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The perception of a situation or reality determines how one responds and awareness is the first step towards understanding, knowing or recognizing it. The majority of the public and the police may be familiar with the phrase ‘cybercrime’, but all of them are fully informed of the nature and scope of these crimes, as well as of the cybercriminals and cyber victims, which has an impact on how they see these issues. This study's main goal was to examine the perception and awareness of cybercrime among undergraduate youth studying in BBA or BCA courses. In this study, we discovered that young people's responses to cybercrime mostly depend on their perceptions of it and their awareness level. To accomplish the study's objective, a thorough examination of existing literature was undertaken. Primary data of 200 students were collected through Google Forms. Percentile analysis, correlation analysis and t-test are done to test the hypotheses. The results of this study may help college administrators better comprehend the mindset of today's youth as they develop laws and policies aimed at reducing cybercrime among students. The results of this study show that the youngsters surveyed have high levels of awareness and a good perception.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

As the hospitality business adapts to the digital age, the importance of using Virtual Design (VD) to create memorable visitor experiences has grown. This study aims to investigate the potential of VD in the hospitality sector, particularly regarding the improvement of guests' overall experiences.

The research study examines how Virtual Reality (VR), Augmented Reality (AR) and other digital technologies are currently used in VD firms. It explores how these simulated architectural features are implemented in other facets of the hospitality experience, like the decor of guest rooms and restaurants and staff responsiveness to guests' needs.

The study also examines VD's potential outcomes and advantages for the hotel industry and its clients. It investigates the potential of VD to help hospitality businesses offer more customised services, boost customer loyalty and gain an edge in the market. VD implementation in the hospitality business may face several obstacles, some of which are discussed in this study.

Methods include both qualitative and quantitative techniques, such as interviews with experts, guest surveys and an examination of the use of VD in specific hotels. This study intends to help the hotel industry benefit from a VD by analysing real-world case studies and gathering empirical data that can be used to draw conclusions and formulate recommendations.