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Bibliometric Analysis of AI Research in Sustainable Smart Cities
Smart cities have the potential to improve city-wide governance, environmental sustainability, sustainable transportation, and economic growth. Urban areas may find these advantages useful in their pursuit of SDG-11 objectives. A key component of smart city architecture is the addition of artificial intelligence (AI) and other smart technology into urban areas. The Artificial Neural Network (ANN) is a major machine learning approach. A number of review studies have already been published, reflecting the substantial interest in artificial neural networks (ANN) for smart city applications. In the past, researchers have shown an interest in studying structural monitoring applications, transportation systems, cybersecurity, and the Internet of Things (IoT). But knowledge about how ANN can help Smart Cities achieve SDG-11 is limited. This paper provides a systematic bibliometric analysis of present research trends on artificial neural networks for smart cities, with an emphasis on SDG-11. The research employed a keyword-based search to obtain 131 papers for content analysis and 743 papers for descriptive analysis. Both the amount of interest in the topic and the tendency for related topics to cluster have increased exponentially, according to the findings. Urbanization, Transportation, and Eco-friendly were identified as the main topics of this study. Specifically, this evaluation focuses on particular SDG-11 issues and provides insights on research trends and thematic importance. 2025 Saravanan Krishnan, A. Jose Anand and Raghvendra Kumar. -
Bibliometric Analysis of Gifts in the Era of E-Commerce: A Data Mining Approach
Gifting is a universal phenomenon. It is deeply connected with history, human culture, social interactions, and economic activities. This study aims to look at the work of various researchers on online gifting. The keywords (gift OR gifts) AND (online OR electronic OR e-commerce OR virtual) were used on the Scopus Database. Bibliometric analysis was conducted on 397 relevant publications, which were filtered and selected from the list of 1398 documents. Analysis through Term co-occurrence map, Network visualization map of terms in title/abstract fields, and Topic trends, among others, was done. Four primary clusters were found in the Term co-occurrence map as well as Network Visualization Map Most of the research was from the USA and China. The multi-disciplinary element of gifting is visible in the analysis. Some of the emerging topics were virtual reality, live streaming, social networking, advertising, and online shopping. The impact of gifts in promotions and marketing showed the potential of gifts as a major tool for marketers. The study was limited to only the Scopus database and gives insights into the evolution of online gifting behaviour. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Bibliometric analysis of the impact of blockchain technology on the tourism industry
The tourism sector is one of the world's fastest-expanding industries. Because of the benefits, it provides to individuals and organizations, the tourism sector has attracted a lot of attention throughout the years. But because of its poor and obsolete data management techniques, this industry is in desperate need of reform. Blockchain technology is one method for managing and exploring data relevant to the tourism industry. This study used bibliometric methods to analyze the impact of blockchain technology on the tourism sector from 2017 to 2022. The publications were extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The findings provided valuable information on the publication year, authors, author's country, author's organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers may be able to design their studies better and add more insights into their empirical studies. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Bibliometric Analysis on Multiobjective Optimization and Metaheuristic Algorithm
For difficult optimization issues, metaheuristic algorithms are effective methods for obtaining workable solutions quickly. In the past few years, continuous efforts have been put forward by researchers to develop new effective and robust metaheuristic algorithms for solving engineering optimization problems. The research aims to find the advancements made in multi-objective optimization and metaheuristic algorithms. Metadata of 4149 articles were extracted from Scopus from the year 2000 onward and bibliometric analysis was done with the help of the VOSviewer software. It was found that Mirjalili. S. has the highest number of citations (4011). IEEE Access has published the maximum number of documents (128), the University of Tehrans School of Industrial Engineering has contributed the most in this field of research (43 documents), and China has the most contribution among all the countries with 977 documents. In recent years, the terms optimization algorithms, exploration and exploitation, learning systems, decision-making, uncertainty analysis, sustainable development, supply chains, neural networks, forecasting, machine learning, and cloud computing are being mostly used by researchers. 2026 by Apple Academic Press, Inc. -
Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET
In recent years, Traffic management and road safety has become a major concern for all countries around the globe. Many techniques and applications based on Intelligent Transportation Systems came into existence for road safety, traffic management and infotainment. To support the Intelligent Transport System, VANET has been implemented. With the highly dynamic nature of VANET and frequently changing topology network with high mobility of vehicles or nodes, dissemination of messages becomes a challenge. Clustering Technique is one of the methods which enhances network performance by maintaining communication link stability, sharing network resources, timely dissemination of information and making the network more reliable by using network bandwidth efficiently. This study uses bibliometric analysis to understand the impact of Clustering techniques on VANET from 2017 to 2022. The objective of the study was to understand the trends & advancement in clustering in VANET through bibliometric analysis. The publications were extracted from the Dimension database and the VOS viewer was used to visualize the research patterns. The findings provided valuable information on the publication author, authors country, year, authors organization affiliation, publication journal, citation etc. Based on the findings of this analysis, the other researchers may be able to design their studies better and add more perception or understanding to their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Bibliometric Insights into the Nexus of Digital HR, Innovation, and Sustainability: Toward a Smart Workforce
HR professionals use AI, blockchain, cloud computing, big data analytics, and Metaverse to optimize the workforce as technology advances. These technologies boost corporate value, employee performance, and smart workforce development. Metaverse improves virtual reality training, 3D simulations, and wearable self-tracking technologies. Cloud computing simplifies simulations and collaborative mixed reality for employees. AI tools usage increases an organization's staff efficiency. Smart workforce tactics and workplace technologies improve success and human experience management, especially in virtual, remote, and collaborative work contexts. Many companies have failed to integrate Metaverse in the workplace despite advances in digital technologies. A Biblioshiny analysis- based systematic assessment of human capital management automation systems addresses this gap. This study examines smart workforce requirements and future automation trends at the organizational, managerial, and individual levels. Additionally, this study allows for the creation of a self-sustaining virtual HR system. 2025 Scrivener Publishing LLC. All rights reserved. -
Bifunctional Amorphous Transition-Metal Phospho-Boride Electrocatalysts for Selective Alkaline Seawater Splitting at a Current Density of 2Acm?2
Hydrogen production by direct seawater electrolysis is an alternative technology to conventional freshwater electrolysis, mainly owing to the vast abundance of seawater reserves on earth. However, the lack of robust, active, and selective electrocatalysts that can withstand the harsh and corrosive saline conditions of seawater greatly hinders its industrial viability. Herein, a series of amorphous transition-metal phospho-borides, namely Co-P-B, Ni-P-B, and Fe-P-B are prepared by simple chemical reduction method and screened for overall alkaline seawater electrolysis. Co-P-B is found to be the best of the lot, requiring low overpotentials of ?270mV for hydrogen evolution reaction (HER), ?410mV for oxygen evolution reaction (OER), and an overall voltage of 2.50V to reach a current density of 2Acm?2 in highly alkaline natural seawater. Furthermore, the optimized electrocatalyst shows formidable stability after 10,000 cycles and 30h of chronoamperometric measurements in alkaline natural seawater without any chlorine evolution, even at higher current densities. A detailed understanding of not only HER and OER but also chlorine evolution reaction (ClER) on the Co-P-B surface is obtained by computational analysis, which also sheds light on the selectivity and stability of the catalyst at high current densities. 2024 The Authors. Small Methods published by Wiley-VCH GmbH. -
Bifunctional CoPBO/Co-MOF composite electrocatalyst for energy-efficient hydrogen evolution by urea-assisted water splitting
Urea oxidation reaction (UOR) offers a lower energy alternative to generate hydrogen from urea-based wastewater while simultaneously contributing to environmental remediation. However, the commercial viability of this process is hindered by the inability of the electrocatalyst to achieve higher current densities for UOR due to the competition with the OER. In this study, a cobalt-MOF-derived CoPBO/Co-MOF composite electrocatalyst was synthesized over Ni foam using a solvothermal method followed by a simple chemical reduction method for UOR. The CoPBO/Co-MOF@NF demonstrated excellent electrocatalytic bifunctional activity with low potentials of +1.32 V and ?0.095 V for UOR and HER, respectively, at 100 mA/cm2 in 1 M KOH +0.33 M urea solution. Under industrial-level alkaline conditions (6 M KOH), the potential requirement for UOR is further decreased to 1.14 V, also achieving a high current density of 1 A/cm2 at only 1.35 V, which is below the thermoneutral voltage for water splitting. Comprehensive electrochemical kinetic analysis revealed that the CoPBO/Co-MOF composite effectively combines the attributes of CoPBO, for strong OH? adsorption and CoOOH formation, with the affinity of Co-MOF for urea adsorption and CO2 desorption, leading to enhanced UOR performance. Furthermore, in a zero-gap electrolyzer configuration, the CoPBO/Co-MOF@NF catalyst demonstrated remarkable efficiency in actual cow urine (with 1 M KOH), requiring only 1.39 V to achieve a current density of 100 mA/cm2 which is 0.5 V lower than in urea-free water splitting. 2025 -
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services
The book provides a comprehensive discussion for utilizing computational models such as artificial neural networks, agent-based models, and decision field theory, for reliability engineering. It further presents optimization solutions for smart and secure healthcare services. The text showcases how to predict the failure and repair rates of healthcare subsystems using computational intelligence. This book: Explores how data-driven methodologies and advanced computational intelligence are revolutionizing the healthcare industry, promoting efficiency, accessibility, and sustainability Highlights the pivotal role that big data analytics plays in harnessing vast amounts of patient records, clinical information, and real-time medical data to provide timely insights for healthcare professionals and policymakers Discusses the integration of artificial intelligence and machine learning techniques in healthcare, with a focus on revolutionizing disease detection, treatment planning, and resource allocation Lays the foundation for developing sustainable healthcare systems that are adaptable to long-term challenges, such as population growth, emerging diseases, and resource constraints Covers computational intelligence techniques, like fuzzy logic, neural networks, and evolutionary computations, emphasizing their role in solving complex, data-driven healthcare problems Includes topics like data management, visualization, protection, and complex adaptive systems, as well as hybrid computational intelligence techniques for synergistic problem-solving strategies This volume will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer engineering, and mathematics. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Big data analytics in tourism development and marketing: Theoretical perspectives on big data analytics in tourism marketing
The title of the suggested book chapter is " Theoretical Perspectives on Big Data Analytics in Tourism Marketing" and it is about the influence of big data analytics in the growth and promotion of tourism. It just shows how the AI and Metaverse can strategically use big data for better Market Segmentation and Customer behaviour analysis. This chapter looks at how metaverse technology allows tourists to participate in virtual experiences. Tourism companies can refine their marketing strategies, streamline operations, and provide value added experiences to their consumers by utilizing big data analytics. This Chapter underlines the power that big data has to change the tourism industry by enhancing decision making and spurring innovation in service provision. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Big data analytics lifecycle
Big data analysis is the process of looking through and gleaning important insights from enormous, intricate datasets that are too diverse and massive to be processed via conventional data processing techniques. To find patterns, trends, correlations, and other important information entails gathering, storing, managing, and analyzing massive amounts of data. Datasets that exhibit the three Vs-volume, velocity, and variety-are referred to as "big data. " The vast amount of data produced from numerous sources, including social media, sensors, devices, transactions, and more, is referred to as volume. The rate at which data is generated and must be processed in real-time or very close to real-time is referred to as velocity. Data that is different in its sorts and formats, such as structured, semi-structured, and unstructured data, is referred to as being varied. 2024, IGI Global. All rights reserved. -
Big Data Analytics Tools and Applications for Modern Business World
In the modern world, data is the unavoidable word. The digital environment in almost all our day to day life is linked with digital data. Effective data management is one of the important tasks. The gradual growth of technology in recent years, the generation of data has increased exponentially. Everything, ranging from sending a mail to simply browsing the internet generates data and this is collected and stored. This data has countless uses in various fields such as medicine, business, agriculture and marketing, but most of the time it goes unused. Business intelligence is a key factor in the current business world. Business growth is purely depending on technology. Technology is not only used in manufacturing it is applied to getting the customer. The data analytics is still in its earlier stages and has a long way to go before it yields favourable results. It is a good time as any to start working in this domain to utilize its prowess. This article has discussed the opportunities and growth of data analytics in the research domain. It can face soon when it reaches its advance stages. The big data is handling a larger amount of data in a conventional and non-conventional manner. Technology is playing a vital role to handle larger data from the database. This article is to discuss data analytics application in modern industry. In the technical perspective, big data Map-reduce is an advanced tool and for simulation part, R tool is used. 2020 IEEE. -
Big Data Analytics: A Trading Strategy of NSE Stocks Using Bollinger Bands Analysis
The availability of huge distributed computing power using frameworks like Hadoop and Spark has facilitated algorithmic trading employing technical analysis of Big Data. We used the conventional Bollinger Bands set at two standard deviations based on a band of moving average over 20 minute-by-minute price values. The Nifty 50, a portfolio of blue chip companies, is a stock index of National Stock Exchange (NSE) of India reflecting the overall market sentiment. In this work, we analyze the intraday trading strategy employing the concept of Bollinger Bands to identify stocks that generates maximum profit. We have also examined the profits generated over one trading year. The tick-by-tick stock market data has been sourced from the NSE and was purchased by Amrita School of Business. The tick-by-tick data being typically Big Data was converted to a minute data on a distributed Spark platform prior to the analysis. 2019, Springer Nature Singapore Pte Ltd. -
Big Data and Artificial Intelligence for Strategic Human Resource Management
In this modern world all the organizations are adopting the new technology and making the use of modern technologies to match the HR procedures with the strategic Big Data and AI enhance HR decision-making by providing insights into workforce demographics, performance patterns, and employee behavior. Together, they automate tasks like hiring, training, and performance reviews, while addressing skill gaps, talent acquisition, and retention with unmatched precision. The study examines several important applications, including predictive analysis for workforce planning, AI driven recruitment system and real-time employee sentiment analysis. Additionally, it looks at data protection issues, ethical issues and the necessity of HR personnel being skilled in order to use these technologies effectively. The study demonstrates how Big Data and AI have the ability to change SHRM from a reactive role into a proactive, value-creating discipline by examining case-studies and new trends. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Big data and artificial intelligence: Creative tools for destination competitiveness
With the advancement of ICT, the tourism industry has undergone a digital transformation where management, marketing, and communication are largely using web based applications. Automation of processes like ticketing and reservation, online hotel booking, E visa processing, etc., indicates the significant reliance of the sector on technology and world wide web for its services. Big data and artificial intelligence are a fairly new and innovative approach to addressing this issue of managing and analyzing huge datasets collected from multiple sources. This chapter focuses on understanding the role and importance of big data and artificial intelligence in the tourism industry and its impact on improving the overall image and attractiveness of destinations. Copyright 2023, IGI Global. -
Big Data and Competition Law: A New Challenge for Competition Authorities
Big data has become a key role player for almost all kinds of markets specifically in a digital economy. It is a raw material as well as a by-product of any process. It has very comprehensive inclusivity to cover all aspects of the market having direct as well as indirect market effects. These effects are inclined towards consumerism and market transparency. But it has inherent dangers that are somehow overlooked by competition authorities. Competition law has dealt with the brick-and-mortar economy that is traditional in a very efficient way. However, this is not the case with the digital economy. Traditional notions of the market, abuse of dominant position, anticompetitive practices, and regulation of combinations cannot be made applicable to the digital economy in the same manner. Big data analytics enables big giants or corporations to establish their dominance in their relevant market. Google, Amazon, Facebook, and Apple have been dominating almost digital economy; hence their strategies are being scrutinized under the lenses of competition law once again. This paper deals with the interplay between big data and competition law, and it is going to explore the impact of this unavoidable aspect of big data on a highly competitive digital economy. 2024 Taylor & Francis. -
Big Data De-duplication using modified SHA algorithm in cloud servers for optimal capacity utilization and reduced transmission bandwidth; [Big Data Deduplicaci utilizando algoritmo SHA modificado en servidores en la nube para una utilizaci tima de la capacidad y un ancho de banda de transmisi reducido]
Data de-duplication in cloud storage is crucial for optimizing resource utilization and reducing transmission overhead. By eliminating redundant copies of data, it enhances storage efficiency, lowers costs, and minimizes network bandwidth requirements, thereby improving overall performance and scalability of cloud-based systems. The research investigates the critical intersection of data de-duplication (DD) and privacy concerns within cloud storage services. Distributed Data (DD), a widely employed technique in these services and aims to enhance capacity utilization and reduce transmission bandwidth. However, it poses challenges to information privacy, typically addressed through encoding mechanisms. One significant approach to mitigating this conflict is hierarchical approved de-duplication, which empowers cloud users to conduct privilegebased duplicate checks before data upload. This hierarchical structure allows cloud servers to profile users based on their privileges, enabling more nuanced control over data management. In this research, we introduce the SHA method for de-duplication within cloud servers, supplemented by a secure pre-processing assessment. The proposed method accommodates dynamic privilege modifications, providing flexibility and adaptability to evolving user needs and access levels. Extensive theoretical analysis and simulated investigations validate the efficacy and security of the proposed system. By leveraging the SHA algorithm and incorporating robust pre-processing techniques, our approach not only enhances efficiency in data deduplication but also addresses crucial privacy concerns inherent in cloud storage environments. This research contributes to advancing the understanding and implementation of efficient and secure data management practices within cloud infrastructures, with implications for a wide range of applications and industries. 2024; Los autores. -
Big Data for Intelligence and Security
The name Big Data for Security and Intelligence is a method of analysis that focuses on huge data (ranging from petabytes to zettabytes) that includes all sources (such as log files, IP addresses, and emails). Various companies use big data technology for security and intelligence in order to identify suspicious tasks, threats, and security tasks. They are able to use this information to combat cyber-attacks. One of the limitations of big data security is the inability to cover both current and past data in order to be able to uncover identified threats, anomalies, and fraud to keep the n/wsafe from attacks. A number of organizations are addressing rising problems like APTs, attacks, and fraud by focusing on them. More is better than less! The easier it will be to determine. Nevertheless, organizations which utilize big data techniques make sure that privacy and security issues have been resolved before putting their data to use. Because there are so many different types of data stored in so many different systems, the infrastructure needed to analyze big data should be able to handle and support more advanced analytics like statistics and data mining. The one side of the coin is the collection and storing of lots of information; the other side is protecting massive amounts of information from uncertified access, which is very difficult. Big data is commonly used extensively in the improvement of security and the facilitation of law enforcement. Big data analytics are used by the US National Security Agency (NSA) to foil terrorist plots, while other agencies use big data to identify and handle cyber-attacks. Credit card companies use big data analytics tools to detect fraud transactions, while police departments use big data methods to track down criminals and forecast illegal activity. Big data is being used in amazing ways in todays information world, but security and privacy are the primary concerns when it comes to protecting massive amounts of data. Real-time data collection, standardization, and analysis used to analyze and enhance a companys overall security is referred to as Security Intelligence. The security intelligence nature entails the formation of software assets and personnel with the goal of uncovering actionable and useful insights that help the organization mitigate threats and reduce risks. To identify security incidents and the behaviors of attackers, todays analysts use machine learning and big data analysis. They also use this cutting-edge technology to automate identification and security events analysis and to extract security intelligence from event logs generated on a network. This chapter will discuss how Big Data analytics can help out in the world of security intelligence, what the appropriate infrastructure needs to be in order to make it useful, how it is more efficient than more traditional approaches, and what it would look like if we built an analytic engine specifically for security intelligence. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Big data management and smart drug delivery system based on Internet of Bio-Nano Things
Integrating big data analytics and Internet of Bio-Nano Things (IoBNT) presents disruptive prospects in healthcare, especially in advancing intelligent medication delivery systems. This chapter examines the complex dynamics of big data management, emphasizing the collection, processing, and analysis of extensive biomedical data produced by IoBNT-enabled devices. IoBNT, an innovative network of bio-nanosensors and actuators, enables accurate and real-time monitoring of physiological states, hence advancing personalized and targeted drug delivery systems. Key components like data integration, predictive analytics, machine learning algorithms, and the way contemporary communication protocols enable smooth data flow between bio-nanodevices and healthcare infrastructure. The chapter also explores the challenges associated with data security, privacy, next scalability within the frameworks of IoBNT designs. Using big data analytics, IoBNT-based innovative drug delivery systems can improve therapeutic outcomes by optimizing dosage, timing, and administration paths. This chapter underscores the potential of the Internet of Things and big data to transform healthcare in the future through a more agile, responsive, and efficient method of disease management. The discussion is supported by analyzing emerging trends and concrete case studies, illustrating how these advancements can lead to significant improvements. 2026 Elsevier Inc. All rights reserved.. -
Big Data Paradigm in Cybercrime Investigation
Big Data is a field that provides a wide range of ways for analyzing and retrieving data as well as hidden patterns of complex and large data collections. As cybercrime and the danger of data theft increase, there is a greater demand for a more robust algorithm for cyber security. Big Data concepts and monitoring are extremely useful in discovering patterns of illegal activity on the internet and informing the appropriate authorities. This chapter investigates privacy and security in the context of Big Data, proposing a paradigm for Big Data privacy and security. It also investigates a classification of Big Data-driven privacy and security of each algorithm. In this section, we first define Big Data in the contexts of police, criminology, and criminal psychology. The chapter will look at how it might be used to analyze concerns that these paradigms confront carefully. We provide a conceptual approach for assisting criminal investigations, as well as a variety of application situations in which Big Data may bring fresh insights into detecting facts regarding illegal incidents. Finally, this chapter will explore the implications, limits, and effects of Big Data monitoring in cybercrime investigations. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.