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In the realm of technological advancement, the fusion of blockchain, artificial intelligence (AI), and the metaverse emerges as a transformative force. This research explores their intricate interplay, reshaping digital innovation. Blockchain`s decentralization aligns with AI`s intelligence, while the metaverse offers a platform for human interaction. This synergy amplifies the metaverse`s potential, impacting gaming, finance, and education. In gaming, blockchain ensures asset ownership, while AI enhances gameplay. In finance, blockchain redefines trust, and AI offers predictive insights. In education, blockchain secures credentials, and AI personalizes learning within an immersive metaverse. Challenges include interoperability between blockchain and AI networks, data privacy in the data-intensive metaverse, and ethical considerations regarding identity, property rights, and human-AI interactions. A collective commitment from industry pioneers, policymakers, and technology visionaries is crucial to unlock this fusion`s potential. In conclusion, this fusion invites a redefined human experience. Navigating with ethical considerations, innovation, and responsible stewardship, we step into an uncharted territory that transcends imagination, uniting reality and the virtual in the future`s canvas.

Metaverse, blockchain, Artificial intelligence, Neural Network, Virtual Reality, Digital Transformation, Realm

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[7] M. Xu, D. Niyato, J. Kang, Z. Xiong, C. Miao, and D. I. Kim, "Wireless edge-empowered metaverse: A learning-based incentive mechanism for virtual reality," arXiv preprint arXiv:2111.03776, 2021.
[8] W. C. Ng, W. Y. B. Lim, J. S. Ng, Z. Xiong, D. Niyato, and C. Miao, "Unified resource allocation framework for the edge intelligence-enabled metaverse," arXiv preprint arXiv:2110.14325, 2021.
[9] H. Du, D. Niyato, J. Kang, D. I. Kim, and C. Miao, "Optimal targeted advertising strategy for secure wireless edge metaverse," arXiv preprint arXiv:2111.00511, 2021.

Kushal Saraf, "Fusing Blockchain and AI with the Metaverse: Unveiling the Future of Digital Transformation," International Journal of Computer Sciences and Engineering, Vol.11, Issue.9, pp.1-10, 2023.

In the realm of resource management, the practice of labeling, ledgering, and tagging has a rich historical legacy that has transcended time, demonstrating its enduring importance. These processes, which have been fundamental since ancient times, continue to wield immense significance in the contemporary context, particularly when applied to intangible assets, which are instrumental in organizational success. However, in the contemporary landscape characterized by digitalization and the proliferation of non-tangible assets, the task of effectively labeling and tagging resources has grown markedly intricate. This complexity is especially conspicuous when considering intangible resources, and it is accentuated within the domain of software infrastructure and application modules. In these domains, the sheer volume of resources in use has burgeoned to unprecedented levels, rendering manual tagging processes not only labor-intensive but also prone to errors and inconsistencies. Moreover, the scope of resource tagging has evolved beyond the rudimentary labeling of resource names, now encompassing a multitude of metadata attributes that impart comprehensive context and information. To tackle these formidable challenges, this paper presents a robust, enterprise-grade solution engineered to automate the resource tagging processes within the Amazon Web Services (AWS) ecosystem. At its core, this solution leverages the capabilities offered by Amazon`s CloudTrail services, harnessing them to mitigate the manual burden associated with resource tagging activities. The automated tagging paradigm, put forth in this work, holds significant promise for enhancing various facets of resource management within AWS. The primary objectives of this solution are multifaceted. Firstly, it endeavors to elevate the precision of resource identification, a crucial aspect for effective resource governance. Through automated tagging, resources are associated with specific teams or entities, enabling efficient identification and allocation of ownership. This, in turn, fosters a more streamlined approach to resource management within complex AWS environments. Secondly, the proposed solution enables granular cost analysis by forging a nexus between resource tags and cost metrics. This synergy between tags and cost data empowers organizations to pinpoint cost drivers and optimize resource utilization. It facilitates a nuanced understanding of the financial implications associated with various resources, fostering data-driven decision-making and cost control. Lastly, the solution paves the way for comprehensive insights into the resource landscape of specific teams or entities. By aggregating tagged resources, organizations can gain a holistic view of their resource inventory. This panoramic perspective facilitates efficient resource allocation, aids in identifying redundancies, and supports the development of resource optimization strategies tailored to the needs and objectives of distinct teams.

Automated Tagging, CloudTrail, Resource Management, Amazon Web Services, Resource Identification, Cost Attribution, Resource Inventory.

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Sai Teja Makani, "Efficient Resource Utilization with Auto Tagging Using Amazon`s Cloud Trail Services," International Journal of Computer Sciences and Engineering, Vol.11, Issue.9, pp.11-16, 2023.

Positive, negative, and neutral tweets concerning COVID-19 have all lately increased in volume. The broad variety of themes covered by tweets encouraged investigators to use sentiment analysis to assess the public`s response to COVID-19. Conventional sentiment analysis algorithms can only assess polarity, categorizing tweets as positive, negative, or neutral. Logistic Regression sentiment analysis, BLSTM sentiment analysis, and LSTM sentiment analysis are all employed to identify the sentiment of tweets at this advanced phase of the intended research effort. While the offered research methodologies may be used across domains, they are particularly well-suited to detecting emotional expressions in social media situations. With the exception of the sentiment analysis approach, the pretreatment and subsequent operations will be the same despite the employment of three separate algorithms. Using the identical processing processes, the three recommended sentiment analysis algorithms will be compared. Furthermore, the proposed analysis has a broad range of practical applications since it gives a public opinion to government officials or even health officials and assists them in basing their judgments on that viewpoint.

Positive, Negative, Covid-19, Tweets, Lockdown, LSTM, BLSTM.

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Anjali Yadav, Chetan Agrawal, Pawan Meena, "A Comprehensive Review of Sentimental Analysis of Covid-19 Tweets," International Journal of Computer Sciences and Engineering, Vol.11, Issue.9, pp.17-21, 2023.

Privacy preservation practises intensify as data privacy infractions are an increasing source of worry. A lot of organisations gather a lot of data. Organisations occasionally utilise this data for a variety of activities. However, the data gathered may include sensitive or private information that has to be secured. If we disclose data for sharing purposes, privacy protection is a crucial problem. There are several methods for protecting privacy, but they are vulnerable to different kinds of assaults and data loss. In this research, we suggested a practical method for maintaining privacy via homomorphic encryption. Our method safeguards sensitive data with little information loss, improving data usefulness and guarding against many sorts of assault.

Privacy preservation, linking sensitive data, Homomorphic Encryption, Blockchain

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Ashwini Patil, Vijay Shelake, "Decentralized based Security Mechanism for Linking Sensitive Data using Blockchain Technology," International Journal of Computer Sciences and Engineering, Vol.11, Issue.9, pp.22-27, 2023.

Assessment of learning outcomes among students at institutions of higher education is a fascinating issue with a wide range of possible applications for all parties including students, administrators, potential employers, etc. Even while education is becoming more widely available and popular, the high drop-out rates remain a challenging issue. Choosing the best courses for your area of specialization can be difficult and time-consuming. The majority of the current course selection algorithms do not consider courses that match student talents, the user`s future professional goals, or the user`s ideal job based on such objectives. The goal is to create a powerful learning analytics system that can effectively recommend courses to students based on their preferences and skills. The collaborative filtering recommender (CF) approach, which combines KNN and decision tree approaches, was used to match courses, abilities, requirements, and interests with recommended lists. The effect of user skills on the recommendation platform was investigated by altering a number of suggestion quality features. A collaborative filtering recommender system was developed by fusing KNN and DT to suggest specialized courses for college students. This improved the standard of the recommendation system. A recommender model was constructed with cosine similarity matrix of student course descriptions in order to include new descriptions that are being suggested to the overall descriptions. Term frequency inverse document frequency was used to convert the entire course description into a vectored representation of words.

Collaborator filtering, decision tree, KNN, machine learning, recommender system

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