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  • 1
    Online Resource
    Online Resource
    Emerald ; 2022
    In:  International Journal of Pervasive Computing and Communications ( 2022-08-22)
    In: International Journal of Pervasive Computing and Communications, Emerald, ( 2022-08-22)
    Abstract: Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis. Design/methodology/approach Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers. Findings Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails. Originality/value It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.
    Type of Medium: Online Resource
    ISSN: 1742-7371 , 1742-7371
    Language: English
    Publisher: Emerald
    Publication Date: 2022
    detail.hit.zdb_id: 2423858-2
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  • 2
    In: AIDS Research and Human Retroviruses, Mary Ann Liebert Inc, Vol. 30, No. S1 ( 2014-10), p. A261-A261
    Type of Medium: Online Resource
    ISSN: 0889-2229 , 1931-8405
    RVK:
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2014
    detail.hit.zdb_id: 1475767-9
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  • 3
    Online Resource
    Online Resource
    Inderscience Publishers ; 2022
    In:  International Journal of Intelligent Enterprise Vol. 9, No. 3 ( 2022), p. 344-
    In: International Journal of Intelligent Enterprise, Inderscience Publishers, Vol. 9, No. 3 ( 2022), p. 344-
    Type of Medium: Online Resource
    ISSN: 1745-3232 , 1745-3240
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2022
    SSG: 3,2
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  • 4
    Online Resource
    Online Resource
    Inderscience Publishers ; 2022
    In:  International Journal of Intelligent Enterprise Vol. 9, No. 3 ( 2022), p. 344-
    In: International Journal of Intelligent Enterprise, Inderscience Publishers, Vol. 9, No. 3 ( 2022), p. 344-
    Type of Medium: Online Resource
    ISSN: 1745-3232 , 1745-3240
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2022
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    Inderscience Publishers ; 2022
    In:  International Journal of Engineering Systems Modelling and Simulation Vol. 1, No. 1 ( 2022), p. 1-
    In: International Journal of Engineering Systems Modelling and Simulation, Inderscience Publishers, Vol. 1, No. 1 ( 2022), p. 1-
    Type of Medium: Online Resource
    ISSN: 1755-9758 , 1755-9766
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Emerald ; 2023
    In:  World Journal of Engineering Vol. 20, No. 5 ( 2023-08-21), p. 801-807
    In: World Journal of Engineering, Emerald, Vol. 20, No. 5 ( 2023-08-21), p. 801-807
    Abstract: The purpose of this paper used for catastrophe and pandemic preparedness was the craft of machine learning calculations. ML is the latest globe learning technique to assist in the identification and remediation of medical care catastrophes. Design/methodology/approach To the greatest extent possible, countries are terrified about debacles and pandemics, which, all in all, are exceptionally improbable occurrences. When health emergencies arise on the board, several issues arise for the medical team because of the lack of accurate information from numerous diverse sources, which is required to be available by suitable professionals. Findings Thus, the current investigation’s main objective is to demonstrate a structure that is dependent on the incorporation of recent advances, the Internet of Things and large information and which can settle this issue by using machine learning (ML) in all stages of catastrophe and providing accurate and compelling medical care. Originality/value The system upholds medical services characters by empowering information to be divided between them, enabling them to perform insightful estimations and enabling them to find significant, legitimate and precise patterns that are required for functional arrangement and better readiness in the event of crises. It is possible that the results of the system’s work may be used by the executives to assist chiefs in differentiating and forecasting the wellbeing repercussions of the fumbles.
    Type of Medium: Online Resource
    ISSN: 1708-5284 , 1708-5284
    Language: English
    Publisher: Emerald
    Publication Date: 2023
    detail.hit.zdb_id: 2854753-6
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  • 7
    Online Resource
    Online Resource
    Elsevier BV ; 2022
    In:  Theoretical Computer Science Vol. 925 ( 2022-08), p. 61-67
    In: Theoretical Computer Science, Elsevier BV, Vol. 925 ( 2022-08), p. 61-67
    Type of Medium: Online Resource
    ISSN: 0304-3975
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 193706-6
    detail.hit.zdb_id: 1466347-8
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  • 8
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-6-25), p. 1-11
    Abstract: People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 9
    In: World Journal of Engineering, Emerald, Vol. 19, No. 2 ( 2022-03-15), p. 238-243
    Abstract: “Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic. Findings According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way. Originality/value The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).
    Type of Medium: Online Resource
    ISSN: 1708-5284 , 1708-5284
    Language: English
    Publisher: Emerald
    Publication Date: 2022
    detail.hit.zdb_id: 2854753-6
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  • 10
    In: Global Business Review, SAGE Publications
    Abstract: Blockchain, which is at the heart of bitcoin and other digital currencies, is a game-changing technology, particularly in the stock market and financial technology. These characteristics make blockchain adoption appealing for enhancing information security, privacy and reliability in a variety of scenarios. This article develops the trust with reputation-based management for the financial stock market. Here distributed ledger technology (DLT) has been integrated for implementing the ledger in which data is stored across a network of decentralized nodes. By this, proof-based ledger has been maintained with transparency, traceability and security. DLT here used is based on trust design in mitigating the malicious activities of the financial stock marketing technology. Then using Bayesian theory based on the fault tolerance (BDLT_FT) model has been used in improving the efficiency of DLT and financial technology. This acts as the protocol which reduces energy consumption and enhance network scalability and security in digital transactions like cryptocurrency The financial data related to blockchain cryptocurrency were collected between the time period 2000 and 2020. The dataset of the stock market is evaluated under the fault tolerance model with statistical analysis. The statistical evaluation of financial data with developed BDLT_FT expressed that there is no association between the fault tolerance and the classification error.
    Type of Medium: Online Resource
    ISSN: 0972-1509 , 0973-0664
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2211884-6
    SSG: 3,2
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