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• Open Access   Article

  Enabling Delay Tolerance in IoT based Vehicular Networks

  Aditya Sharma, Akash Maan

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.1-7, Feb-2022

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  Abstract
  The essence of IoT devices in the networks involving the management of Smart Traffic can be seen in urban cities. The optimal movement of the vehicles, pollution monitoring and control, efficient utilization of the resources, and ensuring security while vehicular communications are some of the key issues related to Smart Traffic Management. This paper aims to keep track of the current Delay Tolerant Network (DTN) application in IoT based Vehicular Networks. Vehicular Delay Tolerant Network (VDTN) is one of the most important IoT applications. Delay Tolerant Networks (DTNs) are introduced to reduce latency and to counter the data loss and data theft conditions that were present in Wireless Ad Hoc networks (WANET). Connectivity, transmission, and security issues in Wireless Ad Hoc networks if used in Vehicular Networks can result in serious losses as the presence of small loopholes in the network can lead to major accidents. This paper aims to provide general insights into the DTN architecture and routing protocols. Applications of Delay Tolerant Network in Vehicular networks as Vehicular Delay Tolerant Networks (VDTNs) along with the routing issues and challenges related to the VDTNs are also presented as a part of this comprehensive work.

  Key-Words / Index Term
  IoT, DTN, Vehicular Networks, VDTN, Routing Protocols

  References
  [1] K. Fall, “A delay-tolerant network architecture for challenged internets,” 2003.
  [2] F. Z. Benhamida, A. Bouabdellah, and Y. Challal, “Using delay tolerant network for the Internet of Things: Opportunities and challenges,” Apr. 2017.
  [3] P. Romano, P. Schrotter, O. Koudelka, and M. Wittig, “Developments towards an Interplanetary Internet,” Sep. 2009.
  [4] P. Kumar and R. Kamble, “A Deep Study of Hybrid Trust Built To Improve Security Technique against Sybil Attack in MANET Based IoT Network,” 2021.
  [5] S. Ali, J. Qadir, and A. Baig, “Routing protocols in Delay Tolerant Networks - a survey,” Oct. 2010.
  [6] T. H. Silva et al., “Users in the urban sensing process,” in Pervasive Computing, Elsevier, 2016.
  [7] D. B. Rawat, C. Bajracharya, and G. Yan, “Towards intelligent transportation Cyber-Physical Systems: Real-time computing and communications perspectives,” Apr. 2015.
  [8] D. B. Rawat and C. Bajracharya, Vehicular Cyber Physical Systems. Cham: Springer International Publishing, 2017.
  [9] M. C. G. Paula, J. N. Isento, J. A. Dias, and J. J. P. C. Rodrigues, “A real-world VDTN testbed for advanced vehicular services and applications,” Jun. 2011.
  [10] P. Godha, S. Jadon, A. Patle, I. Gupta, B. Sharma, and A. Kumar Singh, “Architecture, an Efficient Routing, Applications, and Challenges in Delay Tolerant Network,” May 2019.
  [11] R. S. Mangrulkar and M. Atique, “Routing protocol for Delay Tolerant Network: A survey and comparison,” Oct. 2010.
  [12] K. Kaur and R. Kaur, “A Literature Survey on Security & Privacy Issues in IoT,” 2021.
  [13] H. Kang, S. H. Ahmed, D. Kim, and Y.-S. Chung, “Routing Protocols for Vehicular Delay Tolerant Networks: A Survey,” International Journal of Distributed Sensor Networks, vol. 11, no. 3, Mar. 2015.
  [14] H. Li, Y. Liu, Z. Qin, H. Rong, and Q. Liu, “A Large-Scale Urban Vehicular Network Framework for IoT in Smart Cities,” IEEE Access, vol. 7, 2019.
  [15] C. Giannini, P. Calegari, C. Buratti, and R. Verdone, “Delay Tolerant Network for smart city: Exploiting bus mobility,” 2016.
  [16] C. C. Sobin, V. Raychoudhury, and S. Saha, “An Energy-efficient and Buffer-aware Routing Protocol for Opportunistic Smart Traffic Management,” Jan. 2017.
  [17] Y. Zguira, H. Rivano, and A. Meddeb, “IoB-DTN: A lightweight DTN protocol for mobile IoT applications to smart bike sharing systems,” Apr. 2018.
  [18] P.-C. Cheng, K. C. Lee, M. Gerla, and J. Härri, “GeoDTN+Nav: Geographic DTN Routing with Navigator Prediction for Urban Vehicular Environments,” Mobile Networks and Applications, vol. 15, no. 1, Feb. 2010.
  [19] Y. Guo, S. Schildt, T. Pogel, and L. Wolf, “Detecting malicious behavior in a vehicular DTN for public transportation,” Oct. 2013.
  [20] V. Singh and G. L. Saini, “DTN-Enabled Routing Protocols and Their Potential Influence on Vehicular Ad Hoc Networks,” 2018.
  [21] V. N. G. J. Soares, F. Farahmand, and J. J. P. C. Rodrigues, “Improving Vehicular Delay-Tolerant Network Performance with Relay Nodes,” Jul. 2009.
  [22] R. Rushikesh and C. M. R. Sivappagari, “Development of IoT based vehicular pollution monitoring system,” Oct. 2015.
  [23] G. Rathee, N. Jaglan, R. Iqbal, S. P. Lal, and V. G. Menon, “A trust analysis scheme for vehicular networks within IoT-oriented Green City,” Environmental Technology & Innovation, vol. 20, Nov. 2020.
  [24] Y. Teranishi, T. Kimata, E. Kawai, and H. Harai, “Hybrid Cellular-DTN for Vehicle Volume Data Collection in Rural Areas,” Jul. 2019.
  [25] M. Li, P. Si, and Y. Zhang, “Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks With Mobile Edge Computing in Smart City,” IEEE Transactions on Vehicular Technology, vol. 67, no. 10, Oct. 2018.
  [26] H. Liang, W. Gao, J. H. Nguyen, M. F. Orpilla, and W. Yu, “Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments,” IEEE Access, vol. 8, 2020.
  [27] S. H. Ahmed, Hyunwoo Kang, and Dongkyun Kim, “Vehicular Delay Tolerant Network (VDTN): Routing perspectives,” Jan. 2015.
  [28] J. A. Dias et al., “WWW@VDTN - A Web browsing application for Vehicular Delay-Tolerant Networks,” Jun. 2011.
  [29] K. Fall and S. Farrell, “DTN: an architectural retrospective,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 5, Jun. 2008.
  [30] S. Guennouni, S. Loudari, and N. Benamar, “A new algorithm for implementing non-cooperation in DTN nodes,” Jun. 2016.
  [31] N. Chaubey and P. Mistri, “Routing Protocols in Delay Tolerant Network (DTN): A Critical Study and Comparison,” International Journal in IT and Engineering(IJITE), vol. 4, pp. 8–18, May 2016.
  [32] Y.-P. Kim, J.-I. Koo, E. Jung, K. Nakano, M. Sengoku, and Y.-J. Park, “Composite methods for improving Spray and Wait routing protocol in Delay Tolerant Networks,” Oct. 2010.
  [33] E. Spaho, L. Barolli, V. Kolici, and A. Lala, “Performance Comparison of Different Routing Protocols in Sparse and Dense VDTNs,” Mar. 2016.
  [34] N. Selvakumar, M. Rohini, C. Narmada, and M. Yogeshprabhu, “Network Traffic Control Using AI,” 2020.
  [35] Y. Park, C. Sur, and K.-H. Rhee, “A Secure Incentive Scheme for Vehicular Delay Tolerant Networks Using Cryptocurrency,” Security and Communication Networks, vol. 2018, Jul. 2018.
  [36] F. Tang, Y. Kawamoto, N. Kato, and J. Liu, “Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches,” Proceedings of the IEEE, vol. 108, no. 2, Feb. 2020.
  [37] P. Senthilkumar and B. Subramani, “Study on IoT Architecture, Application Protocol and Energy needs,” 2020.
  

  Citation
  Aditya Sharma, Akash Maan, "Enabling Delay Tolerance in IoT based Vehicular Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.1-7, 2022

• Open Access   Article

  Design and Development of a Dynamic Hashing Algorithm

  Nasir Mahmud Apar, MD. Minhazul Islam Munna, Ahsan Ullah, Habibur Rahman

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.8-14, Feb-2022

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  Abstract
  The world is in front of a huge digital revolution. Pragmatically 59% people of the world population are now using the internet for their daily activities related to business, medical, industries, education. It’s a very big challenge to maintain the integrity of the shared data. Those issues can be solved by using a cryptographic technique called hashing. This paper demonstrates a new smooth cryptographic hashing algorithm based on Merkle–Damgård construction structure and Matyas-Meyer-Oseas compression function which generates hashes through 16 rounds where each block contains 256 bits of data. These blocks of data are processed through some existing and newly developed methods like bit scheduler, switching box, shift in column, shift in row, and bit mixing. This proposed algorithm works block-wise and can generate same level of hashes as SHA-512 and MD5. But it uses less time and resources than SHA-512 and MD5 for processing alphabet and numeric data.

  Key-Words / Index Term
  Hash, Hashing Algorithm, Bit Mixing, Shift in Row, Shift in Column, Data Integrity, SHA – 512, MD5

  References
  [1] Marc Stevens, Elie Bursztein, Pierre Karpman, “The ?rst collision for full SHA-1”, Annual International Cryptology Conference, 2017
  [2] Yuliang Zheng, Josef Pieprzyk, Jennifer Seberry, “HAVAL — A one-way hashing algorithm with variable length of output”, Advances in Cryptology — AUSCRYPT `92, 1993, Volume 718, ISBN : 978-3-540-57220-6
  [3] Kwok HS, Tang WKS, “A chaos-based cryptographic Hash function for message authentication.”, Int J Bifurcat Chaos 2005; 15:4043–50
  [4] Jean-S´ebastien Coron, Yevgeniy Dodis, C´ecile Malinaud, and Prashant Puniya, “Merkle-Damg?ard Revisited : how to Construct a Hash Function”, 2015

  Citation
  Nasir Mahmud Apar, MD. Minhazul Islam Munna, Ahsan Ullah, Habibur Rahman, "Design and Development of a Dynamic Hashing Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.8-14, 2022

• Open Access   Article

  E – Authentication for Secure Net Banking

  Mitul Chauhan, Gayatri Barapatre, Amruta Ghatge, Rajashri Sabale, Pooja Sakunde

  Survey Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.15-18, Feb-2022

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  Recent technological advances have resulted in a plethora of personal computing devices such as smartphones, tablets, watches, and eyewear, among others. This has helped them realize a digital world in which vital everyday tasks may be completed via the Internet from any location, at any time, and on any device. At the same time, improvements in pervasive computing technology have given birth to the notion of smart spaces, which aims to provide customized services to inhabitant in an automated manner. User authentication, or verifying a user`s identity, is critical in the digital world to safeguard personal data kept online (e.g., online bank accounts) and on personal devices (e.g., smart phones), as well as to enable tailored services in smart spaces (e.g., adjusting room temperature etc.). Traditional authentication methods (such as passwords or fingerprints) have recently been proved to be vulnerable to hacking. As a result, researchers have developed a slew of novel ways for authenticating users in the aforementioned scenarios. This study provides an overview of these unique systems in order to help guide future research in these areas.

  Key-Words / Index Term
  Online Banking Authentication (OBA), Linear Regression, Classification, Machine Learning etc.

  References
  [1] SYED W. SHAH AND SALIL S. KANHERE, “Recent Trends in User Authentication – A Survey.” Institute of Electrical and Electronics Engineers (IEEE), Vol 7, Issue. , pp.112505 – 112519, 2019.
  [2] WENZHENG LIU, XIAOFENG WANG, WEIPENG, “State of the Art: Secure Mobile Payment.” Institute of Electrical and Electronics Engineers (IEEE), Vol 8, Issue. , pp. 13898 - 13914, 2020.
  [3] Anum Tanveer Kiyani, Aboubaker Lasebae, Kamran Ali1 and Masood Ur-Rehman “Secure Online Banking with Biometrics”. In the Proceedings of the 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), pp. 1-6, 2020.
  [4] Divyans Mahansaria, Uttam Kumar Roy, “Secure Authentication for ATM transactions using NFC technology.” In the Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), 2019.
  [5] Wen-Hui Lin, Bao-Hua Wu, Qian-Hua Huang, “A Face-Recognition Approach based on Secret Sharing for User Authentication in Public-Transportation Security.” In the Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), 2018.
  [6] N. Clarke and S. Furnell “Authentication of users on mobile telephones – A survey of attitudes and practices.” In the Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), 2018.
  [7] Andrew Bissada, Aspen Olmsted “Mobile Multi-Factor Authentication”, In the Proceedings of the 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), 2017.
  [8] Kavitha K, Saravanan, “SURVIVAL STUDY ON DATA STORAGE AND TRANSACTION SECURITY USING AUTHENTICATION TECHNIQUES”, International Journal of Computer Sciences and Engineering (IJCSE), Vol-6, Issue-8, pp.34-39, 2018.
  [9] Anil Lamba, “A Survey of various machine learning techniques used in Intrusion Detection System”, International Journal of Computer Sciences and Engineering (IJCSE), Vol-7, Issue-5, pp.557-563, 2019.

  Citation
  Mitul Chauhan, Gayatri Barapatre, Amruta Ghatge, Rajashri Sabale, Pooja Sakunde, "E – Authentication for Secure Net Banking," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.15-18, 2022

• Open Access   Article

  SARS-COV-2 Proteins, in Complex with Tirilazad

  I.V. Ferrari, M. Di Mario

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.19-25, Feb-2022

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  In this study, our approach was to carry out a complete investigation of molecular docking analysis with the major SARS-CoV-2 proteins. We analysed more than 6000 drugs, downloaded by the PubChem database. Particular attention, we have focused on Spike Glycoprotein and Main protease 3CLpro Covid-19 proteins, by “Blind docking” method and “Selective docking” procedure, in Ligand Binding site, with AutoDock Vina using Pyrx software. From our results, we have selected Tirilazad against COVID-19. In fact, it reported having an excellent ability to bind both with the “Native Spike Glycoprotein”, with a Binding Energy of -11.8 kcal mol-1, and with the South African (B.1.351) SARS-CoV-2 spike protein variant, with a Binding Energy -10 kcal mol-1. Indeed, in the second case, the docking analysis was evaluated in 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. In addition, Tirilazad has shown a Binding energy score of approximately -10.5 kcal mol-1 against SARS-COV-2 Main protease. This has led us to conclude that this drug could be an excellent candidate against Coronavirus (COVID-19) pandemic, even though further in vitro and in vivo studies are needed.

  Key-Words / Index Term
  Tirilazad; SARS-COV-2

  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.; Tan, and 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] Nobel, Y. R.; Phipps, M.; Zucker, J.; Lebwohl, B.; Wang, T. C.; Sobieszczyk, M. E.; and Freedberg, D. E. “Gastrointestinal Symptoms and Coronavirus Disease 2019: A Case-Control Study From the United States”, Gastroenterology , Vol.159, Issue.1, pp.373-375, 2020, https://doi.org/10.1053/j.gastro.2020.04.017.
  [4] Gautier, J. F.; and Ravussin, Y. “A New Symptom of COVID-19: Loss of Taste and Smell. Obesity”, Blackwell Publishing Inc. Vol.28, Issue.5, pp.848, 2020, https://doi.org/10.1002/oby.22809.
  [5] Cao, X. “COVID-19: Immunopathology and Its Implications for Therapy”, Nature Reviews Immunology, Vol. 20, Issue.5, pp. 269–270, 2020, https://doi.org/10.1038/s41577-020-0308-3.
  [6] Basile, C.; Combe, C.; Pizzarelli, F.; Covic, A.; Davenport, A.; Kanbay, M.; Kirmizis, Di.; Schneditz, D.; van der Sande, F.; and Mitra, S. “Recommendations for the Prevention, Mitigation and Containment of the Emerging SARS-CoV-2 (COVID-19) Pandemic in Haemodialysis Centres” Nephrology Dialysis Transplantation , Vol.35, Issue.5, pp.737–741. https://doi.org/10.1093/ndt/gfaa069.
  [7] Sabetkish, N.; and Rahmani, A.” The Overall Impact of COVID ?19 on Healthcare during the Pandemic: A Multidisciplinary Point of View “Health Science Reports, Vol.4, Issue.4, pp.e386,2021, https://doi.org/10.1002/hsr2.386.
  [8] González, J. M.; Gomez-Puertas, P.; Cavanagh, D.; Gorbalenya, A. E.; and Enjuanes, L. “A Comparative Sequence Analysis to Revise the Current Taxonomy of the Family Coronaviridae,” Archives of Virology, Vol.148, Issue.11, pp.2207–2235, 2003, https://doi.org/10.1007/s00705-003-0162-1.
  [9] Zhu, M. “SARS Immunity and Vaccination, Cell Mol Immunol, Vol. 1, Issue. 3, pp.193-8, 2004.
  [10] Kandeel, M.; Ibrahim, A.; Fayez, M.; and Al-Nazawi, M. “From SARS and MERS CoVs to SARS-CoV-2: Moving toward More Biased Codon Usage in Viral Structural and Nonstructural Genes,” Journal of Medical Virology , Vol.92, Issue 6, pp. 660–666, 2020, https://doi.org/10.1002/jmv.25754.
  [11] Azhar, E. I.; El-Kafrawy, S. A.; Farraj, S. A.; Hassan, A. M.; Al-Saeed, M. S.; Hashem, A. M.; and Madani, T. A. “Evidence for Camel-to-Human Transmission of MERS Coronavirus”, New England Journal of Medicine, Vol.370, Issue 26, pp.2499–2505,2014, https://doi.org/10.1056/nejmoa1401505.
  [12] Ita, K. “Coronavirus Disease (COVID-19): Current Status and Prospects for Drug and Vaccine Development,” Archives of Medical Research. Elsevier Inc, Vol.52, Issue. 1, pp.15–24, 2021,https://doi.org/10.1016/j.arcmed.2020.09.010.
  [13] Gil, C.; Ginex, T.; Maestro, I.; Nozal, V.; Barrado-Gil, L.; Cuesta-Geijo, M. Á.; Urquiza, J.; Ramírez, D.; Alonso, C.; Campillo, N. E.; Martinez, A. COVID-19: Drug Targets and Potential Treatments. Journal of Medicinal Chemistry,” American Chemical Society, Vol.63, Issue 21, pp.12359–12386, 2020, https://doi.org/10.1021/acs.jmedchem.0c00606.
  [14] Tahir ul Qamar, M.; Alqahtani, S. M.; Alamri, M. A.; and Chen, L. L. “Structural Basis of SARS-CoV-2 3CLpro and Anti-COVID-19 Drug Discovery from Medicinal Plants, Journal of Pharmaceutical Analysis , Vol. 10, Issue. 4, pp. 313–319, 2020, https://doi.org/10.1016/j.jpha.2020.03.009.
  [15] Mody, V.; Ho, J.; Wills, S.; Mawri, A.; Lawson, L.; Ebert, M. C. C. J. C.; Fortin, G. M.; Rayalam, and S.; Taval, S.” Identification of 3-Chymotrypsin like Protease (3CLPro) Inhibitors as Potential Anti-SARS-CoV-2 Agents, Communications Biology , Vol.4, Issue.1,pp.1-10, 2021, https://doi.org/10.1038/s42003-020-01577-x.
  [16] Beigel, J. H.; Tomashek, K. M.; Dodd, L. E.; Mehta, A. K.; Zingman, B. S.; Kalil, A. C.; Hohmann, E.; Chu, H. Y.; Luetkemeyer, A.; Kline, S.; Lopez de Castilla, D.; Finberg, R. W.; Dierberg, K.; Tapson, V.; Hsieh, L.; Patterson, T. F.; Paredes, R.; Sweeney, D. A.; Short, W. R.; Touloumi, G.; Lye, D. C.; Ohmagari, N.; Oh, M.; Ruiz-Palacios, G. M.; Benfield, T.; Fätkenheuer, G.; Kortepeter, M. G.; Atmar, R. L.; Creech, C. B.; Lundgren, J.; Babiker, A. G.; Pett, S.; Neaton, J. D.; Burgess, T. H.; Bonnett, T.; Green, M.; Makowski, M.; Osinusi, A.; Nayak, S.; and Lane, H. C. “Remdesivir for the Treatment of Covid-19”, New England Journal of Medicine ,Vol.383, Issue.19, pp. 1813–1826, 2020, https://doi.org/10.1056/nejmoa2007764.
  [17] Boulware, D. R.; Pullen, M. F.; Bangdiwala, A. S.; Pastick, K. A.; Lofgren, S. M.; Okafor, E. C.; Skipper, C. P.; Nascene, A. A.; Nicol, M. R.; Abassi, M.; Engen, N. W.; Cheng, M. P.; LaBar, D.; Lother, S. A.; MacKenzie, L. J.; Drobot, G.; Marten, N.; Zarychanski, R.; Kelly, L. E.; Schwartz, I. S.; McDonald, E. G.; Rajasingham, R.; Lee, T. C.; and Hullsiek, K. H. “A Randomized Trial of Hydroxychloroquine as Postexposure Prophylaxis for Covid-19,” New England Journal of Medicine , Vol.383, Issue.6, pp. 517–525, 2020, https://doi.org/10.1056/nejmoa2016638.
  [18] Maskin, L. P.; Olarte, G. L.; Palizas, F.; Velo, A. E.; Lurbet, M. F.; Bonelli, I.; Baredes, N. D.; and Rodríguez, P. O. “High Dose Dexamethasone Treatment for Acute Respiratory Distress Syndrome Secondary to COVID-19: A Structured Summary of a Study Protocol for a Randomised Controlled Trial, Trials , Vol.21, Issue.1, pp.1-3,2020https://doi.org/10.1186/s13063-020-04646-y.
  [19] Morales-Ortega, A.; Bernal-Bello, D.; Llarena-Barroso, C.; Frutos-Pérez, B.; Duarte-Millán, M. Á.; García de Viedma-García, V.; and Farfán-Sedano, A. I.; Canalejo-Castrillero, E.; Ruiz-Giardín, J. M.; Ruiz-Ruiz, J.; San Martín-López, J. V. Imatinib for COVID-19: A Case Report. Clinical immunology (Orlando, Fla.) , Vol. 218, pp.108518,2020, https://doi.org/10.1016/j.clim.2020.108518.
  [20] Musarrat, F.; Chouljenko, V.; Dahal, A.; Nabi, R.; Chouljenko, T.; Jois, S. D.; and Kousoulas, K. G. “The Anti-HIV Drug Nelfinavir Mesylate (Viracept) Is a Potent Inhibitor of Cell Fusion Caused by the SARSCoV-2 Spike (S) Glycoprotein Warranting Further Evaluation as an Antiviral against COVID-19 Infections,” Journal of Medical Virology, Vol.92, Issue.10, pp. 2087–2095, 2020, https://doi.org/10.1002/jmv.25985.
  [21] Thachil, J. “The Versatile Heparin in COVID-19”, Journal of Thrombosis and Haemostasis. Blackwell Publishing Ltd, pp 1020–1022, 2020, https://doi.org/10.1111/jth.14821.
  [22] Boretti, A.” Favipiravir Use for SARS CoV-2 Infection,” Pharmacological Reports , Vol.72, Issue.6, pp. 1542–1552, 2020, https://doi.org/10.1007/s43440-020-00175-2 .
  [23] Zhu, Z.; Lu, Z.; Xu, T.; Chen, C.; Yang, G.; Zha, T.; Lu, J.; and Xue, Y. “Arbidol Monotherapy Is Superior to Lopinavir/Ritonavir in Treating COVID-19,” Journal of Infection , Vol. 81, Issue.1 , pp. e21–e23,2020, https://doi.org/10.1016/j.jinf.2020.03.060.
  [24] Hippensteel, J. A.; Lariviere, W. B.; Colbert, J. F.; Langouët-Astrié, C. J.; and Schmidt, E. P. “Heparin as a Therapy for COVID-19: Current Evidence and Future Possibilities,” Am J Physiol Lung Cell Mol Physiol , Vol.319, pp. 211–217, 2020, https://doi.org/10.1152/ajplung.00199.2020.-Coronavirus.
  [25] Cao, B.; Wang, Y.; Wen, D.; Liu, W.; Wang, J.; Fan, G.; Ruan, L.; Song, B.; Cai, Y.; Wei, M.; Li, X.; Xia, J.; Chen, N.; Xiang, J.; Yu, T.; Bai, T.; Xie, X.; Zhang, L.; Li, C.; Yuan, Y.; Chen, H.; Li, H.; Huang, H.; Tu, S.; Gong, F.; Liu, Y.; Wei, Y.; Dong, C.; Zhou, F.; Gu, X.; Xu, J.; Liu, Z.; Zhang, Y.; Li, H.; Shang, L.; Wang, K.; Li, K.; Zhou, X.; Dong, X.; Qu, Z.; Lu, S.; Hu, X.; Ruan, S.; Luo, S.; Wu, J.; Peng, L.; Cheng, F.; Pan, L.; Zou, J.; Jia, C.; Wang, J.; Liu, X.; Wang, S.; Wu, X.; Ge, Q.; He, J.; Zhan, H.; Qiu, F.; Guo, L.; Huang, C.; Jaki, T.; Hayden, F. G.; Horby, P. W.; Zhang, D.; and Wang, C. “A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19,” New England Journal of Medicine , Vol. 382, Issue.19, pp.1787–1799, 2020, https://doi.org/10.1056/nejmoa2001282.
  [26] Wegner, J. K., Sterling, A., Guha, R., Bender, A., Faulon, J. L., Hastings, J. and Willighagen, E. “Cheminformatics”, Communications of the ACM, Vol. 55, Issue.11, pp.65-75, 2012.
  [27] 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.
  [28] 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.
  [29] 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.
  [30] Jejurikar, B. L., and Rohane, S. H.” Drug Designing in Discovery Studio”, Asian Journal of Research in Chemistry, Vol. 14, Issue.2, pp.135-138, 2021.
  [31] Wallace, A. C., Laskowski, R. A., AND 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.
  [32] Bracken, M. B., Shepard, M. J., Holford, T. R., Leo-Summers, L., Aldrich, E. F., Fazl, M., and Young, W. “Methylprednisolone or tirilazad mesylate administration after acute spinal cord injury: 1-year follow up: results of the third National Acute Spinal Cord Injury randomized controlled trial”, Journal of neurosurgery, Vol.89, Issue.5, pp.699-706,1998.
  [33] Schmid-Elsaesser, R., Zausinger, S., Hungerhuber, E., Baethmann, A., and Reulen, H. J. “ Neuroprotective effects of combination therapy with tirilazad and magnesium in rats subjected to reversible focal cerebral ischemia. Neurosurgery, Vol. 44, Issue.1, pp.163-171,1998.

  Citation
  I.V. Ferrari, M. Di Mario, "SARS-COV-2 Proteins, in Complex with Tirilazad," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.19-25, 2022

• Open Access   Article

  Evolutionary Training of Binary Neural Networks by Differential Evolution

  Hidehiko Okada

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.26-31, Feb-2022

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  Abstract
  A problem with deep neural networks is that the memory size for recording a trained model becomes large. A solution to this problem is to make the parameter values binary. A challenge for the binary neural networks is that they cannot be trained by the ordinary gradient-based optimization methods. The author previously applied Evolution Strategy (ES) and Genetic Algorithm (GA) to the training of binary neural networks and evaluates its ability. In this paper, the author applies Differential Evolution, another instance of evolutionary algorithms, and compares DE with ES and GA. The experimental results with a classification task revealed that DE could also optimize binary weights well so that the trained model accurately classified both trained and untrained data. Classification accuracies for training data were significantly better by DE than those by ES and GA, which revealed better ability of DE in training binary neural networks.

  Key-Words / Index Term
  Evolutionary algorithm; Differential evolution; Neural network; Network quantization; Neuroevolution

  References
  [1] G. E Hinton, R. R. Salakhutdinov, “Reducing the Dimensionality of Data with Neural Networks,” Science, Vol.313, Issue.5786, pp.504-507, 2006.
  [2] G. E. Hinton, S. Osindero, Y. W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol.18, No.7, pp.1527-1554, 2006.
  [3] Y. L. Boureau, Y. L. Cun, “Sparse Feature Learning for Deep Belief Networks,” Advances in Neural Information Processing Systems, pp.1185-1192, 2008.
  [4] I. Sutskever, G. E. Hinton, “Deep, Narrow Sigmoid Belief Networks are Universal Approximators,” Neural Computation, Vol.20, No.11, pp.2629-2636, 2008.
  [5] Y. Bengio, “Learning Deep Architectures for AI,” Foundations and Trends in Machine Learning, Vol.2, No.1, pp.1-127, 2009.
  [6] H. Larochelle, Y. Bengio, J. Louradour, P. Lamblin, “Exploring Strategies for Training Deep Neural Networks,” Journal of Machine Learning Research, Vol.10(Jan), pp.1-40, 2009.
  [7] X. Glorot, Y. Bengio, “Understanding the Difficulty of Training Deep Feedforward Neural Networks,” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Vol.9, pp.249-256, 2010.
  [8] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P. A. Manzagol, “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion,” Journal of Machine Learning Research, Vol.11(Dec), pp.3371-3408, 2010.
  [9] R. Salakhutdinov, G. Hinton, “An Efficient Learning Procedure for Deep Boltzmann Machines,” Neural Computation, Vol.24, No.8, pp.1967-2006, 2012.
  [10] A. Krizhevsky, I. Sutskever, G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, Vol.25, pp.1097-1105, 2012.
  [11] A. Graves, A. Mohamed, G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks”. IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6645-6649, 2013.
  [12] Y. Bengio, A. Courville, P. Vincent, “Representation Learning: a Review and New Perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.8, pp.1798-1828, 2013.
  [13] Y. LeCun, Y. Bengio, G. Hinton, “Deep Learning,” Nature, Vol.521, No.7553, pp.436-444, 2015.
  [14] J. Schmidhuber, “Deep Learning in Neural Networks: an Overview,” Neural Networks, Vol.61, pp.85-117, 2015.
  [15] S. Zhang, A. E. Choromanska, Y. LeCun, “Deep Learning with Elastic Averaging SGD,” Advances in Neural Information Processing Systems, pp.685-693, 2015.
  [16] I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning,” MIT Press, 2016.
  [17] M. Courbariaux, Y. Bengio, JP. David, “BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations,” Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15), pp.3123–3131, 2015.
  [18] X. Lin, C. Zhao, W. Pan, “Towards Accurate Binary Convolutional Neural Network,” Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), pp.344–352, 2017.
  [19] H. Qin, R. Gong, X. Liu, X. Bai, J. Song, N. Sebe, “Binary Neural Networks: A Survey,” Pattern Recognition, Vol.105, 107281, 2020.
  [20] H. Okada, “Evolutionary Training of Binary Neural Networks by Evolution Strategy, ” International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue 1, pp.31–35, 2021.
  [21] H. Okada, “Evolutionary Training of Binary Neural Networks by Genetic Algorithm, ” International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue 6, pp.64–69, 2021.
  [22] H.P. Schwefel, “Evolution Strategies: a Family of Non-Linear Optimization Techniques based on Imitating Some Principles of Organic Evolution,” Annals of Operations Research, Vol.1, pp.165–167, 1984.
  [23] H.P. Schwefel, “Evolution and Optimum Seeking,” Wiley & Sons, 1995.
  [24] H.G. Beyer, H.P. Schwefel, “Evolution Strategies: a Comprehensive Introduction,” Journal Natural Computing, Vol.1, No.1, pp.3–52, 2002.
  [25] D. E. Goldberg, J. H. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, Vol.3, No.2, pp.95–99, 1988.
  [26] R. Storn, K. Price, “Differential Evolution – a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, Vol.11, pp.341–359, 1997.

  Citation
  Hidehiko Okada, "Evolutionary Training of Binary Neural Networks by Differential Evolution," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.26-31, 2022

• Open Access   Article

  A Development of an Online Student Attendance Management Information System: Case Study “University of Tourism, Technology, and Business Studies”

  GATETE Marcel, HARUBWIRA Flaubert

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.1 , pp.32-48, Feb-2022

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  Abstract
  Student class attendance at the university and college level has been a subject of concern for many years. Poor class attendance is an issue in higher education in several countries as various universities adopt manual attendance recording which a tedious task is sometimes. Instead of focusing on teaching, faculty members are often stuck with completing formal duties, e.g. taking daily student attendance on sheets of papers. In today`s competitive world, with increasing working hours and less classroom time, teachers need automated student attendance management information systems which help them manage precious class time efficiently. An automated system regularly helps lecturers record & manage daily student attendance to speed up the daily attendance process. University of Tourism, Technology, an Business Studies Attendance Management Information System (UTB-SAMIS) is a sub-module of the University’ Integrated Management Information System, a web-based, and online application automating the whole university’s management. UTB-SAMIS was designed to replace its existing manual record-keeping system. The new system makes it easy to add new students to the current module, create daily attendance, mark online attendance of students during class & reduce manual work, display the current timetable of the ongoing module, class, level, specialization, period, and Lecturer’s name. A lecturer can track and manage student attendance by displaying them by list and by card, adding new students to class, uploading attendance, displaying CAT student list, displaying exam student list and attendance, downloading mark sheets, checking and downloading attendance reports by module and by student. This new system is efficient in speeding up the daily attendance process, as these services are provided efficiently and cost-effectively to reduce the time and resources currently required for such tasks. This is an online application with great user interface designs, more performance enhancements, and many enriched modules. It works for a big deal to bring value to the words ‘care’ and ‘comfort’ in this higher learning scenario. Besides, UTB-SAMIS is endowed with an advanced feature as it is a part of the university website, it can be accessed online anywhere at any time.

  Key-Words / Index Term
  Automation; Information; PHP; MySQL Server; Professional Education

  References
  [1] Yadav, R., & Nainan, S. (2014). Design of RFID based student attendance system with notification to parents using GSM. International Journal of Engineering, 3(2).
  [2] Newman-Ford, L, Fitzgibbon, K., Lloyd, S., and Thomas, S. (2015), A large scale investigation into the relationship between attendance and attainment: a study using an innovation, electronic attendance monitoring system. Studies in Higher Education. 33 (6), 699 – 717. December.
  [3] Walia, H., & Jain, N. (2016). Fingerprint Based Attendance Systems-A Review. International Research Journal of Engineering and Technology, 3(5), pp. 1166- 1171.
  [4] Jacob, J., Jha, K., Kotak, P., & Puthran, S. (2015, October). Mobile attendance using Near Field Communication and One-Time Password. In Green Computing and Internet of Things.
  [5] Prince, N., Sengupta, A., & Unni, M. K (2016). Implementation of IoT Based Attendance System on a Dedicated Web-Server. International Journal of Scientific & Engineering Research. 7(6), pp. 351- 355.
  [6] Arbain, N., Nordin, N. F., Isa, N. M., & Saaidin, S. (2014, December). LAS: Web-based laboratory attendance system by integrating RFID-ARDUINO technology. In Electrical, Electronics and System Engineering (ICEESE), 2014 International Conference on (pp. 89-94). IEEE.
  [7] Kamal Hingorani, Bob McNeal, Darius Carlton, Nasrin Askari-Danesh (2013), Enhancing a Learning Management System to track attendance using RFID”, Issues in Information Systems Volume 14, Issue 2, pp.313-318.
  [8] Patel, R., Patel, N., & Gajjar, M. (2012). Online students’ attendance monitoring system in classroom using radio frequency identification technology: a proposed system framework. International Journal of Emerging Technology and Advanced Engineering, 2(2), 61-66.
  [9] Yuru, Z., Delong, C., & Liping, T. (2013). The Research and Application of College Student Attendance System based on RFID Technology. International Journal of Control and Automation, 6(2), 273-282.
  [10] Kurniali, S. "The Development of a Web-Based Attendance System with RFID for Higher Education Institution in Binus University." EPJ Web of Conferences. Vol. 68. EDP Sciences, 2014.
  [11] Schalkwyk, S. Menkveld H. and Ruiters, J. (2010). What’s the story with class attendance? First-year
  students: Statistics and Perspectives. South African Journal of Higher Education, 24 (4), 630 – 645.
  [12] Kohalli, S. C., Kulkarni, R., Salimath, M., Hegde, M., & Hongal, R. (2016). Smart Wireless Attendance System. International Journal of Computer Sciences and Engineering, 4(10), pp. 131-137.

  Citation
  GATETE Marcel, HARUBWIRA Flaubert, "A Development of an Online Student Attendance Management Information System: Case Study “University of Tourism, Technology, and Business Studies”," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.1, pp.32-48, 2022