Volume-8 , Issue-3 , Jun 2020, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Correlation study of New Cases, Deaths, Recoveries and Temperature with Machine Learning during COVID-19 spread in Saudi Arabia

  Zafar Iqbal Khan, Yasir Javed, Khurram Naim Shmasi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.1-5, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Millions of people have been infected and killed by the recent outbreak of novel coronavirus throughout the world. It has affected 210 countries around the globe and two International conveyances [1]. It has evolved from epidemic to pandemic crossing all physical, socio economic and geographic barriers. The untraceable virus mutations can quickly effect hundreds of people before the antibodies are developed by the immune system. Since its first inception in Wuhan, the virus has rapidly influenced all nooks and corners of the tightly connected world. The imperative lethality of the virus varies from hot temperature to cold climates. There have been different impacts of COVID-19 on different races, geographical conditions and socio-cultural environments. It is evident that temperature is a critical factor for incubation period of pathogens. This research paper tries to find out the correlation among various factors such as infections, deaths, recoveries of the patients infected with COVID-19 with respect to the Temperature with the help of k-means clustering

  Key-Words / Index Term
  Coronavirus, COVID-19, k-means, clustering, Machine Learning, correlation

  References
  [1] COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/ extracted on April 27, 2020
  [2] The Nonstructural Proteins Directing Coronavirus RNA Synthesis and Processing. https://www.sciencedirect.com/science/article/pii/S0065352716300471?via%3Dihub
  [3] Human Coronavirus Types. https://www.cdc.gov/coronavirus/types.html
  [4] Rolling updates on coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen
  [5] WHO Director-General`s statement on IHR Emergency Committee on Novel Coronavirus (2019-nCoV). https://www.who.int/dg/speeches/detail/who-director-general-s-statement-on-ihr-emergency-committee-on-novel-coronavirus-(2019-ncov)
  [6] Coronavirus Worldwide Graphs. https://www.worldometers.info/coronavirus//worldwide-graphs/ extracted on April 27, 2020
  [7] 3D medical animation corona virus. https://commons.wikimedia.org/wiki/File:3D_medical_animation_corona_virus.jpg
  [8] https://www.scientificanimations.com (https://commons.wikimedia.org/wiki/File:3D_medical_animation_corona_virus.jpg), https://creativecommons.org/licenses/by-sa/4.0/legalcode
  [9] MOH Reports First Case of Coronavirus Infection https://www.moh.gov.sa/en/Ministry/MediaCenter/News/Pages/News-2020-03-02-002.aspx
  [10] Srivastav V, Issenhuth T, Kadkhodamohammadi A, de Mathelin M, Gangi A, Padoy N. (2018). MVOR: A multi-view RGB-D operating room dataset for 2D and 3D human pose estimation. arXiv[Preprint].arXiv:1808.08180. Retrieved from: https://arxiv.org/pdf/1808.08180.pdf
  [11] Alashwal H, El Halaby M, Crouse J J, Abdalla A, Moustafa A A. ?The application of unsupervised clustering methods to Alzheimer?s disease?. Front Comput Neurosci, 13(issue): pp. 1-9, 2019.
  [12] Nithya N, Duraiswamy K, Gomathy P. ?A survey on clustering techniques in medical diagnosis.? Int J Comput Sci Trends Technol, 1(issue): 17-23, 2013.
  [13] Nikas J B, Low W C. ?Application of clustering analyses to the diagnosis of Huntington?s disease in mice and other diseases with well- defined group boundaries.? Comput Methods Programs Biomed, 104(3): e133-e147, 2011.
  [14] Polat K. ?Classification of Parkinson?s disease using feature weighting method on the basis of fuzzy 366 c-means clustering.? Int J Syst Sci, 43(issue): 597-609, 2012;.
  [15] Chen C -H. ?A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection.? Appl Soft Comput, 20(issue): 4-14, 2014.
  [16] Yilmaz N, Inan O, Uzer M S. ?A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases.? J Med Syst, 38(5): 48, 2014.
  [17] Trevithick L, Painter J, Keown P. ?Mental health clustering and diagnosis in psychiatric in-patients.? BJPsych Bull, 39(issue):pp. 119-123, 2015.
  [18] Wu Y, Duan H, Du S. ?Multiple fuzzy c-means clustering algorithm in medical diagnosis.? Technol Health Care, 23(issue): pp.S519-S527, 2015.
  [19] Nilashi M, Ibrahim O, Ahani A. ?Accuracy improvement for predicting Parkinson?s disease progression.? Sci Rep, 6(issue): 34181, 2016.
  [20] Gamberger D, Lavrac N, Srivatsa S, Tanzi R E, Doraiswamy P M. ?Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer?s disease.? Sci Rep, 7(issue): pp.63-67, 2017;.
  [21] Siddiqui M K, Morales-Menendez R, Gupta P, Iqbal H, Hussain F, Khatoon K, Ahmad S. ?Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis.? J Pure Appl Microbiol, 14(Spl Edn.), 2020.
  [22] Eibe F, Mark AH, Ian HW. 2016. The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
  [23] Mark H, Eibe F, Geoffrey H, Bernhard P, Peter R, Ian H W. ?The WEKA Data Mining Software: An Update.? SIGKDD Explorations, 11(1): pp.10-18, 2009.

  Citation
  Zafar Iqbal Khan, Yasir Javed, Khurram Naim Shmasi, "Correlation study of New Cases, Deaths, Recoveries and Temperature with Machine Learning during COVID-19 spread in Saudi Arabia," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.1-5, 2020

• Open Access   Article

  Drone of Gafsa: A Wind-powered UAV as Flying RSU in VANET

  Wassim Aissaoui, Raik Aissaoui

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.6-12, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Improving the road safety of vehicles, avoiding traffic jams, providing convenience and comfort to passengers and drivers, efficient travel are the objectives of Vehicular Ad-hoc Networks (VANETs). All these objectives are achieved through effective communication between vehicles and other VANET components (Road Side Unit (RSU), Unmanned Aerial Vehicle (UAV), etc...). The RSU or UAV sends (and / or) receives messages (to / from) vehicles within its coverage area. Drone can perform other operations related to road traffic management. However, it has a constraint related to its battery autonomy. Besides, all drones operating in smart cities for traffic management work in coordination with other drones or partially. We propose a new VANET architecture where UAV replaces RSU. In addition, we propose a charging mechanism using wind energy. The simulation results show an affinity between the values obtained following the use of RSU and UAV. As it shows acceptable and workable results for the proposed charging mechanism

  Key-Words / Index Term
  VANET, Road safety, UAV, RSU, Wind energy

  References
  [1] Volvo will test self-driving cars with real customers in 2017. http://www.wired.com/2015/02/volvo-will-test-self-driving-cars-real- customers-2017.
  [2] A. B. Reis, S. Sargento, and O. K. Tonguz, ?On the performance of sparse vehicular networks with road side units,? in 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), May 2011, pp. 1?5.
  [3] W. Shi, H. Zhou, J. Li, W. Xu, N. Zhang, and X. Shen, ?Drone assisted vehicular networks: Architecture, challenges and opportunities,? IEEE Network, vol. 32, no. 3, pp. 130?137, May 2018.
  [4] P. Patil and A. Gokhale, ?Voronoi-based placement of road-side units to improve dynamic resource management in vehicular ad hoc networks,? in 2013 International Conference on Collaboration Technologies and Systems (CTS), May 2013, pp. 389?396.
  [5] N. Wisitpongphan, F. Bai, P. Mudalige, V. Sadekar, and O. Tonguz, ?Routing in sparse vehicular ad hoc wireless networks,? IEEE Journal on Selected Areas in Communications, vol. 25, no. 8, pp. 1538?1556, Oct 2007.
  [6] H. Menouar, I. Guvenc, K. Akkaya, A. S. Uluagac, A. Kadri, and A.Tuncer, ?Uav-enabled intelligent transportation systems for the smart city: Applications and challenges,? IEEE Communications Magazine, vol. 55, no. 3, pp. 22?28, March 2017.
  [7] S. Morton, R. D?Sa, and N. Papanikolopoulos, ?Solar powered uav: Design and experiments,? in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2015, pp. 2460?2466.
  [8] A. Noth, W. Engel, and R. Siegwart, ?Design of an ultra-lightweight autonomous solar airplane for continuous flight,? in Field and Service Robotics, P. Corke and S. Sukkariah, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 441?452.
  [9] A. Noth, M. W. Engel, and R. Siegwart, ?Flying solo and solar to mars,? IEEE Robotics Automation Magazine, vol. 13, no. 3, pp. 44?52, Sep. 2006.
  [10] A. North, R. Siegwart, and W. Engel, Autonomous Solar UAV for Sustainable Flights. Dordrecht: Springer Netherlands, 2007, pp. 377? 405.
  [11] A. F. Burke, ?Batteries and ultracapacitors for electric, hybrid, and fuel cell vehicles,? Proceedings of the IEEE, vol. 95, no. 4, pp. 806?820, April 2007.
  [12] T. Long, M. Ozger, O. Cetinkaya, and O. B. Akan, ?Energy neutral internet of drones,? IEEE Communications Magazine, vol. 56, no. 1, pp. 22?28, Jan 2018.

  Citation
  Wassim Aissaoui, Raik Aissaoui, "Drone of Gafsa: A Wind-powered UAV as Flying RSU in VANET," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.6-12, 2020

• Open Access   Article

  A Machine Learning Approach for Diagnosing Meningococcal Meningitis

  Solomon Osarumwense Alile, Moses Eromosele Bello

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.13-25, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Meningococcal Meningitis is a hazardous sickness brought about by meningococcal microbes called Neisseria meningitidis which offers ascend to irritation of the meninges which influences people extending from babies, more established youngsters and grown-ups in particular old enough. The manifestations of this sickness are fatigue, nausea, seizures, vomiting, stiff neck, cold, sleepiness, skin rash, spasm, cough, loss of appetite, fever just to name a yet a couple. In late past, a few systems have been created to analyze this endemic malady; however they created a great deal of bogus negative during testing and couldn`t distinguish meningococcal meningitis, its overlapping symptoms and serogroup types. Thus, in this paper, we proposed and simulated a model to anticipate meningococcal meningitis and its serogroup types utilizing an AI strategy called Bayesian Belief Network. The model was structured utilizing Bayes Server and tried with data gathered from meningitis medical repository. The model had a 99.99% forecast precision, 97.12% sensitivity of Meningococcal Meningitis disease, 95.42% sensitivity of Serogroup type A, Serogroup type B, Serogroup type C, and Neisseria Meningitidis in that order

  Key-Words / Index Term
  Meningococcal Meningitis; Bacteria Meningitis; Neisseria Meningitidis; Prediction; Detection; Machine Learning; Bayesian Belief Network Component

  References
  [1] G.J. Tortora , B. Derrickson, ?Principles of Anatomy and Physiology?, (15th edition). J. Wiley. ISBN: 978-1-119-34373-8, pp. 1-10, 2016.
  [2] X. Saez-Llorens,G.H. Mcaleren, ?Bacteria Meningitis in Children?. Lancet.2003; Vol. 361(9375), pp.2139-2148.
  [3] H. Ahmed, ?Inflammatory Disease of the Central Nervous System in Paediatrics and Child Health in a Tropical Region? 2nd ed. Owerri, Nigeria, African Educational Services, pp. 525- 570, 2009.
  [4] World Health Organization, ?WHO Report on Global Surveillance of Epidemic-prone Infectious Diseases-Meningococcal Disease?, WHO/CDS/CSR/2000.1, Chapter 5: Meningococcal Disease.pdf, pp. 55-61, 2000.
  [5] Health Department, Republic of South Africa, "Guidelines for the Management, Prevention and Control of Meningococcal Disease in South Africa 2011". ISBN: 978-1-920031-63-3, pp. 1-48, 2011.
  [6] J.R. Zunt, N.J. Kassebaum, N. Blake, L. Glennie, C. Wright, E. Nichols et. al, ?Global, Regional, and National Burden of Meningitis, 1990?2016: A Systematic Analysis for the Global Burden of Disease Study 2016?. Lancet Neurol, 17(12): pp.1061?1082, 2018.
  [7] WHO Technical Task Force, ?Defeating Meningitis By 2030: Baseline Situation Analysis?. The 2010 MenAfriVac vaccine launch in Burkina Faso PATH/Gabe Bienczycki. pp. 1-54, 2010.
  [8] A.P. Hrishi, S. Manikandah, ?Cerebral Spinal Fluid (CSF) Analysis and Interpretation In Neurocritical Care for Acute Neurological Conditions?. Indian Journal of Critical Care Medicine, Vol. 23(2), pp. 115-119, 2019. doi: 10.5005/jp-journals-10071-23197.
  [9] Doherty, C.M and Forbes, R.B.:?Diagnostic Lumbar Puncture?. The Ulster Medical Journal, Vol. 83(2), pp. 93-102, 2014.
  [10] K. Revett, F. Gorunescu, M. Gorunescu, M. Ene, ?A Machine Learning Approach to Differentiating Bacterial From Viral Meningitis?. IEEE. Reprinted from IEEE John Vincent Atanasoff 2006. International Symposium on Modern Computing (JVA`06). IEEE, Los Alamitos, USA, ISBN 0769526438, pp. 155-162, 2006.
  [11] V.K. Mago, R. Mehta, R. Woolrych, E.I. Papageorgiou, "Supporting Meningitis Diagnosis Amongst Infants and Children Through The Use of Fuzzy Cognitive Mapping". BMC Medical Informatics and Decision Making, Vol. 12(98), pp: 1-12, 2012.
  [12] M. Langarizadeh, H. Khajehpour, M. Eftekhari, ?A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis? Iranian Journal of Medical Physics, Vol. 12(1), pp. 1-6, Winter 2015.
  [13] W. Ritha, W.L, Merline, ?Risk Factors of Meningitis in Adults-An Analysis Using Fuzzy Cognitive Map with TOPSIS?. International Journal of Scientific and Innovative Mathematical Research (IJSIMR) Vol. 2(4), pp. 418-425, April 2014, ISSN 2347-307X (Print) & ISSN 2347-3142 (Online).
  [14] N.D. Oye, L.L. Thomas, ?Fuzzy Model for Diagnosis of Bacterial Meningitis?. International Journal of Computer Applications Technology and Research, Vol. 8(2), pp. 33-51, 2019, ISSN: 2319?8656.
  [15] A.M. Abubakar, K.A. Abdulsalam, J.A. Adebisi, ?Application of Artificial Neural Network for Diagnosis of Cerebrospinal Meningitis?. Journal of Engineering Research, Special Edition, Vol. 24(2), pp.12-25, December 2019.
  [16] K. Zaccari, E.C. Marujo, ?Machine Learning for Aiding Meningitis Diagnosis in Pediatric Patients?. World Academy of Science, Engineering and Technology International Journal of Medical and Health Sciences, Vol. 13(9), pp. 411-419, 2019, ISNI:0000000091950263.
  [17] V.M. Lelis, E. Guzman, M-V. Belmonte,?Non-Invasive Meningitis Diagnosis Using Decision Trees?. IEEE Access, Vol. 8, pp. 18394-18407, January 29, 2020., Digital Object Identifier 10.1109/ACCESS.2020.2966397.
  [18] A. Simon, M.S. Deo, S. Venkatesan, D.R.R. Babu, ?Machine Learning and its Applications?. International Journal of Electrical Sciences & Engineering (IJESE), Vol. 1(1), pp. 22-24, 2015.
  [19] I. Ben-Gal, F. Ruggeri, F. Faltin, R. Kenett, ?Bayesian Networks?. Encyclopedia of Statistics in Quality and Reliability. John Wiley and Sons, Ltd. www.eng.tau.ac.il/bengal/BN.pdf/. pp. 1-6, 2007.
  [20] Kaggle Datasets, ?Disease Outbreaks in Nigeria Datasets: Predicting Meningitis Disease Outbreaks Using Machine Learning?, 2020.

  Citation
  Solomon Osarumwense Alile, Moses Eromosele Bello, "A Machine Learning Approach for Diagnosing Meningococcal Meningitis," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.13-25, 2020

• Open Access   Article

  Coronary Illness Prediction and Analysis of Various Machine Learning Techniques

  Banumathi P., Miraclin Joyce Pamila J.C.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.26-33, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The heart is an important organ for all livings, any functional problem in the heart has a direct impact on the survival of human beings. It instigates issues through the vascular to other body organs, for example, the lungs, kidney, liver, brain and so on. Consumption of junk food, alcohol, smoke, and other traditional changes affect the health of people and mainly heart in recent days. A lot of clinical information pertinent to cardiovascular ailment is produced in the clinical association and they need to be analysed critically to predict cardiovascular ailment. In the proposal, the Cleveland clinical information is examined and used to anticipate cardiovascular ailment utilizing various ML techniques. These methods use 13 clinical parameters of the patient to analyse the coronary illness. So it is highly desirable to help individuals to recognize whether they are prone to coronary illness or not. Different strategies are looked at against one another and reports performance exactness.ML techniques also compared with deep learning ANN technique. Also introduce the Random forest techniques which constructs decision trees by splitting information by subset in traditional approaches. The proposal introduces the constructions of DTs for each and every attribute separately and integrates to produce final results

  Key-Words / Index Term
  Machine Learning, Artificial Neural Network, Random Forest, K nearest neighbors, Support Vector Machine, Logistic Regression, Decision Trees, XG Boost, Gaussian Na?ve Bayes

  References
  [1]. Senthilkumar Mohan, Chandrasegar Thirumala, Gautam Srivastava, Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques, IEEE ACCESS Special Section On Smart Caching, Communications, Computing And Cyber Security For Information-Centric Internet Of Things, 81542 - 81554 (Jun2019). https://doi.org/10.1109/ACCESS.2019.2923707.
  [2]. Dinesh Kumar G, Santhosh Kumar D, Arumugaraj K, Mareeswari V, Prediction of Cardiovascular Disease Using Machine Learning Algorithms, International Conference on Current Trends toward Converging Technologies,(ICCTCT)(nov2018). https://doi.org/10.1109/ICCTCT.2018.8550857.
  [3]. SanchayitaDhar, Pritha Datta, Ankur Biswas, Tanusree Dey, Krishna Roy, A Hybrid Machine Learning Approach for Prediction of Heart Diseases, 4th International Conference on Computing Communication and Automation (ICCCA), (Dec 2018). https://doi.org/10.1109/CCAA.2018.8777531.
  [4]. Balasaheb Tarle, Sudarson Jena, an Artificial Neural Network Based Pattern Classification Algorithm for Diagnosis of Heart Disease, Third International Conference on Computing, Communication, Control And Automation (ICCUBEA), (Aug2017). https://doi.org/10.1109/ICCUBEA.2017.8463729.
  [5]. Dr.T. Karthikeyan, Dr. B. Ragavan, V.A.Kanimozhi, A Study on Data mining Classification Algorithms in Heart Disease Prediction, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2016, Volume 5, Issue 4. (Apr 2016).
  [6]. Theresa Princy. R, J. Thomas, Human Heart Disease Prediction System using Data Mining Techniques, International Conference on Circuit, Power and Computing Technologies [ICCPCT] (Mar 2016). https://doi.org/10.1109/ICCPCT.2016.7530265.
  [7]. Tulay Karayilan, Ozkan Kilic, Prediction of Heart Disease Using Neural Network, 2017 International Conference on Computer Science and Engineering (UBMK)(Oct2017). https://doi.org/10.1109/UBMK.2017.8093512.
  [8]. Monika Gandhi, Dr. Shailendra Narayan Singh, Predictions in Heart Disease Using Techniques of Data Mining, 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management(ABLAZE),(Feb2015). https://doi.org/10.1109/ABLAZE.2015.7154917.
  [9]. Rucha Shinde, Sandhya Arjun, Priyanka Patil, Prof. Jaishree Waghmare, An Intelligent Heart Disease Prediction System Using K-Means Clustering and Na?ve Bayes Algorithm, International Journal of Computer Science and Information Technologies, Vol. 6 (1), 2015, 637-639.
  [10].Dhanashree S. Medhekar, Mayur P. Bote, Shruti D. Deshmukh, Heart Disease Prediction System using Naive Bayes, International Journal of Enhanced Research in Science Technology & Engineering, 2013, vol. 2 issue 3.
  [11].Nabeel al-milli, Backpropagation Neural Network for prediction of heart disease,Journal of Theoretical and Applied Information Technology, 2013, vol-56 No.1.
  [12].R.Chitra and V.Seenivasagam, Review of Heart Disease Prediction System using Data Mining and Hybrid Intelligent Techniques, ICTACT Journal on Soft Computing, 2013, Volume: 03, Issue: 04.
  [14]. Milan Kumari, Sunil, Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, International Journal of Computer Science and Technology, IJCST Vol. 2, Issue 2, June 2011.
  [15].https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease
  [16]. https://www.worldatlas.com/articles/top-ten-leading-causes-of-death-in-the-world.html

  Citation
  Banumathi P., Miraclin Joyce Pamila J.C., "Coronary Illness Prediction and Analysis of Various Machine Learning Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.26-33, 2020

• Open Access   Article

  Modeling of Heterogeneous WLAN Based on Traffic Application

  O.J. Adaramola, J.R. Olasina

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.34-38, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  inherently, the negative effect of parameters of interest like: low bit rate, speed loss, packet drop, bandwidth congestion, and decrease in signal strength are latent in nature. These obviously affect the behavioral attitude of a network system and those affected areas are to be monitored and therefore be reduced even though, these factors may not be completely eliminated. No doubt that the performance of the wireless local area network (WLAN) technology has been greatly affect by these retarded factors. Options of total elimination of these factors are almost impossible, but then, this study will greatly reveal the negative effects, posed by these undesirable factors. In the light of these, three different WLAN technology scenarios were modeled and simulated and parameters of concern include: delay (sec), load (bits/sec), and throughput (bits/sec). The file transfer protocol (FTP) as the traffic application was used to administer the effect of the stated factors on the each WLAN of different scenarios. Also, the results obtained showed: more delay, minimum throughput, and minimum load occurred in a scenario with the highest number of nodes. Besides, if factors like: distance, interference, signal sharing, and other physical obstructions between the devices were considered, better results would be obtained.

  Key-Words / Index Term
  Load; Protocol; Signal Sharing; Throughput; WLAN

  References
  [1] K. Ram, L. Vijay, ?Simulation and Analysis of Wireless Local Area Network Using Opnet,? Engineering Sciences International Research Journal, Vol. 2, Issue. 1, pp. 101-104, 2014.
  [2] O. O. Elechi, ?Design and Simulation of Wireless Local Area Network for Administrative Office Using OPNET Network Simulator: A Practical Approach,? Information and Knowledge Management, Vol. 4, Issue.10, pp 27-33, 2014.
  [3] U. K. Okpeki, J. O. Egwaile, and F. Edeko, ?Performance and Comparative Analysis of Wired and Wireless Communication Systems Using Local Area Network Based on IEEE 802.3 And IEEE 802.11,? Journal of Applied Sciences and Environmental Management, Vol. 22, Issue 11, pp.1727?1731, 2018.
  [4] V. Rajan, S. S. Ravinder, and S. S. Gurpreet, ?OPNET Based Wireless LAN Performance Improvisation,? International Journal of Computer Applications, Vol. 48, Issue. 1, pp.0975 ? 8887. 2012.
  [5] R. Krishan, and V. Laxmi, ?Simulation and Analysis of Wireless Local Area Network Using OPNET,? Engineering Sciences International Research Journal, Vol. 2, Issue.1, pp 101-104, 2014.
  [6] H. G. Sameh, and R. A. Abedel, ?Wireless Network Performance Optimization Using Model,? Information Technology Journal, Vol. 5, Issue. 1, pp.18-24. 2006.
  [7] O. M. Olanrewaju, ?The Modeling and Simulation of Wireless Campus Network,? International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, Issue. 9, pp 68-78, 2018.
  [8] A. S. Islam, and M. T. A. Ghassan, ?Comparative Study of Wireless LAN using OPNET and NS-2,? In the Proceedings of the 2016 Conference of Basic Sciences and Engineering Studies (SGCAC), pp 196-200, 2016
  [9] R. K. Bansal, V. Gupta, and R. Malhotra, ?Performance Analysis of Wired and Wireless LAN Using Soft Computing Techniques- A Review,? Global Journal of Computer Science and Technology, Vol. 10, Issue. 8, pp 67-71, 2010.
  [10] R. Vohra, R. S. Sawhney, and G. S. Saini, ?OPNET Based Wireless LAN Performance Improvisation?, International Journal of Computer Applications, Vol. 48, Issue. 1, pp 975 ? 8887, 2012.
  [11] S. H. Ghwanmeh, and A. R. Al-Zoubidi, ?Wireless Network Performance Optimization Using OPNET Modeler,? Information Technology Journal, Vol. 5, Issue. 1, pp 18 - 24, 2006.
  [12] A. H. Ali, A. N. Abbas, and M. H. Hassan, ?Performance Evaluation of IEEE802.11g WLANs Using OPNET Modeler,? American Journal of Engineering Research (AJER), Vol. 2, Issue. 12, pp-09-15, 2013.
  [13] J. Wei, ?OPNET-Based WLAN Modeling, and its Performance Testing,? Chemical Engineering Transactions, Vol. 51, Issue. 2283-9216, pp 361-366, 2016.

  Citation
  O.J. Adaramola, J.R. Olasina, "Modeling of Heterogeneous WLAN Based on Traffic Application," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.34-38, 2020

• Open Access   Article

  GPU based Gaussian Bilateral Filter for Noisy MR Image

  S.D. Oza, K.R. Joshi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.39-44, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Magnetic resonance imaging (MRI) diagnostic technique is significantly used to diagnose disorders and deformities of the knee. The process of MRI corrupts the MR image with signal dependent rician noise which hinders accuracy of diagnosis. The paper proposes a new Gaussian Bilateral Filter giving better performance as against bilateral only and iterative bilateral filter, especially for knee MRI with high noise level. The research work further provides an accelerated solution to the challenge faced by the proposed filter for large resolution images. The work here implements the proposed Gaussian Bilateral filter using CUDA GPU there by achieving real time performance for knee MR image with 64Mega pixel resolution. The speed up achieved with CUDA GPU implementation of the proposed filter is 320.064 times more than its CPU counterpart. This would surely contribute to intelligent diagnostic setups

  Key-Words / Index Term
  Gaussian Bilateral Filter, MRI, CUDA GPU, Shared memory

  References
  [1] J. Hornak ?The Basics of MRI?, e-book, https://www.cis.rit.edu/htbooks/mri
  [2] K R Joshi And Oza S D, ?Mri Denoising For Healthcare?, M. H. Kolekar And Vinod Kumar (Ed) In Biomedical Signal And Image Processing In Patient Care, Igi Global, pp 65-85, 2017
  [3] H. Gudbjartsson. And S. Partz, ?The Rician distribution of noisy MRI data?, in HHS Public Access Manuscript, PMC, 2008
  [4] J. Mohan, V. Krishnaveni and Y. Guo, ?A survey on magnetic resonance image denoising methods?, in Biomedical Signal processing and Control, Elsevier, 9( 1), pp 56-59, 2014
  [5] S. D. Oza and K. R. Joshi, ?Performance Analysis of Denoising Filters for MR Images? Amlan Chakrabarti, Neha Sharma,Valentina Emilia Balas (Eds) In Advances in Computing Applications, Springer Singapore, pp 87-96, 2016
  [6] G. Gerig, O. Kubler and F. Jolez, ?Nonlinear anisotropic filtering of MRI data?, in IEEE Transactions. Medical aging, 11(2), pp. 221?232, 1992
  [7] P. Coupe et al, ?Fast non local means denoising for MR images?, in : Proceedings at the 9th International Conference on Medical Image Computing and Computer assisted Intervention (MICCAI), Copenhagen, pp. 33?40, 2006.
  [8] P. Coupe et al., ?An optimized block wise nonlocal means denoising filter for 3-D magnetic resonance images?, IEEE Trans. Med. Imaging, 27(4), pp. 425?441, 2008
  [9] P. Coupe et al, ?3D wavelet sub bands mixing for image denoising?, Int. J. Biomed. Imaging , http://dx.doi.org/10.1155/2008/590183, Article ID 590183, 2008.
  [10] Liu et al., ?Noise Estimation from Single Image?, proc. IEEE Computer Vision and Pattern Recognition, 1, pp. 901-908, 2006
  [11] H. Liu, Y. Cihui, P. Ning, S. Enmin, G. Richard G., ?Denoising 3D MR images by the enhanced non-local means filter for rician noise?, in Magnetic Resonance Imaging, 28(10) pp. 1485?1496, 2010
  [12] J. Manj?n, J. Carbonell-Caballero, J. Lull, G. Garc?a-Mart?, L. Mart?-Bonmat?. and M. Robles, ?MRI denoising using non-local means?, in Medical Image Anaysis., Elsevier, 12(4), pp. 514?523, 2008.
  [13] J. Manj?n, P. Coup?, L. Mart?-Bonmat?, D. Collins, M. Robles, ?Adaptive non-local means denoising of MR images with spatially varying noise levels?, in Magnetic Resonance Imaging, 31(1), pp. 192?203, 2010
  [14] N. Wiest-Daessl? et al , ?Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI?, Med. Image Comput. Comput. Assist. Interv. 11(2), pp. 171?179, 2008.
  [15] M. Elad, ?On the Bilateral Filter and Ways To Improve It,? IEEE transaction on Image processing, 11(10), pp. 1141-1151, 2002
  [16] W. Wong et al, ?Trilateral Filtering for Biomedical Images,? in Proc. IEEE Int. Symposium Biomedical Imaging, pp. 820-823, 2004
  [17] R. Riji, ?Iterative Bilateral Filter for Rician Noise Reduction in MR Images,? Signal, Image and Video Processing, 9(7), pp 1543-1548, 2015
  [18] V. Aurich and J. Weule, ?Nonlinear Gaussian Filters Performing Edge preserving Diffusion?, In Mutererkennung, 17, DAGM-Symposium, pp 538-545, Springer-verlog, 1995
  [19] J. Larsson, ?A Case Study of Parallel Bilateral Filtering on the GPU?, Master Thesis, Malardalen University 2015
  [20] M. Howison, ?Comparing GPU Implementations of Bilateral and Anisotropic Diffusion Filters for 3D Biomedical Datasets?, Computational Research Division, Lawrence Berkeley National Laboratory, USA, 2018
  [21] M. Howison, and E. W. Bethel, ?GPU-accelerated denoising of 3D magnetic resonance Images?, Journal of Real-Time Image Processing (JRTIP) Springer, vol. 13, issue 4, pp. 713-724, 2017
  [22] J. Fengjiao, S. Dan and C. Liu, Dong. ?Fast Adaptive Ultrasound Speckle Reduction with Bilateral Filter on CUDA?, In Proc. of 5th Int Conf on Bioinformatics and Biomedical Engineering, ICBBE, pp. 1-4, 2011
  [23] S. Smith and J. Brady, ?Susan ? a new approach to low level image processing?, Int. Journal Filtering for Gray and Color Images?, in Proc. Int. Conf Computer Vision Vol. 23, pp. 45-78, 1997
  [24] Tomasi C and Manduchi R, ?Bilateral. Computer Vision, pp. 839-846, 1998
  [25] Paris S et al, ?Filtering: Theory and Applications?, Computer Graphics and Vision,. 4(1), pp. 1-73, 2008
  [26] Nvidia CUDA C programming Guide version 4.2, 2012
  [27] CUDA C Best Practices Guide DG-05603-001_v5.0, Design Guide, October 2012
  [28] D. Granata, U. Amato, B. Alfano, MRI Denoising by non local means on multi-GPU, Journal of Real Time Image Processing, ACM, 16(2), 2019
  [29] P K Jithy,, P Simon, ?Review on Automatic Segmentation Techniques in Medical Images?, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, IJSRCSEIT, 2(4,) ISSN : 24356-3307, 2017

  Citation
  S.D. Oza, K.R. Joshi, "GPU based Gaussian Bilateral Filter for Noisy MR Image," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.39-44, 2020

• Open Access   Article

  Agro Informatics and Its Way with Integrating Emerging Technologies?A Potential Utilizations & Educational Perspective

  P.K. Paul, Anil Bhuimalli, R.R. Sinha, Pappachan Baby, Ricardo Saavedra, Bashiru Aremu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.3 , pp.45-52, Jun-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Agriculture becomes an important and valuable concern for each and everyone due to its importance in our daily lives. Without agriculture and particularly cultivation and farming it is difficult to survive and in this context it is essential to have better healthy agricultural systems throughout. More importantly, there should be proper mechanism in healthy and modern agricultural development. And regarding this various type of initiatives and methods could be followed. The method of agriculture is changing rapidly and growing with the support of different tools and technologies viz. genetic technologies, information technology & computing, nanotechnology, Management Science and Technology etc. The interaction and applications of Information Technology and Computing led a subject called Agricultural Informatics. Agricultural Informatics is associated with all the components and sub components of the Information Technology. However the latest applications and integration of the emerging technologies of IT viz. Big Data and Analytics, AI & Robotics, Cloud Computing & Virtualization, Internet of Things etc lead more development and thus Agricultural Informatics become widely changed its actual periphery and functioning. The emerging Agricultural Informatics leading the more progress and development in agro systems and this ultimately helping in society at a large. This paper is theoretical and policy based; discussed various aspects of Agricultural Informatics including its evolution, features and specially the impact of emerging technologies with reference agriculture. Paper focused on possibilities of education al degrees at different level of Agricultural Informatics with reference to emerging technologies

  Key-Words / Index Term
  Agricultural Informatics, ICT, Agricultural Information Technology, Higher Education, Emerging Technologies, AIoT, Emerging Degrees

  References
  [1] Abbasi, A. Z., Islam, N., & Shaikh, Z. A. (2014). A review of wireless sensors and networks` applications in agriculture. Computer Standards & Interfaces, 36(2), 263-270.
  [2] Ad?o, T., Hru?ka, J., P?dua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
  [3] Adetunji, K. E., & Joseph, M. K. (2018, August). Development of a Cloud-based Monitoring System using 4duino: Applications in Agriculture. In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) (pp. 4849-4854). IEEE.
  [4] Ahmad, T., Ahmad, S., & Jamshed, M. (2015, October). A knowledge based Indian agriculture: With cloud ERP arrangement. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 333-340). IEEE.
  [5] Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers` adoption decision of precision agriculture technology. Decision support systems, 54(1), 510-520.
  [6] Babu, S. M., Lakshmi, A. J., & Rao, B. T. (2015, April). A study on cloud based Internet of Things: CloudIoT. In 2015 global conference on communication technologies (GCCT) (pp. 60-65). IEEE.
  [7] Balamurugan, S., Divyabharathi, N., Jayashruthi, K., Bowiya, M., Shermy, R. P., & Shanker, R. (2016). Internet of agriculture: Applying IoT to improve food and farming technology. International Research Journal of Engineering and Technology (IRJET), 3(10), 713-719.
  [8] Bauckhage, C., & Kersting, K. (2013). Data mining and pattern recognition in agriculture. KI-K?nstliche Intelligenz, 27(4), 313-324.
  [9] Channe, H., Kothari, S., & Kadam, D. (2015). Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis. Int. J. Computer Technology & Applications, 6(3), 374-382.
  [10] Gill, S. S., Chana, I., & Buyya, R. (2017). IoT based agriculture as a cloud and big data service: the beginning of digital India. Journal of Organizational and End User Computing (JOEUC), 29(4), 1-23.
  [11] G?mez-Chabla, R., Real-Avil?s, K., Mor?n, C., Grijalva, P., & Recalde, T. (2019, January). IoT Applications in Agriculture: A Systematic Literature Review. In 2nd International Conference on ICTs in Agronomy and Environment (pp. 68-76). Springer, Cham.
  [12] Goraya, M. S., & Kaur, H. (2015). Cloud computing in agriculture. HCTL Open International Journal of Technology Innovations and Research (IJTIR), 16, 2321-1814.
  [13] Guardo, E., Di Stefano, A., La Corte, A., Sapienza, M., & Scat?, M. (2018). A fog computing-based iot framework for precision agriculture. Journal of Internet Technology, 19(5), 1401-1411.
  [14] Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194.
  [15] Kajol, R., & Akshay, K. K. (2018). Automated Agricultural Field Analysis and Monitoring System Using IOT. International Journal of Information Engineering and Electronic Business, 11(2), 17.
  [16] Khattab, A., Abdelgawad, A., & Yelmarthi, K. (2016, December). Design and implementation of a cloud-based IoT scheme for precision agriculture. In 2016 28th International Conference on Microelectronics (ICM) (pp. 201-204). IEEE.
  [17] Liu, S., Guo, L., Webb, H., Ya, X., & Chang, X. (2019). Internet of Things monitoring system of modern eco-agriculture based on cloud computing. IEEE Access, 7, 37050-37058.
  [18] Manos, B., Polman, N., & Viaggi, D. (2011). Agricultural and environmental informatics, governance and management: Emerging research applications. Z. Andreopoulou (Ed.). IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA).
  [19] Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture, 156, 467-474.
  [20] Na, A., & Isaac, W. (2016, January). Developing a human-centric agricultural model in the IoT environment. In 2016 International Conference on Internet of Things and Applications (IOTA) (pp. 292-297). IEEE.
  [21] Nandyala, C. S., & Kim, H. K. (2016). Green IoT agriculture and healthcare application (GAHA). International Journal of Smart Home, 10(4), 289-300.
  [22] Nayyar, A., & Puri, V. (2016). Smart farming: IoT based smart sensors agriculture stick for live temperature and moisture monitoring using Arduino, cloud computing & solar technology. In Proc. of The International Conference on Communication and Computing Systems (ICCCS-2016) (pp. 9781315364094-121).
  [23] Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66-84.
  [24] Othman, M. F., & Shazali, K. (2012). Wireless sensor network applications: A study in environment monitoring system. Procedia Engineering, 41, 1204-1210.
  [25] Ozdogan, B., Gacar, A., & Aktas, H. (2017). Digital agriculture practices in the context of agriculture 4.0. Journal of Economics Finance and Accounting, 4(2), 186-193.
  [26] Pau1, Prantosh Kumar Minakshi Ghosh, Dipak Chaterjee. (2014)Information Systems & Networks (ISN): Emphasizing Agricultural Information Networks with a case Study of AGRIS.Scholars Journal of Agriculture and Veterinary Sciences. 1 (1).
  [27] Paul, Prantosh Kumar (2013) Information and Knowledge Requirement for Farming and Agriculture Domain.International Journal of Soft Computing Bio Informatics4 (2), 80-84
  [28] Paul, Prantosh Kumar et.al. (2015) Agricultural Problems in India requiring solution through Agricultural Information Systems: Problems and Prospects in Developing Countries. International Journal of Information Science and Computing 2 (1), 33-40
  [29] Paul, Prantosh Kumar et.al. (2016). Cloud Computing and Virtualization in Agricultural Space: A Knowledge Survey. Palgo Journal of Agriculture, 4(2), 202-206
  [30] Paul, Prantosh Kumar et.al. (2015). Information and Communication Technology and Information: their role in Tea Cultivation and Marketing in the context of Developing Countries?A Theoretical Approach. Current Trends in Biotechnology and Chemical Research. 5 (2), 155-161
  [31] Rezn?k, T., Charv?t, K., Lukas, V., Charv?t Jr, K., Hor?kov?, ?., & Kepka, M. (2015, September). Open data model for (precision) agriculture applications and agricultural pollution monitoring. In EnviroInfo and ICT for Sustainability 2015. Atlantis Press.
  [32] TongKe, F. (2013). Smart agriculture based on cloud computing and IOT. Journal of Convergence Information Technology, 8(2).
  [33] Tsekouropoulos, G., Andreopoulou, Z., Koliouska, C., Koutroumanidis, T., & Batzios, C. (2013). Internet functions in marketing: multicriteria ranking of agricultural SMEs websites in Greece. Agr?rinformatika/journal of agricultural informatics, 4(2), 22-36.
  [34] Zamora-Izquierdo, M. A., Santa, J., Mart?nez, J. A., Mart?nez, V., & Skarmeta, A. F. (2019). Smart farming IoT platform based on edge and cloud computing. Biosystems engineering, 177, 4-17

  Citation
  P.K. Paul, Anil Bhuimalli, R.R. Sinha, Pappachan Baby, Ricardo Saavedra, Bashiru Aremu, "Agro Informatics and Its Way with Integrating Emerging Technologies?A Potential Utilizations & Educational Perspective," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.45-52, 2020