The Cutting Edge technologies in Computer Science:
Main Article Content
Abstract
Technological gadgets, methods, and accomplishments that make use of the most recent and advanced IT advances are examples of cutting-edge technology. "Cutting edge" is a term commonly used to describe the most advanced and forward-thinking companies in the IT industry. The term "cutting-edge technology" is used to describe the most advanced and up-to-date technological features, as opposed to "Cutting-edge technology," which is so novel that it presents risks to consumers. While the term "technology" is most often associated with computer and electronic devices, it can refer to advancements in virtually any field. The author of this paper has properly cited the most up-to-date cutting-edge research.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
Lyu, Y., Lv(u), X. (2022). The Cutting-Edge Applications and Trends of Big Data and AI Technology in the Digitalization of the Fashion Industry. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 102. Springer, Singapore. https://doi.org/10.1007/978-981-16-7466-2_119
G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, O.A. von Lilienfeld, and K. Müller. Learning invariant representations of molecules for atomization energy prediction. In P. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 449–457. 2012.
Mishra, A., Khan, M.H., Khan, W., Khan, M.Z., Srivastava, N.K. (2022). A Comparative Study on Data Mining Approach Using Machine Learning Techniques: Prediction Perspective. In: Husain, M.S., Adnan, M.H.B.M., Khan, M.Z., Shukla, S., Khan, F.U. (eds) Pervasive Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77746-3_11
D. Potts, G. Steidl, and M. Tasche. Fast and stable algorithms for discrete spherical Fourier transforms. Linear Algebra and its Applications, 275:433–450, 1998.
D. Potts, J. Prestin, and A. Vollrath. A fast algorithm for nonequispaced Fourier transforms on the rotation group. Numerical Algorithms, pages 1–28, 2009.
A. Raj, A. Kumar, Y. Mroueh, P.T. Fletcher, et al. Local group invariant representations via orbit embeddings. arXiv preprint arXiv:1612.01988, 2016.
S. Ravanbakhsh, J. Schneider, and B. Poczos. Deep learning with sets and point clouds. In International Conference on Learning Representations (ICLR) – workshop track, 2017.
D.N. Rockmore. Recent Progress and Applications in Group FFTS. NATO Science Series II: Mathematics, Physics and Chemistry, 136:227–254, 2004.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld. Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters, 108:058301, 2012.
M. Savva, F. Yu, H. Su, A. Kanezaki, T. Furuya, R. Ohbuchi, Z. Zhou, R. Yu, S. Bai, X. Bai, M. Aono, A. Tatsuma, S. Thermos, A. Axenopoulos, G. Th. Papadopoulos, P. Daras, X. Deng, Z. Lian, B. Li, H. Johan, Y. Lu, and S. Mk. Large-Scale 3D Shape Retrieval from ShapeNet Core55. In Ioannis Pratikakis, Florent Dupont, and Maks Ovsjanikov, editors, Eurographics Workshop on 3D Object Retrieval. The Eurographics Association, 2017. ISBN 978-3-03868-030-7. doi: 10.2312/3dor.20171050.
Y.C. Su and K. Grauman. Learning spherical convolution for fast features from 360 imagery. Adv. Neural Inf. Process. Syst., 2017. M. Sugiura. Unitary Representations and Harmonic Analysis. John Wiley & Sons, New York, London, Sydney, Toronto, 2nd edition, 1990.
M.E. Taylor. Noncommutative Harmonic Analysis. American Mathematical Society, 1986. ISBN 0821815237.
M. Weiler, F.A. Hamprecht, and M. Storath. Learning steerable filters for rotation equivariant CNNs. 2017.
D.E. Worrall, S.J. Garbin, D. Turmukhambetov, and G.J. Brostow. Harmonic networks: Deep translation and rotation equivariance. In CVPR, 2017.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola. Deep sets. arXiv preprint arXiv:1703.06114, 2017a.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R.R. Salakhutdinov, and A.J. Smola. Deep sets. In Advances in Neural Information Processing Systems 30, pages 3393–3403, 2017b.
Tripathi, M. M., Haroon, M., Khan, Z., & Husain, M. S. (2022). Security in Digital Healthcare System. In Pervasive Healthcare (pp. 217-231). Springer, Cham.
Jänecke M (2019) Techtextil draws together threads from trends in digitalization, cities of the future and sustainability. Technische Textilien 62(2):51–52
Khan, M. Z., Mishra, A., & Khan, M. H. (2020). Cyber Forensics Evolution and Its Goals. In Critical Concepts, Standards, and Techniques in Cyber Forensics (pp. 16-30). IGI Global.
Zheng, L., & Jiang, Y. (2021). Combining dissimilarity measure for the study of evolution in scientific fields. arXiv preprint arXiv:2104.10996.
Farooqui, M. Z., Shoaib, M., & Khan, M. Z. (2014). A comprehensive survey of page replacement algorithms. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume, 3.
Gaur A, Tiwari S, Kumar C et al (2020) Flexible, lead-free nanogenerators using poly(vinylidene fluoride) nanocomposites. Energy Fuels 34(5):6239–6244
Husain, M. S., Adnan, M. H. B. M., Khan, M. Z., Shukla, S., & Khan, F. U. (2021). Pervasive Healthcare: A Compendium of Critical Factors for Success. Springer International Publishing AG.
Sebald AK, Jacob F (2020) What help do you need for your fashion shopping? A typology of curated fashion shoppers based on shopping motivations. Eur Manag J 38(2):319–334
Khan, M. Z., Khanam, M. A., & Khan, M. H. (2016). Software Testability in Requirement Phase: A Review. International Journal of Advanced Research in Computer and Communication Engineering, 5(4), 1031-1035.
Khan, M. Z., Khanam, M. A., & Khan, M. H. (2017). Requirement Based Testability Estimation Model of Object Oriented Software. Oriental Journal of Computer Science and Technology, 10(4), 793-801.
Khan, M. Z., Khanam, M. A., & Khan, M. H. (2017). REQUIREMENT UNDERSTANDABILITY QUANTIFICATION MODEL OF OBJECT ORIENTED SOFTWARE. International Journal of Advanced Research in Computer Science, 8(7).
Khan, M. Z., Khanam, M. A., & Khan, M. H. (2017). Requirement Modifiability Quantification Model of Object Oriented Software. Global Journal of Pure and Applied Mathematics, 13(3), 2017.
Keramatian A, Gulisano V, Papatriantafilou M et al (2021) MAD-C: multi-stage approximate distributed cluster-combining for obstacle detection and localization. J Parallel Distrib Comput 147(9):248–267
Ahamad, F., Khan, M. Z., & Akhtar, N. (2021). An empirical study on the current state of internet of multimedia things (IoMT). International Journal of Engineering Research in Computer Science and Engineering.
Ying Z (2020) “Digital Survival†and the diversification of stage art. Popul Color 000(003):112–113
Khan, M. Z., Husain, M. S., & Shoaib, M. (2020). Introduction to email, web, and message forensics. In Critical concepts, standards, and techniques in cyber forensics (pp. 174-186). IGI Global.
Xiaohong Y, Dan Y (2019) Digital innovation to solve the regional imbalance in the development of cultural heritage—thinking about the investigation of dinosaur fossil sites in western Liaoning. Popul Color 07:165–166
Khan, M. U., Beg, R., & Khan, M. Z. (2012). Improved Line Drawing Algorithm: An Approach and Proposal. UACEE International Journal of Computer Science and its Applications, 122-127.
Grobelink M (2019) Assessing the comfort of functional fabrics for smart clothing using subjective evaluation. J Ind Text 48(8):1310–1326
Khan, M. Z., Kidwai, M. S., Ahamad, F., & Khan, M. U. (2021, March). Hadoop based EMH framework: A Big Data approach. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1068-1070). IEEE.
Miao Y, Wu G, Liu C et al (2019) Green cognitive body sensor network: architecture, energy harvesting, and smart clothing-based applications. IEEE Sens J 19(9):8371–8378
Khan, M. Z., & Shoaib, M. (2019). Healthcare Analytics in the Modern Era: A Survey. International Journal of Research in Advent Technology, 7(3), 132-13.
Jeong EG, Jeon Y, Cho SH et al (2019) Textile-based washable polymer solar cells for optoelectronic modules: toward self-powered smart clothing. Energy Environ Sci 12(6):1878–1889
Kidwai, M. S., & Khan, M. Z. (2021, March). A new perspective of detecting and classifying neurological disorders through recurrence and machine learning classifiers. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 200-206). IEEE.
Husain, M. S., & Khan, M. Z. (Eds.). (2019). Critical Concepts, Standards, and Techniques in Cyber Forensics. IGI Global.
Lepak-Kuc S, Podsiady B, Skalski A et al (2019) Highly conductive carbon nanotube-thermoplastic polyurethane nanocomposite for smart clothing applications and beyond. Nanomaterials 9(9):1287
Singh, J., & Khan, M. Z. (2019). Detection of Fake Profile in Social Media. Journal of Emerging Technologies and Innovative Research, 6(6), 38-42.
C. Olah. Groups and Group Convolutions, 2014. URL https://colah.github.io/posts/ 2014-12-Groups-Convolution/.
Srivastava, N. & Khan, M. Z. (2021). Machine Learning Based Crowd Behaviour Analysis and Prediction.
Siddiqui, Mohd. Maroof & Jain, Ruchin & Kidwai, Mohd & Khan, Mohammad. (2022). Recording of eeg Signals and Role in Diagnosis of Sleep Disorder. Biomedical and Pharmacology Journal. 15. 1421-1426. 10.13005/bpj/2479.
Mishra, A., Khan, M.H., Khan, W., Khan, M.Z., Srivastava, N.K. (2022). A Comparative Study on Data Mining Approach Using Machine Learning Techniques: Prediction Perspective. In: Husain, M.S., Adnan, M.H.B.M., Khan, M.Z., Shukla, S., Khan, F.U. (eds) Pervasive Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77746-3_11
Baier-Fuentes, H., Merigó, J. M., Amorós, J. E., & Gaviria-MarÃn, M. (2019). International entrepreneurship: a bibliometric overview. International Entrepreneurship and Management Journal. https://doi.org/10.1007/s11365-017-0487-y
Chiu, W. T., & Ho, Y. S. (2007). Bibliometric analysis of tsunami research. Scientometrics. https : // doi.org /10.1007/s11192-005 -1523-1
Gogoi M., & Barooah, P. K. .(2016). Bibliometric Analysis of Indian Journal of Chemistry, Section B To Study the Usage Pattern of Information in the Field of Material Science.Library Philosophy & Practice, (February), 116. Retrieved from http: // search.ebscohost.com/login. aspx? direct = true&db = llf & AN = 113437690&site = ehost-live
Guimerà , A. D., Gonzà lez, X. O., & Margalef, M. V. (2007). Twenty-five years spreading geographical research: Bibliometric analysis of Documents d'Anà lisiGeogrà fica [Vint-i-cincanys de difusió de la recercageogrà fica: Anà lisibibliométrica de la revista Documents d'Anà lisiGeogrà fica]. Documents d'AnalisiGeografica.
Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ale Ebrahim, N., Gani, A., Ebrahimi, A., & Shamshirband, S. (2017). A bibliometric approach to tracking big data research trends.Journal of Big Data, 4(1), 118. https://doi.org/10.1186/ s40537-017-0088-1
Koseoglu, M. A., Rahimi, R., Okumus, F., & Liu, J. (2016). Bibliometric studies in tourism. Annals of Tourism Research. https ://doi. org /10.1016/j.annals. 2016.10.006
Ma, R. (2012). Author bibliographic coupling analysis: A test based on a Chinese academic database. Journal of Informetrics. https://doi.org/10.1016/ j.joi.2012.04.006
Paul, G., & Deoghuria, S. (2009). Indian journal of physics? : A scientometric analysis. 10th International Conference on Webometrics, Informetrics and Scientometrics& 15th COLLNET Meeting 2014 Indian, 2092016.
Thavamani, K. (2014). Authorship Pattern and Collaborative Research Work in Pearl: A Journal of Library and Information Science: A Scientrometric Study. Pearl?: A Journal of Library and Information Science. https://doi.org /10.5958 / 0975- 6922.2014.00740.2
L. C. Blum and J.-L. Reymond. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc., 131:8732, 2009.
W. Boomsma and J. Frellsen. Spherical convolutions and their application in molecular modelling. In I Guyon, U V Luxburg, S Bengio, H Wallach, R Fergus, S Vishwanathan, and R Garnett, editors, Advances in Neural Information Processing Systems 30, pages 3436–3446. Curran Associates, Inc., 2017.
A.X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
G.S. Chirikjian and A.B. Kyatkin. Engineering Applications of Noncommutative Harmonic Analysis. CRC Press, 1 edition, may 2001. ISBN 9781420041767.
T.S. Cohen and M. Welling. Group equivariant convolutional networks. In Proceedings of The 33rd International Conference on Machine Learning (ICML), volume 48, pages 2990–2999, 2016.
T.S. Cohen and M. Welling. Steerable CNNs. In ICLR, 2017. T.S. Cohen, M. Geiger, J. Koehler, and M. Welling. Convolutional networks for spherical signals. In ICML Workshop on Principled Approaches to Deep Learning, 2017.
S. Dieleman, K. W. Willett, and J. Dambre. Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 2015.
S. Dieleman, J. De Fauw, and K. Kavukcuoglu. Exploiting Cyclic Symmetry in Convolutional Neural Networks. In International Conference on Machine Learning (ICML), 2016.
J.B. Drake, P.H. Worley, and E.F. D’Azevedo. Algorithm 888: Spherical harmonic transform algorithms. ACM Trans. Math. Softw., 35(3):23:1–23:23, 2008. doi: 10.1145/1391989.1404581.
J.R. Driscoll and D.M. Healy. Computing Fourier transforms and convolutions on the 2-sphere. Advances in applied mathematics, 1994.
G.B. Folland. A Course in Abstract Harmonic Analysis. CRC Press, 1995. R. Gens and P. Domingos. Deep Symmetry Networks. In Advances in Neural Information Processing Systems (NIPS), 2014.
B. Gutman, Y. Wang, T. Chan, P.M. Thompson, and others. Shape registration with spherical cross correlation. 2nd MICCAI workshop, 2008.
N. Guttenberg, N. Virgo, O. Witkowski, H. Aoki, and R. Kanai. Permutation-equivariant neural networks applied to dynamics prediction. 2016.
D. Healy, D. Rockmore, P. Kostelec, and S. Moore. FFTs for the 2-Sphere – Improvements and Variations. The journal of Fourier analysis and applications, 9(4):340–385, 2003.
P.J. Kostelec and D.N. Rockmore. SOFT: SO(3) Fourier Transforms. 2007. URL http://www. cs.dartmouth.edu/~geelong/soft/soft20_fx.pdf.
P.J. Kostelec and D.N. Rockmore. FFTs on the rotation group. Journal of Fourier Analysis and Applications, 14(2):145–179, 2008.
S. Kunis and D. Potts. Fast spherical Fourier algorithms. Journal of Computational and Applied Mathematics, 161:75–98, 2003.
A. Makadia, C. Geyer, and K. Daniilidis. Correspondence-free structure from motion. Int. J. Comput. Vis., 75(3):311–327, December 2007.
D.K. Maslen. Efficient Computation of Fourier Transforms on Compact Groups. Journal of Fourier Analysis and Applications, 4(1), 1998