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Data and Its Dimensions
In current times Data is the biggest economic opportunity. As per the studies, it is observed that the world is becoming 2.5 quintillions data-rich every day, with an average of every human contributing 1.7MB of data per second. Every individual has a good appetite for data, as it gives immense insight to explore and expand the business. With the invention of smart devices and innovation in the field of connectivity such as 4G-5G Mobile Networks and Wi-Fi, the generation and consumption of the data are steadily increasing. These smart devices continuously generate data, leading to a bigger pool for better decision-making. This chapter presents data, the various forms and sources, and the concept of Data Science; it discusses how the ownership and value of data are decided; and also highlights the use, abuse, and overuse of the data along with data theft, and a case study to represent data breach. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Privacy
Data privacy is a private and public phenomenon and its operations have implications for the individual and the society. This understanding of privacy ceases it from being viewed as a simple technological process and highlights different factors that are associated with it. While on one hand, the right to privacy is seen as integral to the freedom of the individual, on the other hand, it is also seen as the ability to hide certain information for malpractice. This chapter delves into this existing dichotomy of data privacy and simplifies various terms and operations that have emerged in the field of study. A discussion is facilitated on the concept and its associated areas. The chapter looks at privacy regimes in different countries to note emerging developments and also presents a critique of the practice to bring forth shortcomings and enable change. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Influence of Customer Relationship Management for the Success of E-Business
Customer relationship management has recently been one of the key factors in the success of many organizations. Organizations have realized the importance of customer satisfaction and are integrating their operations with that of customer relationship to serve the customers in a better way. This paper seeks to understand the importance of CRM in e-business. It also talks about the importance of customer relationship for an organization in its growth. Relationship marketing has been studied to show how customer relationship management software can be made use of for the benefit of an organization. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Cryptocurrency Price Prediction Study Using Deep Learning and Machine Learning
A cryptocurrency is a network-based computerized exchange that makes imitation and double-spending pretty much impossible. Many cryptocurrencies are built on distributed networks based on blockchain technology, which is a distributed ledger enforced by a network of computers. Thanks to blockchain technology, transactions are secure, transparent, traceable, and immutable. As a result of these traits, cryptocurrency has increased in popularity, especially in the financial industry. This research looks at a few of the most popular and successful deep learning algorithms for predicting bitcoin prices. LSTM and Random Forest outperform our generalized regression neural architecture benchmarking system in terms of prediction. Bitcoin and Ethereum are the only cryptocurrencies supported. The approach can be used to calculate the value of a number of different cryptocurrencies. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comprehensive Data Analysis of Anticorrosion, Antifouling Agents, and the Efficiency of Corrosion Inhibitors in CO2 Pipelines
This study explores the various methods that are being proposed for their anticorrosion and antifouling capabilities and also reviews the unique properties that make them suitable for such applications. Special attention has also been given to the problem of corrosion in CO2 pipelines, considering the corrosion inhibitors currently being used and performing statistical analysis about if and how various factors such as temperature, flow velocity, pH, and CO2 pressure affect the rate of corrosion of the CO2 pipelines. Tests including ANOVA, correlation, and graph analyses were conducted to explore their relationships, and suitable conclusions were drawn for the data collected. 2024 Scrivener Publishing LLC. -
Carbon Nanotube-Polymer Nanocomposites for Energy Storage and Conversion
A large global commitment is necessary to scale up the deployment of renewable energy, engage in research and development, and implement energy-efficient practices. The development of large-scale energy storage technologies is crucial to fully harness renewable resources, ensure grid stability, and facilitate a more sustainable and reliable energy future. This becomes increasingly important as the demand for clean and renewable energy grows. Polymer nanocomposites have demonstrated considerable promise in energy storage and conversion. These nanocomposites can have better mechanical strength, electrical conductivity, thermal stability, and electrochemical performance due to adding nanoparticles or nanofillers to polymer matrices. Although carbon nanotubes (CNTs) cansignificantly enhance the characteristics of polymers at extremely low filler loadings, they are the perfect filler for both structural and functional applications. An extensive review of current studies on the synthesis and modification of polymer nanocomposites reinforced with CNTs is given in this chapter. To promote this new subject, it also severely evaluates a number of applications pertaining to energy conversion and storage. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Carbon Nanotubes for Supercapacitors
Supercapacitors are energy storage devices that boast significant capacitance, enhanced energy density, rapid charge/discharge cycles, minimal heat generation, safety, sustainability with no expendable components, and extended durability. Supercapacitors, due to their unique characteristics, are increasingly favoured in consumer electronics and as alternate energy solutions. Carbon nanotubes (CNTs) have emerged as a promising material for supercapacitor electrodes, thanks to their remarkable features like exceptional conductivity, large surface area, robust mechanical strength, and chemical stability. The objective is to offer a comprehensive understanding of the pros and cons of supercapacitor materials involving CNTs and to pinpoint ways to boost their efficiency. This also entails examining how the inherent physical and chemical traits of pure CNTs, such as their size, quality, imperfections, shape, modifications, and treatment processes, influence their capacitance. Moreover, a deeper dive into composites, like CNTs combined with oxides, polymers, and other hybrid materials, aims to customize their composition and characteristics to optimize capacitance while ensuring the devices longevity. This section also compiles the latest studies on various CNT composites as potential supercapacitor electrode materials. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Exploring the ethical issues and considerations in neuromarketing
Neuromarketing is an interdisciplinary field consolidating neuroscience, psychology, and marketing, aiming to understand consumer behaviour at the subconscious level. However, as neuromarketing techniques become increasingly sophisticated, ethical issues and considerations have emerged as a focal point of debate and scrutiny. The paper critically evaluates foundational ethical principles, such as informed consent, beneficence and nonmaleficence, privacy and confidentiality, transparency, scientific or methodological rigor, predicting and influencing consumer choices, safeguarding the vulnerable population, and commitment to abiding and respecting the guidelines and codes of ethics. It also includes the emerging techniques and research, need for ethics and terms like neuroethics and brain privacy. 2024, IGI Global. All rights reserved. -
Twitter Sentiment Analysis Based on Neural Network Techniques
Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
AI in Data Recovery and Data Analysis
The use of artificial intelligence (AI) techniques for data collection and analysis is examined in this chapter. It also looks at the benefits, challenges, and future directions. It provides a broad overview of AI techniques and illustrates the use of generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. in this area. Data recovery is an essential process when trying to recover lost or damaged data. For AI methods like CNN, the retrieval of image and video data has shown great promise. Using the power of deep learning, CNNs can search for patterns in data, assisting in the reconstruction and restoration of lost information. On the other hand, RNNs excel at retrieving serial data, such as text or time series data. These networks can efficiently learn dependencies and contexts, which makes it possible to precisely reconstruct missing or imperfect sequences. AI-based data analytics provides businesses with insightful information and opportunities. GANs, for example, are increasingly being used to generate and improve data, enabling organizations to expand the size of their datasets and improve the efficacy of their analytical models. Large amounts of data can also be divided up using A-based clustering algorithms, which are also well classified and provide insightful analysis and interpretation. In the gathering and analysis of data, AI has many benefits. Businesses can process and analyze enormous amounts of data in a fraction of the time thanks to this productivity-boosting automation of challenging and time-consuming tasks. By reducing bias and human error, AI techniques also increase accuracy, resulting in results that are more dependable and consistent. Additionally, AI-driven insights assist businesses in spotting trends, uncovering buried patterns, and coming to wise decisions that may not be apparent using traditional analytics methods. Due to privacy concerns, ethical considerations, interpretability, transparency, and accountability, AI deployment in data recovery and analysis is difficult. Future directions include collaboration between humans and AI, edge computing integration, and privacy-preserving methods. In conclusion, organizations looking to maximize their data assets stand to benefit greatly from the application of AI techniques to data analytics and data retrieval. 2024 selection and editorial matter, Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, and Naveen Kumar. -
Beyond Teacher Quality: Understanding the Moderating Role of Infrastructure in Student Learning Outcomes in Secondary Education
Education is an essential resource for individuals and societies, and it plays a significant role in shaping the future of any nation. Depriving a generation of young children of their basic right to quality education can easily be regarded as the highest form of injustice in a society. Bihar, which was once the epitome of education and knowledge across the world, is now counted among the states with the lowest literacy rates and the poorest educational infrastructure. While a list of reasons can be enumerated behind this downfall, including historic and social reasons, it is prudent to act on those that we can effectively alter and improve upon, such as infrastructure and teaching quality. The quality of education provided to students is influenced by various factors, such as infrastructure, teacher quality, and student-teacher relationships. This study explores the moderating effect of infrastructure on the relationship between teacher quality and student outcome in secondary education in Bihar, mapping an intriguing contrast with Kerala, the state with the highest literacy rate in India. With the help of a simple moderation analysis and drawing on the resource dependency theory, our findings indicate that the moderation effect of infrastructure on student outcome is stronger in Bihar than in Kerala. This study highlights the urgent need to prioritise consolidating and enhancing the quality of education in schools in Bihar rather than adding up a number of concrete blocks. 2024 Patliputra School of Economics. -
Critical Factors Leading to Sustainable Initiatives in the Global Market
With the emergence of digital technologies, no sector has remained untouched from the influence and application of digitalization. The future is moving more toward implementing and operating things digitally. Sustainable development aims to achieve a better and secure future, thus the need for renewal of resources. To secure the future of all, there is a need to meet human development goals by preserving the natural resources on which the economy depends. Sustainable development concentrates on economic development, social development, and environmental development. The natural system, or resources, has significant importance on the economic as well as social development. There are many areas of influence that lead to the need of sustainable development. This chapter provides thorough and deep insight into sustainable development and its implementation in digital technologies. The chapter covers, but is not limited to, the following: Introduction to sustainable development History of sustainable development Factors influencing sustainable development Underlying goals of sustainable development. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
The youth's way of personal branding as bookstagrammers
The Bookstagrammers use Instagram to post about their life as avid readers and regard themselves as social media influencers (SMIs) for books. Creating a personal brand helps influencers differentiate themselves from other users. Similarly, Bookstagrammers have made a place for themselves as book influencers in this growing SMI market. Since Bookstagrammers have the potential to influence the publishing industry's sales, creating a personal brand plays a significant role in their career as an influencer. Some Bookstagrammers have successfully created their personal brand by posting book-related and reader-centric content on Instagram and are followed by a niche audience of readers. This study conducted content analysis on India's two most popular Bookstagrammers, and discusses their personal branding strategies. The results showcase 8 broad categories of content shared by the Bookstagrammer that are mapped to three elements of personal branding: brand identity, brand positioning, and brand image, showing how social media fuels the youth's creativity to as SMIs. 2023, IGI Global. All rights reserved. -
Bookstagrammers vs. BookTubers: A comparative study on readers' preferred social media book influencer
As the Internet has become a part of many people's daily lives, it has led to the growth of a reading culture influenced by book bloggers on different social media platforms. This chapter identifies two social media platforms that the readers utilize to share about the books they have read. While readers have found their reading space on social media platforms, some have become book influencers. This chapter identifies two categories of prominent literary influencers i.e., Bookstagrammers and BookTubers. Since the readers follow book influencers to learn about the latest books and to read their reviews before making their purchase decision. This chapter aims to compare and analyse the prominent categories of book influencers focusing on knowing more about the preferred book influencers from the readers' point of view. 2024, IGI Global. All rights reserved. -
IoT networks: Integrated learning for privacy-preserving machine learning
Financial fraud is a persistent problem for consumers and financial institutions worldwide. It loses billions of dollars annually. Consequently, a strong fraud detection system (FDS) is essential to minimizing damage to financial institutions as well as clients. One common technique for spotting fraud is to use machine learning algorithms, which analyze large volumes of data to help with pattern detection and future prediction. It is difficult for a centralized FDS to detect fraud trends when these problems are coupled. To train a fraud detection model, this work presents a framework for federated learning, a machine-learning environment in which several entities collaborate to solve a machine-learning problem under the guidance of a central server or service provider. Also, the chapter examines how combined learning can be used to protect privacy in machine learning in Internet of Things systems. It focuses on four main calculations: federated averaging (FedAvg), secure aggregation, holomorphic encryption-based federated learning, and differential privacy in combined learning. Extensive experiments were carried out to evaluate these computations in terms of proving accuracy, conserving protection, and computing efficiency. The findings are shown in the results, with FedAvg achieving the highest accuracy of 92.5% and secure conglomeration demonstrating competitive precision levels of 91.8%. Calculations for differential privacy and holomorphic encryption demonstrated strong security conservation with very little data leakage and security parameters of 2.5 and 1.0, respectively. With little communication overhead and the ability to alter accuracy and conserve protection, secure aggregation emerged as a potential configuration. The computational productivity assessments revealed that secure accumulation produced little communication overhead despite its strong security conservation, which makes it suitable for IoT scenarios with limited resources. By using this tactic, financial institutions may avoid sharing datasets and benefit from a shared model that has seen more fraud than any one bank has on its own. Thus, the sensitive data of the user is protected. The results of the chapter indicate that the federated model (federated averaging) may be as good as or better than the central model (multi-layer perceptron) in detecting financial fraud. This chapter adds to the growing conversation around mixed learning in the Internet of Things by providing insights into the trade-offs between accuracy, security, and efficacy and by laying the groundwork for future developments in privacy-preserving machine learning standards. 2025 selection and editorial matter, Ahmed A. Elngar, Diego Oliva and Valentina E. Balas. All rights reserved. -
IoT-Based automated dust bins and improved waste optimization techniques for Smart City
Effective waste management systems are essential for maintaining sustainability, environmental health, and cleanliness in the age of smart cities. This chapter provides a thorough analysis of the combination of cutting-edge waste minimization techniques with the deployment of an internet of things-based automatic dust bin. The suggested system optimizes garbage collection, lowers operating costs, and has a less environmental effect by combining the creative use of proximity sensors, real-time data analytics, and smart bin technologies. Remote monitoring and administration are made possible by the linked ecosystem that is created by the integration with the internet of things (IoT). In order to further promote environmentally friendly urban life, the study also examines waste-to-energy technology, circular economy ideas, and sustainable waste management techniques. The results provide insightful information for scholars, decision-makers, and urban planners looking for ground-breaking waste management solutions for today's cities. 2024, IGI Global. -
The Accountability of Stakeholders in Combating Domestic Violence with Women in India
As per the Hindu tradition, women are considered as ardhangini and the western civilisation considers them as better half. Since ancient times, women have been considered as the epitome of love, kindness, care and above all the mother of mankind. But on the contrary, the most terrible and horrifying cruelties are imposed upon her. The predominant type of violence which is inflicted upon a woman is domestic violence also known as intimate partner violence. According to the World Health Organisation, almost one third, i.e., 27% of the women aged between 15 and 49 years, who have been in a relationship report that they have been subjected to some form of physical or sexual violence by their intimate partner. The issue of domestic violence has been addressed at both international and national levels, but still there exists a persistent gap in enforcing and implementing them. And this can be easily proved from the continuous rise in the cases of domestic violence worldwide, especially during the COVID times. The problem that needs to be addressed at hand is the role of several accountable stakeholders involved in the process of providing access to justice to the women affected by domestic violence. According to the Protection of Women from Domestic Violence Act (2005), in India, the several stakeholders include the protection officers, service providers, lawyers, police officers, shelter homes and judges. These are the people with whom the power to protect and help the women are vested, but instead the affected women fall a prey to this system due to various reasons like lack of resources, fewer power and many more which will be further discussed in the article. 2024 selection and editorial matter Dr. Shilpi Sharma and Baidya nath Mukherjee; individual chapters, the contributors. -
A comprehensive view of artificial intelligence (ai)-based technologies for sustainable development goals (sdgs)
Agenda 2030, aimed at sustainable and inclusive development through seventeen SDGs formulated by the United Nations (UN), has become a massive challenge for most nations around the world. Many countries are setting a plan of action for achieving carbon neutrality by 2050. Due to this, industries are under immense pressure to mitigate harmful emissions and incorporate SD in their business activities. In the past decade, AI has grown as the dominating technology which influences nearly every aspect of human life, i.e., society, business, environment, etc. This chapter provides a comprehensive view of AI-driven technological applications in achieving SDGs. It provides a snapshot of the emerging relationship between AI applications and sustainable development and how AI could be used to create sustainable business models. Large-scale adoption of AI-driven technologies has enormous potential from the sustainable development perspective. The purpose of this chapter is to map the application of AI-based technological tools and solutions with the various SDGs. Further, this chapter also extends the discussion on AI-based technology as an enabler of or barrier to addressing sustainable development issues. It provides an important insight for policymakers, practitioners, investors, and other stakeholders about the conducive influence of AI on society, governance, and ecology in line with the priorities underlined in the UN SDGs. 2024 Walter de Gruyter GmbH, Berlin/Boston.