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• Open Access   Article

  An Emoji Based Emotion Recognition System for Fixed and Active Corporate Webpages

  Fatima Isiaka, Zainab Adamu, Muhammad A. Adamu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.72-78, Dec-2021

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  In AI, one of its major contributions in modern technology is the application of emotion recognition tools which is mostly based on facial expression and modification of its inference engine. The facial recognition scheme is mostly built to understand user expression in an online business webpage on a marketing site but has limited abilities to recognise elusive expressions. The basic emotions are expressed when interrelating and socialising with other personals online. At most times studying how to understand user expression is often a most tedious task, especially the subtle expressions. An emotion recognition system can be used to optimise and reduce complexity in understanding users’ subconscious thoughts and reasoning through their pupil changes. This paper demonstrates the use of a PC webcam to read in eye movement data that includes pupil changes as part of distinct user attributes. A custom eye movement algorithm (CAMA) is used to capture users’ activity and record the data which is served as an input model to an inference engine (artificial neural network (ANN)) that helps to predict user emotional response conveyed as emoticons on the webpage. The dynamic webpage is rendered static for the gaze point and emoji coordinated reiteration. The result from the error in performance shows that ANN is most adaptable to user behaviour prediction and can be used for the system’s modification paradigm.

  Key-Words / Index Term
  AI, Static Business webpages, artificial neural network, emotion recognition system, Pupil changes, User expression, Eye movement behaviour

  References
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  Citation
  Fatima Isiaka, Zainab Adamu, Muhammad A. Adamu, "An Emoji Based Emotion Recognition System for Fixed and Active Corporate Webpages," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.72-78, 2021

• Open Access   Article

  Lapatinib (Tyverb®) Against Zika Virus

  Ivan Vito Ferrari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.79-81, Dec-2021

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  Abstract
  After Docking analysis of over 300 drugs, processed with Pyrx, a small Virtual Screening software, into the active site of protein of RNA-directed RNA polymerase NS5, ( ID PDB 6LD3 chain A), could be an excellent candidate drug against Zika virus. Indeed, from the results of Autodock Vina has a good Binding affinity value, ca. -9.6 kcal mol -1, good estimation inhibitory constant (Ki) value ca. 91 nM and goof Ligand efficiency ca -0.24 kcal mol -1. These results are higher to the crystallized ligand G8O ( 2,4-dimethoxy-5-thiophen-2-yl- benzoic acid ) complexed in this studied protein.

  Key-Words / Index Term
  Lapatinib, Zika virus (ZIKV), Autodock 4, Autodock Vina

  References
  [1] Gharbi-Ayachi, A., Santhanakrishnan, S., Wong, Y. H., Chan, K. W., Tan, S. T., Bates, R. W., and Lescar, J. “ Non-Nucleoside Inhibitors of Zika virus RNA-dependent RNA polymerase”, Journal of Virology, Vol. 94, Issue. 21, pp. e00794-20, 2020.
  [2] Faye, O., Freire, C. C., Iamarino, A., Faye, O., de Oliveira, J. V. C., Diallo, M., and Sall, A. A. “ Molecular evolution of Zika virus during its emergence in the 20th century”, PLoS neglected tropical diseases, Vol. 8, Issue.1, pp.e2636, 2014.
  [3] Dallakyan, S., and Olson, A. J. “Small-molecule library screening by docking with PyRx”, In Chemical biology pp. 243-250, Humana Press, New York, NY, 2015.
  [4] Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., and Moreno, E. “ AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4”,Biology direct, Vol. 15, Issue.1, pp.1-12, 2020.
  [5] Trott, Oleg, and Arthur J. Olson. "AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading." Journal of computational chemistry, Vol. 31, Isuue. 2 , pp. 455-461, 2010.
  [6] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S. and Olson, A. J. “ Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility”, J. Computational Chemistry Vol. 30, Issue. 16, pp. 2785-91, 2009.
  [7] Gharbi-Ayachi, A., Santhanakrishnan, S., Wong, Y. H., Chan, K. W., Tan, S. T., Bates, R. W., and Lescar, J.” Non-Nucleoside Inhibitors of Zika virus RNA-dependent RNA polymerase”, Journal of Virology, Vol.94, Issue.21, pp.e00794-20, 2020.
  [8] Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., and Ferrin, T. E. “UCSF Chimera—a visualization system for exploratory research and analysis”, Journal of computational chemistry, Vol. 25, Issue.13, pp.1605-1612, 2004.
  [9] Guex, Nicolas, and Manuel C. Peitsch. "SWISS?MODEL and the Swiss?Pdb Viewer: an environment for comparative protein modeling." Electrophoresis, Vol. 18, Issue.15 , pp. 2714-2723, 1997.
  [10] Jász, Á.; Rák, Á.; Ladjánszki, I.; Tornai, G. J.; Cserey, G. “Towards Chemically Accurate QM/MM Simulations on GPUs”, Journal of Molecular Graphics and Modelling Vol., 96, pp.107536, 2020.
  [11] Kim, H. P., Yoon, Y. K., Kim, J. W., Han, S. W., Hur, H. S., Park, J., and Kim, T. Y. “ Lapatinib, a dual EGFR and HER2 tyrosine kinase inhibitor, downregulates thymidylate synthase by inhibiting the nuclear translocation of EGFR and HER2”, PloS one, Vol.4, Issue.6 ,pp. e5933, 2009.

  Citation
  Ivan Vito Ferrari , "Lapatinib (Tyverb®) Against Zika Virus," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.79-81, 2021

• Open Access   Article

  A Super Permutation Algorithm

  Arogya Dahal, Kalyan Khatiwada, Siddhartha Gautam

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.82-84, Dec-2021

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  We present an algorithm that ?nds a string that contains every permutation of n symbols as a contiguous sub string, also called super permutation, from a given starting permutation. The algorithm is simple and can be implemented for any element, as the algorithm is independent of the representation of n. The algorithm also produces shortest super permutation for n smaller than 6.

  Key-Words / Index Term
  Super Permutation; Algorithm; Mathematics

  References
  [1] Robin Houston. “Tackling the minimal superpermutation problem.” arXiv:1408.5108, 2014
  [2] Gilbert Laporte and Silvano Martello. “The selective travelling salesman problem.” “Discrete applied mathematics”: pp. 193–207, 1990.
  [3] Desmond Lobo, Kalpita Wagaskar, Sarang Gawane, Clifford Fernandes, "Hybrid Quantum - Classical AI based approach to solve the Traveling Tournament Problem", International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.83-90, 2021.
  [4] S. Suriya, M. Rahul Kumar, "Solving Travelling Salesman Problem using an Enhanced Ant Colony Algorithm", International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.214-222, 2019.

  Citation
  Arogya Dahal, Kalyan Khatiwada, Siddhartha Gautam, "A Super Permutation Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.82-84, 2021

• Open Access   Article

  Blind Docking Analysis of Potential Drugs against SARS-COV-1 and SARS-COV-2 Proteins

  Ivan Vito Ferrari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.85-89, Dec-2021

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  — In this short communication, more than 300 drugs were investigated by Blind Docking approach, by Autodock Vina Scoring Function with Pyrx Software. From Blind Docking Results, Deslanoside, Dactinomycin, Teicoplanin aglycone, Conivaptan, Paritaprevir, Acetyldigitoxin, Nafarelin, Lanatoside_C, Dydroergocristine, Dydroergotamine, Tirilazad, and Ubrogepant are potential candidates against SARS-COV-1 and SARS-COV-2 proteins. Particular attention, Acetyldigitoxin, Dactinomycin, and Deslanoside with Binding Energy about -12 .00 kcal mol -1 are higher binding Energies values against SARS-COV-2 Mpro. Regarding SARS-COV-2 RBD, Teicoplanin aglycone and Dactinomycin have a potentially significant role with a binding energy value of -12.50 kcal mol -1.

  Key-Words / Index Term
  Autodock Vina, PyRx

  References
  [1] Dallakyan, S., and Olson, A. J. “Small-molecule library screening by docking with PyRx”, Methods in molecular biology (Clifton, N.J.), Vol. 1263, pp. 243–250, 2015.https://doi.org/10.1007/978-1-4939-2269-7_19.
  [2] Llabrés, M., and Valiente, G. “ Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2”, PloS one, Vol.15, Issue. 12, pp.e0236304, 2020.https://doi.org/10.1371/journal.pone.0236304.
  [3] Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., and Olson, A. J. “Computational protein-ligand docking and virtual drug screening with the AutoDock suite”, Nature protocols, Vol.11, Issue.5, pp.905–919,2016. https://doi.org/10.1038/nprot.2016.051.
  [4] Trott, O., and Olson, A. J. “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading”, Journal of computational chemistry, Vol.31, Issue.2, pp. 455–461, 2010. https://doi.org/10.1002/jcc.21334
  [5] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., and Olson, A. J.” AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility”, Journal of computational chemistry, Vol.30, Isssue.16, pp.2785–2791, 2009. https://doi.org/10.1002/jcc.21256.
  [6] Kaplan, W., & Littlejohn, T. G.” Swiss-PDB Viewer (Deep View)”Briefings in bioinformatics, Vol.2, Issue. 2, pp.195–197, 2001. https://doi.org/10.1093/bib/2.2.195.
  [7] Sulimov, A. V., Kutov, D. K., Ilin, I. S., & Sulimov, V. B. “Doking s kombinirovannym primeneniem silovogo polia i kvantovo-khimicheskogo metoda [Docking with combined use of a force field and a quantum-chemical method]”, Biomeditsinskaia khimiia, Vol.65, Issue.2, pp.80–85, 2019.

  Citation
  Ivan Vito Ferrari , "Blind Docking Analysis of Potential Drugs against SARS-COV-1 and SARS-COV-2 Proteins," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.85-89, 2021

• Open Access   Article

  Gepants against SARS COV-2 Main Protease

  I.V. Ferrari, M. Di Mario, R. Narducci, P. Patrizio

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.90-93, Dec-2021

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  Abstract
  in the present in Silico work, we focused on “Gepants", used for the treatment of Migraine, as a potential target of SARS-COV-2 Main Protease, using Molecular Docking Analysis. From Autodock Vina and Autodock 4 results, we found out Zavegepant (as knownVazegepant), which has significant binding energy and excellent affinity against SARS-COV-2 Main pro. Indeed, it has shown a high Binding Energy score, of about -11.43 kcal mol -1, and an excellent estimation of Inhibitory constant Ki 4.19 nM.

  Key-Words / Index Term
  AutoDock Vina, PyRx, calcitonin gene-related peptide receptor (CGRPR)

  References
  [1] Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G. F., and Tan, W. “A Novel Coronavirus from Patients with Pneumonia in China, 2019”, New England Journal of Medicine , Vol. 382, Issue. 8, pp. 727–733, 2020, https://doi.org/10.1056/nejmoa2001017.
  [2] Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J., Yu, T., Zhang, X., and Zhang, L.. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, The Lancet ,Vol. 395, Issue. 10223, pp.507–513, 2020, https://doi.org/10.1016/S0140-6736(20)30211-7.
  [3] Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., Yang, X., andYang, H. “ Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors”, Nature, Vol.582, Issue.7811, pp. 289–293, 2020. https://doi.org/10.1038/s41586-020-2223-y.
  [4] Mostafa, I., Mohamed, N. H., Mohamed, B., Almeer, R., Abulmeaty, M., Bungau, S. G., El-Shazly, A. M., and Yahya, G. “ In-silico screening of naturally derived phytochemicals against SARS-CoV Main protease”, Environmental science and pollution research international, pp. 1–17, 2021. https://doi.org/10.1007/s11356-021-17642-9.
  [5] Sharma, P., Vijayan, V., Pant, P., Sharma, M., Vikram, N., Kaur, P., Singh, T. P., and Sharma, S. “ Identification of potential drug candidates to combat COVID-19: a structural study using the main protease (Mpro) of SARS-CoV-2”, Journal of biomolecular structure & dynamics, Vol.39, Issue.17, pp. 6649–6659,2021. https://doi.org/10.1080/07391102.2020.1798286.
  [6] Negro, A., & Martelletti, P. “Gepants for the treatment of migraine”, Expert opinion on investigational drugs, Vol. 28, Issue.6, pp. 555–567, 2019. https://doi.org/10.1080/13543784.2019.1618830.
  [7] Tepper D. “Gepants”, Headache, Vol.60, Issue.5, pp.1037–1039, 2020. https://doi.org/10.1111/head.13791.
  [8] Moreno-Ajona, D., Pérez-Rodríguez, A., and Goadsby, P. J. “Gepants, calcitonin-gene-related peptide receptor antagonists: what could be their role in migraine treatment?”, Current opinion in neurology, Vol.33, Issue.3, pp.309-315, 2020.
  [9] Dallakyan, S., and Olson, A. J. “Small-molecule library screening by docking with PyRx”, In Chemical biology pp. 243-250, Humana Press, New York, NY, 2015.
  [10] Valdés-Tresanco, M. S.; Valdés-Tresanco, M. E.; Valiente, P. A.; Moreno, E. AMDock: “A Versatile Graphical Tool for Assisting Molecular Docking with Autodock Vina and Autodock4”, Biology Direct, Vol. 15 , Issue.1, 2020, https://doi.org/10.1186/s13062-020-00267-2.
  [11] Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., and Ferrin, T. E. “UCSF Chimera--a visualization system for exploratory research and analysis”, Journal of computational chemistry , Vol. 25, Issue.13, pp. 1605–1612, 2004, https://doi.org/10.1002/jcc.20084 .
  [12] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., and Olson, A. J. “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility”, Journal of computational chemistry, Vol. 30, Issue.6, pp. 2785–2791, 2009. https://doi.org/10.1002/jcc.21256.
  [13] Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M. C. “SWISS-MODEL: An Automated Protein Homology-Modeling Server”, Nucleic Acids Research,Vol.31,Issue.13,pp.3381–3385,2003, https://doi.org/10.1093/nar/gkg520.
  [14] Wallace, A. C., Laskowski, R. A., & Thornton, J. M. “ LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions”, Protein engineering, design and selection, Vol. 8, Issue.2, pp. 127-134, 1995.

  Citation
  I.V. Ferrari, M. Di Mario, R. Narducci, P. Patrizio, "Gepants against SARS COV-2 Main Protease," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.90-93, 2021

• Open Access   Article

  Impact of E-Learning on Secondary School Students in Nigeria during COVID-19 Lockdown

  Soladoye Afeez Adekunle, Ojo Adeolu Opeyemi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.94-104, Dec-2021

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  Abstract
  Online Instructional Method (E-Learning) has become the only way out when the incidence of Covid-19 lockdown put all the world economic and social activities on pause, which led to a stop in academics activities too. Nigeria was not excluded and we need a way to ensure continuation in academics activities of the students, this led to the adoption of E-Learning. This is the very first time the Nigerian Education Sector fully and openly call for adoption of online classes, which made it very hard for many students to adapt easily to it, while some people have problems with digital devices to use or lack access to good internet network. This study aims to study the level of involvement and participation of secondary school students in Nigeria, the impact this E-Learning has on their studies after the resumption and their view on this method of learning. A survey of 200 secondary school students were randomly selected from four (4) Federal Unity Schools across Nigeria, Google form was used as a means to collect primary data from these students, the link to the form was shared with their teachers for them to fill it in school while some were filled by them in their ICT units/Lab. The collected primary data was analyzed using descriptive analysis, while the mean of some related questions was also analyzed using ANOVA (Analysis of variance) to check maybe there is any significant difference among them and to know maybe the respondents really maintain consistency while answering the questions. The findings of this research indicate the level of participation of the 200 randomly selected students which represent the level of participation of the secondary school students at large because all these students come from various parts of the country, it also show that the most used E-learning platform is WhatsApp and that the adoption of E-Learning really helped the students during the covid-19 Lockdown as it kept them busy and able to make good use of the long break and exposed them to the use of digital devices for Learning, while most students are ready to attend online classes if the need arises.

  Key-Words / Index Term
  Covid-19 lockdown; Online Pedagogy; Federal Unity Schools Students; Nigeria: Education Technology

  References
  [1]L. M. L. Lorente, A. A. Arrabal and C. Pulido-Montes.( 2020). “The Right to Education and ICT during COVID-19”: An International Perspective. Retrieved January 2021.
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  [3]C. Jackson; E. Vynnycky; J. Hawker; B. Olowokure; P. Mangtani. “School closures and in?uenza: Systematic review of epidemiological studies”. BMJ Open 2013, 3, e002149.
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  [20]A. Purwanto, M. Asbari, M. Fahlevi, A., E. Agistiawati, Y. Cahyono, P. Suryani. “Impact of Work from Home (WFH) on Indonesian Teachers Performance During the Covid-19 Pandemic: An Exploratory Study”. International Journal of Advanced Science and Technology, vol. 29, issue. 05,pp. 6235 – 6244, 2020.
  [21] M. Suryaman, Y. Cahyono, D. Muliansyah, O. Bustani, P. Suryani, M. Fahlevi, R. Pramono, A. Purwanto, J. T. Purba, A. P. Munthe, Juliana, S. M. Harimurti. “Covid-19 Pandemic and Home Online Learning System: Does It Affect the Quality of Pharmacy School Learning?” Systematic Review in Pharmacy. vol. 11, issue. 8:pp. 524-530, 2020
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  [24]Blundell, C., Lee, K.T., Shaun (2015). Digital learning in schools: Conceptualizing the challenges and influence on teacher practice. Journal of Information Technology Education: Research, 15, 535-56

  Citation
  Soladoye Afeez Adekunle, Ojo Adeolu Opeyemi, "Impact of E-Learning on Secondary School Students in Nigeria during COVID-19 Lockdown," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.94-104, 2021

• Open Access   Article

  A Paper on Preparation of Dataset for Handwritten Dzongkha Alphabets

  Deewas Chamling, Yeshi Jamtsho, Yonten Jamtsho

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.105-109, Dec-2021

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  Abstract
  In this paper, we present the complete methodology of preparing a dataset for handwritten Dzongkha alphabets of Bhutan to promote the development of the Handwritten Dzongkha Alphabet Recognition System (HanDARS). The dataset consists of 30 classes, each representing a character of the Dzongkha language with 500 images in each class amounting to a total of 15000 images. The images were manually collected from different individuals and were then augmented to add more varieties to the dataset. The alphabet images were converted to binary format. This dataset can be utilized as a basis for further research and development in the field of optical character recognition for the Dzongkha language. In the future, a greater number of handwritten alphabets needs to be collected to introduce variations in the dataset.

  Key-Words / Index Term
  Deep Learning; Convolutional Neural Networks; Dzongkha, Bhutanese dataset

  References
  [1] D. Chamling, Y. Jamtsho, and Y. Jamtsho, “Handwritten Dzongkha Alphabet Recognition System using Convolutional Neural Network,” Int. J. Sci. Res. Comput. Sci. Eng., Vol. 9, Issue. 5, pp. 20–24, 2021.
  [2] S. K. Dasari, S. Mehta, and D. Steffi D.D, “Optical Character Recognition of Devanagari Script Using Machine Learning- A Survey,” J. Xian Univ. Archit. Technol., Vol. 12, Issue. 8, pp. 593–599, 2020.
  [3] L. Deng, “The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web],” IEEE Signal Process. Mag., Vol. 29, Issue. 6, pp. 141–142, 2012.
  [4] W. Zhu, “Classification of MNIST Handwritten Digit Database using Neural Network,” Aust Natl Univ, p. 7, 2012.
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  [6] U. Bhattacharya, M. Shridhar, S. K. Parui, P. K. Sen, and B. B. Chaudhuri, “Offline recognition of handwritten Bangla characters: an efficient two-stage approach,” Pattern Anal Applic, Vol. 15, Issue. 4, pp. 445–458, 2012.
  [7] L. Ma, H. Liu, and J. Wu, “MRG-OHTC Database for Online Handwritten Tibetan Character Recognition,” in 2011 International Conference on Document Analysis and Recognition, pp. 207–211, 2011.
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  [9] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.

  Citation
  Deewas Chamling, Yeshi Jamtsho, Yonten Jamtsho, "A Paper on Preparation of Dataset for Handwritten Dzongkha Alphabets," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.105-109, 2021

• Open Access   Article

  Ergot Alkaloids against SARS-COV-2 Main Protease

  I.V. Ferrari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.110-114, Dec-2021

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  Abstract
  The present study based on the Docking approach it focused on Ergot alkaloids for instance Ergotamine, Dihydroergotamine, Ergocristine, and Dihydroergocristine, which they obtained excellent results of both Binding Energy, of about -11,29 kcal mol -1; -12,16 kcal mol -1; -12,03 kcal mol -1 and -12,2 kcal mol -1, respectively and in terms of estimation of inhibitory constants Ki (5,30 nM, 1,22 nM 1,52 nM, and 1, 14 nM, respectively. This has led to the conclusion, that they could be excellent candidates against SARS-COV-2 M protease, even though further in vitro and in vivo studies are needed to confirm this preliminary analysis.

  Key-Words / Index Term
  Docking analysis, Ergotamine and Dydroergotamine, M-pro

  References
  [1] Campos, D., Fulco, U. L., de Oliveira, C., and Oliveira, J. “ SARS-CoV-2 virus infection: Targets and antiviral pharmacological strategies”, Journal of evidence-based medicine, Vol. 13, Issue. 4, pp.255–260 , 2020. https://doi.org/10.1111/jebm.12414.
  [2] Huang, F., Li, Y., Leung, E. L., Liu, X., Liu, K., Wang, Q., Lan, Y., Li, X., Yu, H., Cui, L., Luo, H., and Luo, L. “ A review of therapeutic agents and Chinese herbal medicines against SARS-COV-2 (COVID-19). Pharmacological research, Vol.158, pp.104929 , 2020. https://doi.org/10.1016/j.phrs.2020.104929.
  [3] Clementi, N., Scagnolari, C., D`Amore, A., Palombi, F., Criscuolo, E., Frasca, F., Pierangeli, A., Mancini, N., Antonelli, G., Clementi, M., Carpaneto, A., andFilippini, A. “Naringenin is a powerful inhibitor of SARS-CoV-2 infection in vitro. Pharmacological research, Vol.163, pp.105255, 2021. https://doi.org/10.1016/j.phrs.2020.105255.
  [4] Beeraka, N. M., Tulimilli, S. V., Karnik, M., Sadhu, S. P., Pragada, R. R., Aliev, G., & Madhunapantula, S. “The Current Status and Challenges in the Development of Vaccines and Drugs against Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2). BioMed research international, Vol.2021, pp. 8160860, 2021. https://doi.org/10.1155/2021/8160860.
  [5] Ferreira, L. G., Dos Santos, R. N., Oliva, G., and Andricopulo, A. D. “Molecular docking and structure-based drug design strategies”, Molecules (Basel, Switzerland), Vol.20, Issue.7 , pp. 13384–13421, 2015. https://doi.org/10.3390/molecules200713384.
  [6] Lohning, A. E., Levonis, S. M., Williams-Noonan, B., and Schweiker, S. S. “A Practical Guide to Molecular Docking and Homology Modelling for Medicinal Chemists”, Current topics in medicinal chemistry, Vol.17, Issue.18 , pp. 2023–2040, 2017. https://doi.org/10.2174/1568026617666170130110827.
  [7] Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., and Ferrin, T. E. “ UCSF Chimera--a visualization system for exploratory research and analysis”, Journal of computational chemistry, Vol. 25, Issue.13 , pp. 1605–1612, 2004. https://doi.org/10.1002/jcc.20084.
  [8] Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., and Moreno, E. “ AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4”, Biology direct, Vol.15, Issue. 1, pp. 12, 2020. https://doi.org/10.1186/s13062-020-00267-2.
  [9] Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., and Olson, A. J. “ Computational protein-ligand docking and virtual drug screening with the AutoDock suite”, Nature protocols, Vol.11, Issue.5, pp.905–919, 2016. https://doi.org/10.1038/nprot.2016.051.
  [10] Silberstein, S. D., and McCrory, D. C. “ Ergotamine and dihydroergotamine: history, pharmacology, and efficacy”, Headache: The Journal of Head and Face Pain, Vol.43, Issue.2, pp.144-166, 2003.
  [11] Lei, X., Yu, J., Niu, Q., Liu, J., Fraering, P. C., and Wu, F. “ The FDA-approved natural product dihydroergocristine reduces the production of the Alzheimer’s disease amyloid-? peptides”, Scientific reports, Vol.5 Issue.1, pp.1-11, 2015.
  Roquebert, J., and Demichel, P. “ Agonist/antagonist activity of ergocristine at alpha-adrenoceptors in the rat”,Fundamental & clinical pharmacology, Vol.1, Issue.1, 23-33, 1987.

  Citation
  I.V. Ferrari, "Ergot Alkaloids against SARS-COV-2 Main Protease," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.110-114, 2021

• Open Access   Article

  Find me: A User Friendly Key Logger Tool for Insider Threat Identification

  J. Dhiviya Rose, Vishal Kumar, Amit Singh Khinchee, Tushar Rathee, Keerthivardhan A.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.115-120, Dec-2021

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  Abstract
  Insider Threat Detection has become one of the lime lights that are a widely accepted issue that reflects a growing concern in security communities. Insider Threats are listed as the malicious activities performed by authorized users in any organization resulting in various damages like intellectual property loss and breach of security details. The increasing insider threads have made the organizations turn their focus to various ‘malicious insiders’: the own people who work not in favor of the organization`s growth. Moreover, an insider, additionally viewed as a worker of an organization, turns into a threat when the aim or activity, intention can influence the organization contrarily. The insider threat has been a famous issue in associations that have brought about the deficiency of trust, secret information, and data. Although many insider attacks have more privilege and legitimate access to information in comparison to external attacks which can cause much damage to the organization, the internal threats can cause extensive damage. This project aims to offer a tool that helps in Insider Threat Detection by detecting similar data of the database using java. The Keylogger detects the abnormal behavior of the user using correlating the data movement as a keylogger installed in all systems organizations for monitoring the keystrokes activities of the employee system. After matching the Keystrokes with sensitive keywords, the system generates an alert log to warn relevant IT cells of the organization when the user accesses extraordinary, confidential data after comparing its system database.

  Key-Words / Index Term
  Insider Attack; Malicious insiders; Authorized users; Keylogger; Insider Threat Detection

  References
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  [3] S. Johannessen Berdal, “A Holistic Approach to Insider Threat Detection,” p. 117, 2018.
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  [6] P. M. Patel and P. V. K. Shah, “Analysis and Implementation of Decipherments ofKeyLogger,” vol. 5, no. 1, p. 53, 2015, [Online]. Available: http://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/special_file.php?val=January_2015_1422602239__44.pdf.
  [7] M. K. Shah, D. Kataria, S. B. Raj, and G. Priya, “Real Time Working of Keylogger Malware Analysis,” vol. 9, no. 10, pp. 569–573, 2020.
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  [13] Insider Threats Global Report, “2020 Cost of Insider Threats,” Available online https//www.observeit.com/costof- Insid., p. 31, 2020, [Online]. Available: https://www.observeit.com/wp-content/uploads/2020/04/2020-Global-Cost-of-Insider-Threats-Ponemon-Report_UTD.pdf.
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  Citation
  J. Dhiviya Rose, Vishal Kumar, Amit Singh Khinchee, Tushar Rathee, Keerthivardhan A., "Find me: A User Friendly Key Logger Tool for Insider Threat Identification," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.115-120, 2021

• Open Access   Article

  Computational analysis of antiviral drugs against South African (B.1.351) SARS-CoV-2 spike protein variant

  I.V. Ferrari, M. Di Mario

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.121-128, Dec-2021

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  Abstract
  The present work is intended to study, through the “Molecular Docking” approach, about 100 antiviral drugs, against the South African (B.1.351) SARS-CoV-2 spike protein variant. The analysis was performed by Autodock Vina, in which we scan the active area of three key amino acids belonging to the Spike Protein RBD, responsible for a higher binding with the ACE2 receptor. They are ASN 417; Lys 484, and Tyr 501 respectively. From our results, we have found out five potential inhibitor drugs of the hepatitis C virus (HCV ), which have been shown a good Binding Energy score of -10 kcal mol-1. They are Furaprevir, Simeprevir, Odalasvir, and Paritaprevir respectively. In addition, their best-docked pose can bind to two of the three investigated residues of the Spike-RBD, which are N 417 and Y 501. As known, these last residues have been found in other Coronavirus variants as the most recent case of the Omicron one.

  Key-Words / Index Term
  Docking; SARS-CoV-2 spike protein variant

  References
  [1] Weisblum, Y., Schmidt, F., Zhang, F., DaSilva, J., Poston, D., Lorenzi, J. C., and Bieniasz, P. D. “ Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants”,Elife, Vol. 9, pp. e61312, 2020.
  [2] Priesemann, V., Balling, R., Brinkmann, M. M., Ciesek, S., Czypionka, T., Eckerle, I., and Szczurek, E. “ An action plan for pan-European defence against new SARS-CoV-2 variants”, The Lancet, Vol.397, Issue.10273, pp.469-470, 2021.
  [3] Harvey, W. T., Carabelli, A. M., Jackson, B., Gupta, R. K., Thomson, E. C., Harrison, E. M., Ludden, C., Reeve, R., Rambaut, A., “COVID-19 Genomics UK (COG-UK) Consortium, Peacock, S. J., and Robertson, D. L. “ SARS-CoV-2 variants, spike mutations and immune escape”, Nature reviews. Microbiology, Vol.19, Issue.7, pp. 409–424, 2021. https://doi.org/10.1038/s41579-021-00573-0.
  [4] Harrington, D., Kele, B., Pereira, S., Couto-Parada, X., Riddell, A., Forbes, S., Dobbie, H., and Cutino-Moguel, T. “Confirmed Reinfection With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variant VOC-202012/01,” Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, Vol. 73, Issue.10, pp. 1946–1947, 2021. https://doi.org/10.1093/cid/ciab014.
  [5] Hill, K. J., Dewar, R., and Templeton, K. “ A Multiregional Evaluation of Cycle Threshold Values in Severe Acute Respiratory Syndrome Coronavirus 2 VOC-20DEC-01 Variant”, The Journal of infectious diseases, Vol.224, Issue.5, pp.927–928, 2021. https://doi.org/10.1093/infdis/jiab303.
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  [8] Resende, P. C., Gräf, T., Paixão, A., Appolinario, L., Lopes, R. S., Mendonça, A., da Rocha, A., Motta, F. C., Neto, L., Khouri, R., de Oliveira, C. I., Santos-Muccillo, P., Bezerra, J. F., Teixeira, D., Riediger, I., Debur, M., Ribeiro-Rodrigues, R., Leite, A. B., do Santos, C. A., Gregianini, T. S. and Siqueira, M. M. “ A Potential SARS-CoV-2 Variant of Interest (VOI) Harboring Mutation E484K in the Spike Protein Was Identified within Lineage B.1.1.33 Circulating in Brazil”, Viruses, Vol 13, Issue.5, 724,2021.https://doi.org/10.3390/v13050724.
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  Citation
  I.V. Ferrari, M. Di Mario, "Computational analysis of antiviral drugs against South African (B.1.351) SARS-CoV-2 spike protein variant," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.121-128, 2021