A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dash, Bibhu
- The Digital Carbon Footprint: Threat to an Environmentally Sustainable Future
Authors
1 Dept. of Computer and Information Systems, University of the Cumberlands, KY, US
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 14, No 3 (2022), Pagination: 19-29Abstract
With digitalization at its peak, every online action we take has an environmental impact. There is a growing concern about the world's ever-increasing carbon emission due to technological advancement. The vast majority of human actions have been proved harmful to the environment. This effect has been mostly tied to available carbon emissions. On the other hand, recent findings have raised awareness of digital carbon emissions. These harmful emissions represent the available CO2 emissions rate resulting from generic digitization concepts. The advancement of technology has considerably contributed to CO2 emissions. This study paper discusses the total effects of carbon emissions. It also shows the rates of carbon emissions caused by the tech industry worldwide. The article describes how digital services have boosted carbon emissions and the number of regions affected by the higher rates. The study focuses on the relationship between carbon emissions and digitization, remedies to the problem, and an overall analysis of the global digital carbon footprint.Keywords
Digitalization, Digital carbon footprint, CO2 emission, Global warming, NARDL Approach, FD & CO2 emission nexus, Sustainability.References
- Patsavellas, J., & Salonitis, K. (2019). The Carbon Footprint of Manufacturing Digitalization: critical literature review and future research agenda. Procedia Cirp, 81, 1354-1359.
- Stolz, S., & Jungblut, S. (2019, December 30). Our Digital Carbon Footprint: What's the Environmental Impact of the Online World? RESET.ORG. Retrieved May 24, 2022, from https://en.reset.org/our-digital-carbon-footprint-environmental-impact-living-life-online-12272019/
- Akpan, P., & Fuls, W. (2021). Cycling of coal fired power plants: A generic CO2 emissions factor model for predicting CO2 emissions. Energy, 214, 119026. https://doi.org/10.1016/j.energy.2020.119026
- Osburg, T., & Lohrmann, C. (2017). Sustainability in a Digital World. Springer.
- Baloch, M. A., & Wang, B. (2019). Analyzing the role of governance in CO2 emissions mitigation: the BRICS experience. Structural Change and Economic Dynamics, 51, 119-125.
- Sari, E., & Sofwan, M. (2021). Carbon Dioxide (CO2) Emissions Due to Motor Vehicle Movements in Pekanbaru City, Indonesia. Journal Of Geoscience, Engineering, Environment, And Technology, 6(4), 234-242. https://doi.org/10.25299/jgeet.2021.6.4.7692
- Seriño, M., & Klasen, S. (2015). Estimation and Determinants of the Philippines' Household Carbon Footprint. The Developing Economies, 53(1), 44-62. https://doi.org/10.1111/deve.12065
- Vourdoubas, J. (2016). Reduction of CO2 Emissions Due to Energy Use in Crete-Greece. Energy And Environment Research, 6(1), 23. https://doi.org/10.5539/eer.v6n1p23
- Jungblut, Sarah-Indra. (2019). Our Digital Carbon Footprint: What's the Environmental Impact of the Online World? Reset Org. https://en.reset.org/our-digital-carbon-footprint-environmental-impact-living-life-online-12272019/
- Pontecorvo, E. (2021). Meet the startup producing oil to fight climate change. Grist. Retrieved 21 May 2022, from https://grist.org/climate-energy/lucky-charm/.
- Chen, L. (2021). How CO2 emissions respond to changes in government size and level of digitalization? Evidence from the BRICS countries. Environmental Science And Pollution Research, 29(1), 457-467. https://doi.org/10.1007/s11356-021-15693-6
- Rafael, D., & Olvera, C. (2022). The Polluting Cloud. A Socio-environmental analysis of the Digital Carbon Footprint. PAAKAT: Revista De Tecnología Y Sociedad, 12(22). https://doi.org/10.32870/pk.a12n22.730
- Hermansson, F., H, S., & J, M. (2021). Carbon fiber material has reduced carbon footprint. Reinforced Plastics, 65(4), 171. https://doi.org/10.1016/j.repl.2021.06.057
- World Favor. 2022. The growing carbon footprint of digitalization and how to control it. https://blog.worldfavor.com/the-growing-carbon-footprint-of-digitalization-and-how-to-control-it
- Gerlach, A. (2021). Reducing Emissions through Digitalization - Center for Climate and Energy Solutions. Center for Climate and Energy Solutions. Retrieved 21 May 2022, from https://www.c2es.org/press-release/reducing-emissions-through-digitalization/.
- Brooker, M. (2019). Digital technologies and environmental impact. The Ecologist. Retrieved 21 May 2022, from https://theecologist.org/2019/jul/16/digital-technologies-and-environmental-impact.
- Aksyutin, O. (2018). THE CARBON FOOTPRINT OF NATURAL GAS AND ITS ROLE IN THE CARBON FOOTPRINT OF ENERGY PRODUCTION. International Journal Of GEOMATE, 15(48). https://doi.org/10.21660/2018.48.59105
- Hermansson, A. L., Hassellöv, I. M., Moldanová, J., & Ytreberg, E. (2021). Comparing emissions of polyaromatic hydrocarbons and metals from marine fuels and scrubbers. Transportation Research Part D: Transport and Environment, 97, 102912.
- Li, S., Yu, Y., Jahanger, A., Usman, M., & Ning, Y. (2022). The Impact of Green Investment, Technological Innovation, and Globalization on CO2 Emissions: Evidence From MINT Countries. Retrieved 21 May 2022, from https://www.frontiersin.org/articles/10.3389/fenvs.2022.868704/full.
- Agarwal, A., Kabita Agarwal, & Gourav Misra. (2020). Is internet becoming a Major Contributor for Global warming – The Online Carbon Footprint!!. December 2020, 02(04), 217-220. https://doi.org/10.36548/jitdw.2020.4.005
- Kessel, L., Holland, K., & Squire, J. (2008). Off-gas treatment carbon footprint calculator: Form and function. Remediation Journal, 19(1), 39-51. https://doi.org/10.1002/rem.20190
- Dash, B., & Ansari, M. F. (2022). Self-service analytics for data-driven decision making during COVID-19 pandemic: An organization’s best defense. Academia Letters, 2.
- Adhikari, S. (2021). Carbon Sequestration and Carbon Footprint in Some Aquaculture Practices in West Bengal, India. International Journal of Zoology And Animal Biology, 4(2). https://doi.org/10.23880/izab-16000291
- Barthelmie, R., Morris, S., & Schechter, P. (2018). Carbon neutral Biggar: calculating the community carbon footprint and renewable energy options for footprint reduction. Sustainability Science, 3(2), 267-282. https://doi.org/10.1007/s11625-008-0059-8
- Wolf, R., Abramoff, M., Channa, R., Tava, C., Clarida, W., & Lehmann, H. (2022). Potential reduction in healthcare carbon footprint by autonomous artificial intelligence. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00605-w
- Fei, H., & Zhang, C. (2021). Global warming solutions: Carbon capture and storage. E3S Web of Conferences, 308, 01024. https://doi.org/10.1051/e3sconf/202130801024
- Veretekhina, S., Krapivka, S., & Kireeva, O. (2020). Digital University, Student'S Digital Footprint, Digital Education Currency in the System of Modern Higher Education. International Journal Of Psychosocial Rehabilitation, 24(03), 1878-1889. https://doi.org/10.37200/ijpr/v24i3/pr200936
- Gani, A. A. (2012). The Relationship between Good Governance and Carbon Dioxide Emissions: Evidence from Developing Economies. J.Econ.Dev. 37 (1), 77–93. doi:10.35866/caujed.2012.37.1.004
- Karabatak, S., & Alanoglu, M. (2021). FACULTY MEMBERS' DIGITAL FOOTPRINT EXPERIENCES AND DIGITAL FOOTPRINT AWARENESS. Atatürk Üniversitesi Kazım Karabekir Eğitim Fakültesi Dergisi. https://doi.org/10.33418/ataunikkefd.891924
- Polat Bulut, A., Bulut, A., & Demirel, Ş. (2022). Creating Turkey's Carbon Footprint and Digital Maps Originating from Road Transportation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4060912
- Wada, Y. (2019). Ecological Footprint, Carbon Footprint and Radioactive Footprint in the Context of Building a Low Carbon Society. Journal Of Life Cycle Assessment, Japan, 6(3), 201-208. https://doi.org/10.3370/lca.6.201
- Gaillac, R., & Marbach, S. (2021). The carbon footprint of meat and dairy proteins: A practical perspective to guide low carbon footprint dietary choices. Journal Of Cleaner Production, 321, 128766. https://doi.org/10.1016/j.jclepro.2021.128766
- Lukose, N. (2016). A Review on Green ICT Solutions for CO2 Emissions. International Journal of Science And Research (IJSR), 5(4), 382-386. https://doi.org/10.21275/v5i4.nov162563
- Shahnazi, R., & Dehghan Shabani, Z. (2021). The effects of renewable energy, spatial spillover of CO2 emissions and economic freedom on CO2 emissions in the EU. Renewable Energy, 169, 293-307. https://doi.org/10.1016/j.renene.2021.01.016
- Ahmad, M., Khan, Z., Ur Rahman, Z., & Khan, S. (2018). Does financial development asymmetrically affect CO2 emissions in China? An application of the nonlinear autoregressive distributed lag (NARDL) model. Carbon Management, 9(6), 631-644.
- Makosembu, J. (2020). Global Warming: Impacts on Society and Alternative Solutions Taken. Journal Siplieria Sciences, 1(1), 1-6. https://doi.org/10.48173/jss.v1i1.1
- Uddin, S. (2022). Causes, Effects, and Solutions to Global Warming. Academia Letters. https://doi.org/10.20935/al4829
- Qafisheh, N., Sarr, M., Hussain, U., & Awadh, S. (2017). Carbon Footprint of ADU Students: Reasons and Solutions. Environment And Pollution, 6(1), 27. https://doi.org/10.5539/ep.v6n1p27
- De Wrachien, D. (2017). Impacts of Global Warming on Irrigation and Drainage Development: Perspectives Challenges and Solutions. Scifed Journal Of Global Warming, 1(1). https://doi.org/10.23959/sfjgw-1000003
- Turek, T., Dziembek, D., & Hernes, M. (2021). The Use of IT Solutions Offered in the Public Cloud to Reduce the City's Carbon Footprint. Energies, 14(19), 6389. https://doi.org/10.3390/en14196389
- Matawal, D., & Maton, D. (2013). Climate Change and Global Warming: Signs, Impact and Solutions. International Journal Of Environmental Science And Development, 62-66. https://doi.org/10.7763/ijesd.2013.v4.305
- Teufel, B., & Sprus, C. M. (2020, October 29). How digitalization acts as a driver of decarbonization. EY. Retrieved May 26, 2022, from https://www.ey.com/en_ch/decarbonization/how-digitization-acts-as-a-driver-of-decarbonization
- Säynäjoki, A., Heinonen, J., & Junnila, S. (2019). Carbon Footprint Assessment of a Residential Development Project. International Journal of Environmental Science and Development, 116-123. https://doi.org/10.7763/ijesd.2019.v2.107
- Aditya, N. (2017). Globalization and its impact on environment. Scifed Journal of Global Warming, 1(3). https://doi.org/10.23959/sfjgw-1000014
- Remote Work And Innovation During This COVID-19 Pandemic: An Employers’ Challenge
Authors
1 University of the Cumberlands, Williamsburg, KY, US
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 14, No 2 (2022), Pagination: 13-18Abstract
The ongoing COVID-19 pandemic is rapidly transforming the remote work culture of many organizations. The demand for online work-from-home has significantly increased during this pandemic. Though it has a significant advantage in eliminating travel time and positively impacting the environment and productive family time, some organizations raise the alarm over the new work style patterns. We discuss some organizational survey results and perspective employees permanently working from home. Some responses have a solid negative relationship on how the work from home affects their organization's innovative culture and work habits. But at the same time, some have expressed positive views and show a strong promise of a cultural shift with sustainable growth. Furthermore, we analyse the present and future pandemic era and how the C-level executives at each organization take the challenge forward to get maximum returns in this competitive global marketplace.
Keywords
Innovation, COVID-19, work culture, digitalization, sustainability, C-Suite reset, WFH, employee safety.References
- Al-Habaibeh, A., Watkins, M., Waried, K., & Javareshk, M. B. (2021). Challenges and opportunities of remotely working from home during the Covid-19 pandemic. Global Transitions, 3, 99-108.
- Benveniste, A. (2020). These companies' workers may never go back to the office. NewsBreak. Retrieved February 2, 2022, from https://www.newsbreak.com/news/2084679159083/these-companies-workers-may-never-go-back-to-the-office
- Billa, G. (2021). Remote work and innovation. Deloitte United States. Retrieved February 2, 2022, from https://www2.deloitte.com/us/en/blog/human-capital-blog/2021/remote-work-innovation.html
- Brynjolfsson, E., Horton, J. J., Ozimek, A., Rock, D., Sharma, G., &TuYe, H. Y. (2020). COVID-19 and remote work: An early look at US data (No. w27344). National Bureau of Economic Research.
- Caglar, D., Faccio, E., Couto, V., & Sethi, B. (2021). It's time to reimagine where and how work will get done. Life & Health Advisor. Retrieved February 3, 2022, from https://www.lifehealth.com/time-reimagine-work-will-get-done/
- Carroll, N., & Conboy, K. (2020). Normalising the “new normal”: Changing tech-driven work practices under pandemic time pressure. International Journal of Information Management, 55, 102186.
- Craft, A. R. (2020). Remote work in library technical services: Connecting historical perspectives to realities of the developing COVID-19 pandemic. Serials Review, 46(3), 227-231.
- DeFilippis, E., Impink, S. M., Singell, M., Polzer, J. T., & Sadun, R. (2020). Collaborating during coronavirus: The impact of COVID-19 on the nature of work (No. w27612). National Bureau of Economic Research.
- McLean, R. (2020). These companies plan to make working from home the new normal. As in forever. CNN Business, available at https://www. cnn. com/2020/05/22/tech/work-fromhome-companies/index. Html
- Parker, K., Horowitz, J. M., & Minkin, R. (2020). How the coronavirus outbreak has—and hasn’t—changed the way Americans work. Pew Research Center.
- Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist, 55(1), 68.
- Saad, L., & Jones, J. M. (2022). Seven in 10 US white-collar workers still working remotely. Gallup.com. Retrieved February 5, 2022, from https://news.gallup.com/poll/348743/seven-u.s.-white-collar-workers-still-working-remotely.aspx
- Wong, M. (2020). A snapshot of a new working-from-home economy. Stanford News. Retrieved February 3, 2022, from https://news.stanford.edu/2020/06/29/snapshot-new-working-home-economy/
- Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., ... & Teevan, J. (2021). The effects of remote work on collaboration among information workers. Nature human behaviour, 1-12.
- Big Data and Metaverse Revolutionizing the Futuristic Fintech Industry
Authors
1 School of Computer and Information Science, University of the Cumberlands, KY, US
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 15, No 1 (2023), Pagination: 1-13Abstract
The Fintech industry is no stranger to transformation and disruption. With the emergence of new technologies such as Big Data and the Metaverse, finance companies are quickly leveraging these technologies to revolutionize the virtual fintech sector. Big data transforms how financial institutions gather, analyze, and utilize data. The sheer amount of data available has opened up countless opportunities for better customer understanding and insight into business operations. Companies can use this data to create predictive models that identify emerging trends in customer behavior, market activity, and other aspects of their business. By utilizing these insights, companies can make smarter decisions about products, services, marketing strategies, and more. The Metaverse is another technology with massive potential for disrupting the Fintech sector. The Metaverse enables businesses to offer virtual goods and services without physical stores or retail outlets. This makes it easier for companies to scale their operations across multiple regions and gives customers more options when choosing a financial service provider. Additionally, using digital currencies like Bitcoin helps reduce transaction costs significantly while increasing security compared to traditional payment methods like credit cards or PayPal.Keywords
Big Data, Metaverse, FinTech, Innovation, Data Security.References
- Cuțitoi, A. C. (2022). Machine Vision Algorithms, Sensory Data Mining Techniques, and Geospatial Mapping Tools in the Blockchain-based Virtual Economy. Review of Contemporary Philosophy, (21), 223-238.
- El Nokiti, A., Shaalan, K., Salloum, S., Aburayya, A., Shwedeh, F., & Shameem, B. (2022). Is Blockchain the answer? A qualitative Study on how Blockchain Technology Could be used in the Education Sector to Improve the Quality of Education Services and the Overall Student Experience. Computer Integrated Manufacturing Systems, 28(11), 543-556.
- Taherdoost, H. (2023). Blockchain: a catalyst in fintech future revolution. Future Technology (FUTECH).
- Renduchintala, T., Alfauri, H., Yang, Z., Pietro, R. D., & Jain, R. (2022). A Survey of Blockchain Applications in the FinTech Sector. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 185.
- Visconti, R. M. (2022). The Valuation of Digital Intangibles: Technology, Marketing, and the Metaverse. Springer Nature.
- Charamba, K. (2022). Beyond The Corporate Responsibility To Respect Human Rights In The Dawn Of A Metaverse. University of Miami International and Comparative Law Review, 30(1), 110.
- Lo, C. (2022). The Digital Renminbi’s Disruption: Shaping the Global Economic, Financial and Policy Landscapes. Emerald Group Publishing.
- Shi, Y., Gao, Y., Luo, Y., & Hu, J. (2022). Fusions of industrialisation and digitalisation (FID) in the digital economy: Industrial system digitalisation, digital technology industrialisation, and beyond. Journal of Digital Economy.
- Polas, M. R. H., Jahanshahi, A. A., Kabir, A. I., Sohel-Uz-Zaman, A. S. M., Osman, A. R., & Karim, R. (2022). Artificial Intelligence, Blockchain Technology, and Risk-Taking Behavior in the 4.0 IR Metaverse Era: Evidence from Bangladesh-Based SMEs. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 168.
- Indriasari, E., Prabowo, H., Gaol, F. L., & Purwandari, B. (2022). Intelligent Digital Banking Technology and Architecture: A Systematic Literature Review. International Journal of Interactive Mobile Technologies, 16(19).
- Bao, H., &Roubaud, D. (2021). Recent Development in Fintech: Non-Fungible Token. FinTech, 1(1), 44-46.
- Dash, B., & Sharma, P. (2022, December). Impact of Digitalization on Shaping Consumer-Centered Smart Healthcare System-A Comprehensive Study. In 2nd International Conference on Advances in Computing & Information Technologies (CACIT 2022).
- Moro Visconti, R. (2022). The Valuation of Digital Intangibles: Technology, Marketing, and the Metaverse.
- Lu, L. (2023). Fintech: Technology-Enabled Financial Innovation for Digital Trade. In Research Handbook on Digital Trade. Edward Elgar.
- Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., … & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279-295.
- Bisht, D., Singh, R., Gehlot, A., Akram, S. V., Singh, A., Montero, E. C., … & Twala, B. (2022). Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective. Electronics, 11(19), 3252.
- Xu, J. (2022). The Technical Foundations of FinTech: ABCDI and More. Future And Fintech, The: AbcdiAnd Beyond, 37.
- Bhat, J. R., AlQahtani, S. A., & Nekovee, M. (2022). FinTech enablers, use cases, and role of future internet of things. Journal of King Saud University-Computer and Information Sciences.
- Dash, B., Sharma, P., & Ali, A. (2022). Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech. International Journal of Software Engineering & Applications, 13(4), 1-13.
- Turi, A. N. (Ed.). (2023). Financial Technologies and DeFi: A Revisit to the Digital Finance Revolution. Springer Nature.
- Ansari, M. F., Dash, B., Sharma, P., &Yathiraju, N. (2022). The Impact and Limitations of Artificial Intelligence in Cybersecurity: A Literature Review. International Journal of Advanced Research in Computer and Communication Engineering, 11(9), 81-90.
- Yathiraju, N. (2022). Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System. International Journal of Electrical, Electronics and Computers, 7(2), 1-26.
- Dash, B., Sharma, P., Ansari, M. F., & Swayamsiddha, S. (2022a). A review of ONDC's digital warfare in India taking on the e-commerce giants. International Journal of Engineering & Technology, 11(2), 96-99.
- Melnyk, M., Kuchkin, M., & Blyznyukov, A. (2022). Commercial Banks: Traditional Banking Models Vs. Fintechs Solutions.
- Marr, B. (2022, November 17). Banking in the metaverse – the next frontier for Financial Services. Forbes. Retrieved January 23, 2023, from https://www.forbes.com/sites/bernardmarr/2022/11/16/banking-in-the-metaverse--the-next-frontier-for-financial-services/?sh=6510596e22d1
- Dubey, V., Mokashi, A., Pradhan, R., Gupta, P., &Walimbe, R. (2022). Metaverse and Banking Industry–2023 The Year of Metaverse Adoption.
- Shevlin, R. (2022, April 14). JPMorgan opens a bank branch in the metaverse (but it's not what you think it's for). Forbes. Retrieved January 23, 2023, from https://www.forbes.com/sites/ronshevlin/2022/02/16/jpmorgan-opens-a-bank-branch-in-the-metaverse-but-its-not-for-what-you-think-its-for
- Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms
Authors
1 School of Computer and Information Science, University of the Cumberlands, KY, US
2 Department of Computer and Information Science, University of the Cumberlands, KY, US
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 15, No 1 (2023), Pagination: 31-42Abstract
The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call.Keywords
Artificial Intelligence, NLP, Microsoft Teams, Speech Identification, Video Call, Video Signal Data, Machine Learning.References
- Nassif, A.B., Shahin, I., Attili, I., Azzeh, M. and Shaalan, K., (2019). Speech recognition using deep neural networks: A systematic review. IEEE Access, 7, pp.19143-19165.
- Duan, S., Zhang, J., Roe, P. and Towsey, M., (2014) A survey of tagging techniques for music, speech, and environmental sound. Artificial Intelligence Review, 42(4), pp.637-661.
- Hubbard, M. and Bailey, M.J., (2018) Mastering Microsoft Teams. Mastering Microsoft Teams. https://doi. Org/10.1007/978-1-4842-3670-3.
- Skelly, P., Hester, S., Ryan, T., Underwood, J., Bowden, E., Evans, T.K., Geurts, G., Lang, K., Liemohn, K., McCann, C. and Schubert Sr, M., (2014) The New Business Operating System: Combining Office 365 and the Microsoft Cloud Ecosystem to Create Business Value.
- Ganesan, V. and Manoharan, S., (2015) Surround noise cancellation and speech enhancement using sub band filtering and spectral subtraction. Indian Journal of Science and Technology, 8(33), p.1.
- Reddy, C.K., Gopal, V., Cutler, R., Beyrami, E., Cheng, R., Dubey, H., Matusevych, S., Aichner, R., Aazami, A., Braun, S. and Rana, P., (2020) The interspeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results. arXiv preprint arXiv:2005.13981.
- Lin, S., Ryabtsev, A., Sengupta, S., Curless, B.L., Seitz, S.M. and Kemelmacher-Shlizerman, I., (2021) Real-time high-resolution background matting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8762-8771).
- Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B. and Zimmermann, T., (2019) May. Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 291-300). IEEE.
- Strake, M., Defraene, B., Fluyt, K., Tirry, W. and Fingscheidt, T., (2020) October. INTERSPEECH 2020 Deep Noise Suppression Challenge: A Fully Convolutional Recurrent Network (FCRN) for Joint Dereverberation and Denoising. In INTERSPEECH (pp. 2467-2471).
- Panesar, A., (2019) Machine learning and AI for healthcare (pp. 1-73). Coventry, UK: Apress.
- Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N. and Kingsbury, B., (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), pp.82-97.
- Brynjolfsson, E. and Mcafee, A.N.D.R.E.W., (2017) Artificial intelligence, for real. Harvard Business Review.
- Das, S., Dey, A., Pal, A. and Roy, N., (2015) Applications of artificial intelligence in machine learning: review and prospect. International Journal of Computer Applications, 115(9).
- Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R. and Shahabi, C., (2014) Big data and its technical challenges. Communications of the ACM, 57(7), pp.86-94.
- Crick, M. ed., (2016) Power, Surveillance, and Culture in YouTube's Digital Sphere. IGI Global.
- Noda, K., Yamaguchi, Y., Nakadai, K., Okuno, H.G. and Ogata, T., (2015) Audio-visual speech recognition using deep learning. Applied Intelligence, 42(4), pp.722-737.
- Rösler, P., (2018) On the End-to-End Security of Group Chats in In-stant Messaging Protocols.
- Cutler, R., Hosseinkashi, Y., Pool, J., Filipi, S., Aichner, R., Tu, Y. and Gehrke, J., (2021) Meeting Effectiveness and Inclusiveness in Remote Collaboration. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), pp.1-29.
- Boyes, H., Hallaq, B., Cunningham, J., and Watson, T., (2018) The industrial internet of things (IIoT): An analysis framework. Computers in industry, 101, pp.1-12.
- Lane, N.D. and Georgiev, P., (2015) February. Can deep learning revolutionize mobile sensing? In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications (pp. 117-122).
- Dash, B., Sharma, P., & Ali, A. (2022). Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech. International Journal of Software Engineering & Applications, 13(4), 1-13.
- Wang, B., Qi, Z., Ma, R., Guan, H. and Vasilakos, A.V., (2015) A survey on data center networking for cloud computing. Computer Networks, 91, pp.528-547.
- Choudhary, B. and Rakesh, S.K., (2016) February. An approach using agile method for software development. In 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH) (pp. 155-158). IEEE.
- Attaran, M., Attaran, S. and Kirkland, D., (2019) The need for digital workplace: increasing workforce productivity in the information age. International Journal of Enterprise Information Systems (IJEIS), 15(1), pp.1-23.
- Géron, A., (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.
- Tidd, J. and Bessant, J.R., (2020) Managing innovation: integrating technological, market and organizational change. John Wiley & Sons.
- Yang, P.R., and Meals, R.A., (2014) How to establish an interactive conference and Journal Club. The Journal of hand surgery, 39(1), pp.129-133.
- Mutanen, T.P., Metsomaa, J., Liljander, S. and Ilmoniemi, R.J., (2018) Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm. Neuroimage, 166, pp.135-151.
- Jha, K., Doshi, A., Patel, P., and Shah, M., (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, pp.1-12.
- Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K. and Zhang, J., (2019) Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), pp.1738-1762.
- Bender, E.M. and Friedman, B., (2018) Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, pp.587-604.
- Zhang, Z., (2012) Microsoft Kinect sensor and its effect. IEEE Multimedia, 19(2), pp.4-10.
- Garnett, R., Huegerich, T., Chui, C. and He, W., (2005) A universal noise removal algorithm with an impulse detector. IEEE Transactions on image processing, 14(11), pp.1747-1754.
- Alshemali, B. and Kalita, J., (2020) Improving the reliability of deep neural networks in NLP: A review. Knowledge-Based Systems, 191, p.105210.
- Dörk, M., Riche, N.H., Ramos, G. and Dumais, S., (2012) Pivotpaths: Strolling through faceted information spaces. IEEE transactions on visualization and computer graphics, 18(12), pp.2709-2718.
- Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A. and Ogu, I.O., (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 9(5), p.5665.
- Fernández, A., del Río, S., López, V., Bawakid, A., del Jesus, M.J., Benítez, J.M. and Herrera, F., (2014) Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5), pp.380-409.
- Pan, Y., Yao, T., Li, Y., Wang, Y., Ngo, C.W. and Mei, T., (2019). Transferrable prototypical networks for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2239-2247).
- Rodriguez, M., Piattini, M. and Ebert, C., (2019) Software verification and validation technologies and tools. IEEE Software, 36(2), pp.13-24.
- Sharma, P., Dash, B., and Ansari, M. F., (2022) Anti-phishing techniques – a review of Cyber Defense Mechanisms, IJARCCE, vol. 11, no. 7, 2022.
- Indu, I., Anand, P.R. and Bhaskar, V., (2018) Identity and access management in cloud environment: Mechanisms and challenges. Engineering science and technology, an international journal, 21(4), pp.574-588.
- Barona, R. and Anita, E.M., (2017) April. A survey on data breach challenges in cloud computing security: Issues and threats. In 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (pp. 1-8). IEEE.
- Elsayed, M.A.M.A., (2018) Advancing Security Services for Cloud Applications (Doctoral dissertation, SQueen's University (Canada)).
- Ansari, M. F., Dash, B., Sharma P., and Yathiraju N., (2022) The impact and limitations of Artificial Intelligence in cybersecurity: A literature review, IJARCCE, vol. 11, no. 9, 2022.
- Thomas, K., Li, F., Zand, A., Barrett, J., Ranieri, J., Invernizzi, L., Markov, Y., Comanescu, O., Eranti, V., Moscicki, A. and Margolis, D., (2017, October) Data breaches, phishing, or malware? Understanding the risks of stolen credentials. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security (pp. 1421-1434).
- Attaran, M., Attaran, S. and Kirkland, D., (2019) The need for digital workplace: increasing workforce productivity in the information age. International Journal of Enterprise Information Systems (IJEIS), 15(1), pp.1-23.
- Davenport, T.H., (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), pp.73-80.
- Haenlein, M. and Kaplan, A., (2019) A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), pp.5-14.
- Ferrell, J. and Kline, K., (2018) Facilitating trust and communication in virtual teams. People & Strategy, 41(2), pp.30-36.
- Dash, B. (2022) REMOTE WORK AND INNOVATION DURING THIS COVID-19 PANDEMIC: AN EMPLOYERS’CHALLENGE.
- Ferrario, A., Loi, M. and Viganò, E., (2020). In AI we trust Incrementally: a Multi-layer model of trust to analyze Human-Artificial intelligence interactions. Philosophy & Technology, 33(3), pp.523-539.
- Dash, B., & Sharma, P. (2022). Role of Artificial Intelligence in Smart Cities for Information Gathering and Dissemination (A Review). Academic Journal of Research and Scientific Publishing, 4(39).