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                <text>Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis</text>
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                <text>Deep learning; Inception-V3; Malware; ResNet-50; Transfer learning; VGG-16</text>
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                <text>The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Jeyalakshmi V.S.; Krishnan N.; Jayapriya J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 39-51.</text>
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                <text>ISSN: 23673370; ISBN: 978-981993009-8</text>
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                <text>Jeyalakshmi V.S., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Krishnan N., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Jayapriya J., Department of Computer Science (YPR Campus), Christ University, Bangalore, India</text>
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                <text>Distributed Maximum Power Point Tracking for Mismatched Modules of Photovoltaic Array</text>
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                <text>Artificial neural network; Compensators; Distributed maximum power point tracking; Partial shading; Photovoltaic; PV module</text>
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                <text>The multiple peaks in the output P-V characteristics of the photovoltaic (PV) module and the complete loss of shaded modules generation due to the existing bypass diode-based scheme are eliminated through the implementation of proven distributed maximum power point tracking (DMPPT). Considering the unique behavior of each PV Module, the artificial neural network is used in the DMPPT algorithm to track the MPP at every instant by learning the unique behavior of each PV module in this chapter. This eliminates the effect of manufacturing dispersion. Though the unique MPP is identified, the inability of the DMPPT algorithm in maintaining the PV modules in its own MPP is eliminated by the compensator circuits which are introduced in the array configuration along with the DMPPT in this chapter. These compensators enabled the maintenance of each PV module in its own MPP by providing the deficient current of each module and the deficient voltage of each string. So, this configuration increases the output power by including the generation of shaded modules instead of bypassing it. The results show that the proposed configuration avoids the multiple peak condition in P-V characteristics and improves the efficiency of the PV array under partially shaded conditions.  2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Jeyaprabha S.B.</text>
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                <text>Green Energy and Technology, pp. 231-251.</text>
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                <text>ISSN: 18653529</text>
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                <text>Jeyaprabha S.B., Department of Electrical and Electronics Engineering, CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Low cost ANN based MPPT for the mismatched PV modules</text>
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                <text>Due to manufacturing dispersal, the photovoltaic (PV) panels of similar rating and manufacturer have distinctive characteristics in practical. As the maximum power point tracking (MPPT) becomes essential to optimally utilize the solar PV panel, distributed maximum power point tracking (DMPPT) is considered in this paper to follow the MPP of each panel. As the common MPP value is used in the existing DMPPT method to control all the panels, it fails to consider the uniqueness of each panel. By considering the uniqueness of each panel, the ANN based MPPT is implemented in this paper. As the ANN is trained using the actual characteristics of each panel based on the operating current, voltage and temperature, it is able to track the actual MPP. Due to the solar irradiance free MPPT, the costly pyranometer is not required in the actual PV system for MPPT. It reduces the cost of the system and also provides the interruption free tracking due to its independent nature on Voc and Isc values. Also, because of the looping free behaviour of the proposed algorithm, it is capable of following the MPP at rapidly varying condition. The proposed technique and the verified outcomes are discussed here in detail.  Published under licence by IOP Publishing Ltd.</text>
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                <text>Jeyaprabha S.B.</text>
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                <text>Journal of Physics: Conference Series, Vol-1706, No. 1</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 17426588</text>
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                <text>Jeyaprabha S.B., Department of Electrical and Electronics Engineering, CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Jeyaseelan, M.; Regis, Susai; James Robert, S.</text>
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                <text>Relationship between Parenting Styles and Identity Formation among Emerging Adulthood</text>
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                <text>Jeyaseelan M., Department of Sociology, Periyar University, Salem, India; Regis S., Department of Sociology, Periyar University, Salem, India; James Robert S., Department of Psychology, Christ University, Bengaluru, India</text>
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                <text>Does Cultural Intelligence &amp;amp; Emotional Intelligence Differ by Region in India? A Comparative Study; [??????????? ?? ?????????? ? ????????????? ?????????????? ? ???????? ?????? ????????????? ????????????]</text>
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                <text>Students from various parts of India periodically migrate to universities for academic and professional reasons. They reflect various cultural diversities and have to overcome obstacles like intergroup prejudice and acculturative stress. Although these factors can differ by region, the response tendency may be influenced by their respective cultural intelligence/quotient (CQ) and emotional intelligence/quotient (EQ). The comparisons of CQ and EQ across students from significant regions of India, however, have never been recorded in a prior study. This study attempted to examine the level of CQ and EQ among students who were enrolled in educational institutions in Kalaburagi City, who belong to three major regions of India (North, South-East, and South-West). A total of 385 students between the ages of 18 and 31 (mean age: 20.56; standard deviation: 2.633) were recruited for the study. Surprisingly, there were significant geographical disparities in the use of emotions and metacognitive CQ. Compared to students from the other two regions, students from the south-eastern area performed better while using emotional EQ and scored less while using meta-cognitive CQ. The study deduced the plausible factors and potential explanations for CQ  EQ disparities and inter-regional acceptability among students from three major regions, which may be used to develop a CQ &amp;amp; EQ training program for usage across Indias educational system  2023, Cultural-Historical Psychology.All Rights Reserved.</text>
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                <text>Jeyavel S.; Subhasree G.; Pandey V.; Rajkumar E.; Eapen J.C.; Lakshmana G.</text>
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                <text>Cultural-Historical Psychology, Vol-19, No. 1, pp. 97-105.</text>
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                <text>Jeyavel S., Central University of Punjab, India; Subhasree G., Gandhi Institute of Technology and Management, India; Pandey V., Central University of Karnataka, India; Rajkumar E., Central University of Karnataka, India; Eapen J.C., Christ University, India; Lakshmana G., Central University of Karnataka, India</text>
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                <text>The advent of GenAI has brought about substantial progress and prospects in diverse sectors, including education. We are witnessing significant progress in this field of artificial intelligence, with the emergence of chatbots such as ChatGPT and the proliferation of remarkably realistic AI-generated graphics. Generative AI, as an emerging technology, has the potential to bring significant and transformative improvements to education. Generative AI encourages higher education institutions to embrace and utilize the potential of these technologies to enhance several aspects such as student experience, faculty workload, intellectual property, etc. This chapter has explored the application of generative AI in the context of higher education, in light of its increasing prevalence. Although generative artificial intelligence offers a great deal of promise to improve education, the technology is not entirely devoid of difficulties. The chapter also discusses challenges and strategies related to generative AI in higher education.  2024 by IGI Global.</text>
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                <text>The Industrial Revolution From Mechanisation (1.0) to Smart Automation (5.0)</text>
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                <text>The Industrial Revolution was a transformative event of unprecedented magnitude, profoundly altering many facets of society, including consumption patterns, social structures, cultural norms, and global arrangements. Contrary to popular belief, there is not a one Industrial Revolution, but rather a sequence of stages characterised by gradual advancements. We are currently on the verge of the Fifth Industrial Revolution, which is expected to have a greater impact than the cumulative effect of the preceding four revolutions. Industry 5.0 is the latest term gaining popularity in the business sector, with significant claims to fundamentally transform our lifestyle. Industry 5.0 is expected to drive more progress in the field of intelligent automation within the manufacturing sector. This chapter specifically examines the transition of industry sector from version 1.0 to version 5.0.  2025 selection and editorial matter, Payal Bansal, Rajeev Kumar, Ashwani Kumar and Daniel D. Dasig, Jr.; individual chapters, the contributors.</text>
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                <text>Jha B., Christ (Deemed to be University) Delhi NCR, India; Garg S., Christ (Deemed to be University) Delhi NCR, India; Dhingra S., Christ (Deemed to be University) Delhi NCR, India</text>
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                <text>The metaverse is generating widespread interest among organizations of various scales, as companies acknowledge its capacity for profound transformation. Prominent organizations are actively adopting the metaverse concept to improve their operations and interact with clients in innovative and captivating manners. This chapter examines the vital function that blockchain plays in bolstering user data security as it explores the urgent problem of data breaches in the metaverse sector. The authors also carefully examine recent data breach cases in the metaverse sector, such as sandbox incursions, deepfake attacks, and digital avatar assaults. By outlining these practical difficulties, they make clear how urgent it is to come up with reliable ways to safeguard user data in the metaverse. The study findings have emphasized the dynamic and evolving nature of the metaverse industry, which is now undergoing substantial development and continuous exploration. They have also focused on significant blockchain technologies that demonstrate potential in ensuring data security.  2024, IGI Global. All rights reserved.</text>
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                <text>Meeting ESG (environmental, social, and governance) standards is becoming an essential goal for businesses, one that is supported by both investors and customers. FinTech can help companies evaluate and reduce their environmental effect, as well as investors channel their operations toward more sustainable assets. Embracing sustainability has become one of the priorities for many fintech companies today. Green fintech innovations have provided technologically enabled solutions for financial services that help in increasing the flow of financial resources for sustainable development. Recognizing the importance of green fintech in today's scenario, this chapter will initially focus on the components of the fintech ecosystem and IoT and AI-enabled fintech innovations in India, drivers of green fintech potentials of green fintech IoT, and AI-enabled green fintech from global perspective and has highlighted the green fintech solutions provided by the topmost companies.  2022, IGI Global. All rights reserved.</text>
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                <text>Web3 is a ground-breaking invention that has the ability to address the shortcomings of web1 and web2. The industry witnessing its major impact is the finance sector. A wave of innovation in traditional finance has been inspired by the introduction of Web3. It is also referred to as the decentralised web and is a developing movement that is upending conventional finance by providing a more open, safe, and decentralised substitute. Traditional banking should work to adopt the features that Web3 offers, including stability, scalability, interoperability, security, performance, extensibility, management, and openness. In order for TradFi to maintain its relevance and expertise in the face of the widespread adoption of digital financial modes, it is now necessary to embrace several Web3 capabilities. Keeping into consideration the relevance and importance of Web3 in finance, this chapter will basically focus on analysing the key features and characteristics of Web3 in comparison to traditional finance.  2023 by IGI Global. All rights reserved.</text>
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                <text>Cloud computing has become an essential source for modern trade or market environments by abled frameworks. The exponential growth of cloud computing services in the last few years has resulted in extensive use, especially in storing and sharing the data on various cloud servers. The current trend in the cloud shows that the cloud owners use relative functions and target areas in such a way that cloud customers access or store their data either in the same servers or related servers. Simultaneously, from the security point of view, the lack of confidence about the customer's data on the cloud server is still questionable. The hour's need is to provide the cloud service in a single port way by forming the joint management policy to increase customer satisfaction and profitability. In addition to this, the authentication steps also need to be improvised. This paper discusses issues on the security authentication and access provisioning of the cloud service consumers in federated clouds using subscribed user identity. This work proposes the user identity verification module (UidVM) in the cloud service consumer's authentication process to serve as a cloud broker to minimize the work overloads on the central cloud federation management system, thus enhancing the cloud security.  2021. All Rights Reserved.</text>
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                <text>Jha S.; Ahmad S.; Alharbi M.; Alouffi B.; Sebastian S.</text>
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                <text>International Journal of Advanced Computer Science and Applications, Vol-12, No. 11, pp. 178-187.</text>
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                <text>Jha S., School of Sciences, Christ (Deemed to be University), NCR, New Delhi, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alharbi M., Department of Computer Science, College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alouffi B., Department of Computer Science, College of Computers and Information Technology Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Sebastian S., Department of Computer Science, Christ (Deemed to be University), Bangalore, 29, India</text>
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                <text>agriculture; blockchain; chatbot; Proof-of-Work; smart contracts</text>
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                <text>This study explores the important role of blockchain technology in the transformation of agriculture and presents a new way to integrate chatbots and smart contracts to solve the problem of persistence. Leverage the decentralized structure and security of the blockchain to increase traceability, transparency and fairness in agricultural product prices. A user-friendly chatbot built in Python using Tkinter that acts as a bridge between farmers and the Ethereum-based blockchain pricing algorithm. Smart contracts used in Solidity dynamically adjust crop prices based on the weather in real time, making it possible for prices to react and adjust. Simulations and tests in Ganache validate the proposed method, confirming its economic value and effectiveness in many agricultural cultures. This study delves into analytics, including latency and production time, to demonstrate the benefits of the blockchain model in creating transparent, farmer-centric and region-specific crop prices. The importance of this research is to support continuous change in agricultural technology, paving the way for the introduction of appropriate and fair prices. According to the amendment, the integration of advanced machine learning, further integration and collaboration with agricultural stakeholders should be developed in the future. This work sets a good path for agriculture, promoting transparency, fairness and quick access to the best crop prices, thus ensuring security and agricultural technology.   2024 IEEE.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Jha S., Christ (Deemed to be University), India; Alapatt B., Christ (Deemed to be University), India; George J., Christ (Deemed to be University), India</text>
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                <text>Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety</text>
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                <text>Autonomous vehicles; Batch rekeying; Blockchain; Ethereum; Hashgraph; Logical key hierarchy</text>
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                <text>Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.  2022 by the authors. Licensee MDPI, Basel, Switzerland.</text>
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              <elementText elementTextId="108357">
                <text>Jha S.; Jha N.; Prashar D.; Ahmad S.; Alouffi B.; Alharbi A.</text>
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                <text>Sensors, Vol-22, No. 7</text>
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                <text>MDPI</text>
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                <text>&lt;a href="https://doi.org/10.3390/s22072529" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.3390/s22072529&lt;/a&gt;
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              <elementText elementTextId="108362">
                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 14248220; PubMed ID: 35408144</text>
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                <text>Jha S., School of Sciences, Christ (Deemed to be University), NCR-New Delhi Campus, Ghaziabad, 201003, India; Jha N., Department of Networking and Security, School of Computer Science and Engineering (SCSE), Lovely Professional University, Phagwara, 144411, India; Prashar D., Department of Networking and Security, School of Computer Science and Engineering (SCSE), Lovely Professional University, Phagwara, 144411, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alouffi B., Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Alharbi A., Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia</text>
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                <text>An Introduction to Business Intelligence</text>
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                <text>The quality of managerial decisions impacts the performance of any business, and this decision mainly depends on the reliability, inclusiveness, correctness, and trustworthiness of the data used for this purpose. Nowadays, business intelligence (BI) has become a key buzzword. BI supports better business decision-making by transforming data into actionable insights. The digitalization or digitization of business is accommodating and embracing the new BI to endure and stand for consistency and competitiveness for business development toward technological or digital transformation. In this digital or computer era, only those businesses will be profitable and successful that are well furnished to digitally (or binary) shift their practices in the technological or information age. In the new technological age, high powered by data analytics capabilities, meaningful and systematic data assimilation has become a new challenge for an organization to transfer data into BI. BI is a technology-driven process for analyzing data into information; information into knowledge; and knowledge into plans that manage and regulate the organization. BI presents actionable information to help corporate executives; business managers, and other end-users and makes more informed business decisions. BI software systems provide historical, current, and predictive views of business operations. Dashboards; Forecasting; Graphical Reporting; Graphical Online Analytical Processing (OLAP); and Key Performance Indicators (KPIs) are the modules of BI. BI helps in organizing teams, keeping them mindful and aware of KPIs. The awareness of KPIs through dashboards and reports keeps teams aligned and more focused on their goals. The optimal aim of BI is to enable a business to make informed decisions. BI helps business managers or leaders utilize data in a way that is coherent and dynamic. The key elements of BI involved are Advanced Analytics or Corporate Performance Management; BI; Data Sources; Data Warehousing and OLAP. With the latest technology and innovations, there are countless BI applications available for varied types of data analysis. BI software or technologies can deal with multiple structured and unstructured data to identify, develop, and create new strategies business opportunities. Its purpose is to enable clear and accessible interpretation of the huge data, to identify new opportunities and execute effective strategies. Strategic BI (SBI) is always associated with reporting from an analytical data source or data warehouse. Essentially, SBI improves the business process by analyzing a predetermined set of data pertinent to that process and provides the historical background of that data. SBI assembles on four crucial and necessary criteria or frameworks, namely collection and storage of data; Optimization of data for analysis; Identification of important business drivers through past data records; and seeking answers to key business questions. Hence, BI provides procedures and technologies, and tools for current business leaders to alter and modify dynamically and effectively lead their companies with correct data decisions. This research paper is qualitative and based on secondary data. This chapter aims to provide insights into BI and highlights the recent innovations and future of BI.  2023 selection and editorial matter, Deepmala Singh, Anurag Singh, Amizan Omar &amp;amp; S.B Goyal.</text>
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              <elementText elementTextId="155917">
                <text>Jha S.K.; Jha B.</text>
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