispered speech emotion recognition with gender detection using hybridopti-gendernet
The Whispered Speech Emotion Recognition (SER) presents special challenges because it does not involve the vibration of vocal folds and less acoustic information, which makes it more difficult to identify the nuances of emotions and gender-related peculiarities than in normal speech. The research presents a new system, HybridOpti-GenderNet, of concurrent whispered speech emotion and gender recognition. It is a framework of six stages are data collection, pre-processing, feature extraction, feature selection, gender detection, and emotion recognition (ER). A Red Butterfly Optimization Algorithm (RBOA), which uses Butterfly Optimization Algorithm (BOA) and Red Kite Optimization Algorithm (ROA) by adjusting the weight to achieve optimal feature selection, is proposed to achieve a better balance between exploration and exploitation. Gender identification is carried out with the proposed HybridOpti-GenderNet, a new hybridization of Bi-LSTM and DCNN, and ER is accomplished with the help of the Attention-Enhanced Hybrid Belief Network (AHBN) that incorporates Deep Belief Network (DBN), Feedforward Neural Network (FNN) & a tailor-made attention mechanism to detect delicate emotional signals. The innovativeness of the given work is that the gender-informed recognition is combined with a high-level optimization and a hybrid deep learning pipeline that enhances the robustness of the analysis of whispered speech significantly. The system was tested in Python and tested on the GeWEC and EMO-DB data, showing superior performance in cases of privacy preservation, noisy conditions, and real-life human-computer interaction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations
Accurate documentation of meetings is essential for effective communication, decision-making, and accountability across various settings, including corporate environments, academic discussions, research collaborations, project reviews, and multi-stakeholder forums. Manual note-taking is often error-prone and time-consuming, highlighting the need for automated solutions. This paper proposes an LLM-based approach for generating structured Minutes of Meeting (MoM) from audio recordings. The method begins with audio transcription using the Vosk model, followed by grammatical and punctuation recovery to enhance text readability. The preprocessing step cleans and segments the transcribed text. Named Entity Recognition (NER) is applied to identify relevant entities such as names, dates, and organizations. The method then segments the conversation into topical sections aligned with the meeting agenda. A Large Language Model (LLM), named OpenAI, has been used to produce abstractive summaries. An action item extraction module identifies tasks, responsible individuals, and deadlines. A speaker analysis component highlights principal participants in decision-making processes. The system organizes the final output into a structured format, such as JSON or PDF, for easy access and distribution. This end-to-end approach enhances the clarity, accessibility, and utility of meeting documentation, reducing manual effort and improving organizational productivity. 2025 IEEE. -
Business Resilience
Business resilience has emerged as one of the key elements for ensuring the survival and success of the organization in the 21st century which has been marked by socio-economic crisis, notable disruptions, and tumultuous market shifts. The term Business resilience is defined as the capacity of an organization to navigate through adversity, adapt to changing conditions, and emerge stronger from setbacks. Financial, operational, organizational, digital, and supply chain resilience are among the measures that resilient organizations invest on. Companies that are financially strong put careful money management first and protect shareholders wealth. Organizational resilience promotes flexibility and ongoing learning, whereas operational resilience guarantees the continuation of vital business operations. While supply chain resilience concentrates on minimizing disruptions, digital resilience entails strong cybersecurity measures. Effective leadership is essential for managing uncertainty, and a flexible strategy for enhancing resilience guarantees continuous adjustment. In the end, corporate resilience is about overcoming hardship and integrating resilience as a strategic requirement for sustained success in a changing business climate. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
MetaMinds and digital realms: Deciphering Gen Z consumer behavior in metaverse marketing
This conceptual research paper explores the innovative convergence of virtual reality (VR), augmented reality (AR), and digital spaces as it dives into the complex dynamics of Gen Z consumer behavior within the metaverse. Businesses and scholars alike face hurdles in comprehending the complex preferences, motives, and decision-making processes of this generation of digital natives, as traditional marketing tactics undergo a metamorphosis in the aftermath of the metaverse. Understanding the nuances of Gen Z's interactions in this immersive digital world is crucial for businesses looking to connect and engage with this significant consumer generation as the metaverse becomes an increasingly important part of everyday life. Thus, this study also attempts to understand the awareness about metaverse among Gen Z and the challenges faced during the act of purchase in a meta environment. 2025 by Priyakrushna Mohanty, Aarthy Chellasamy and Aishwarya Nagarathinam. All rights reserved. -
Click, Pay, Sustain: The Emergence of Green Digital Payment Systems Within ESG Frameworks
Digital payment systems have become central to contemporary financial infrastructures, yet their contribution to sustainability and ESG outcomes remains conceptually fragmented. This study develops an integrative framework explaining how FinTech innovations shape sustainability in digital payment ecosystems. Drawing on a structured thematic analysis of academic and policy literature, the paper proposes the FinTech InnovationPerformanceAnalyticsESG (FIPAE) Framework, which positions technological innovation as a driver mediated by organisational performance and analytical capacities. The framework clarifies pathways linking digital paymentinnovation to environmentalresponsibility, governance compliance, and social impact, offering a foundation for future empirical research and policy-oriented analysis. Copyright 2026, IGI Global Scientific Publishing.