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• Open Access   Article

  Solving University Course Timetabling Problem Using a Meta-Heuristic Algorithm Based on Clustering

  Tooba Bardestani, Musa Mojarad, Hassan Arfaeinia

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.1-8, Dec-2021

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  This paper examines the University Course Timetabling Problem (UCTP), which is one of the time-consuming issues in any educational environment. In UCTP there are many parameters such as courses, classes, students and timeslots that make it an NP-Hard problem. The objective here is to produce an acceptable timetable based on satisfying a set of hard and soft constraints. Hard constraints must be observed, and adherence to soft constraints will improve the timetable, although failure to comply with soft constraints does not make the solution unacceptable. In this paper, a clustering-based genetic algorithm in the form of a meta-heuristic algorithm is used to solve this problem. Here, instances of input problems are examined from two perspectives. The first view is instances that have a small search space and the solutions in which they are produced often have no hard constraints. The second view is instances of high complexity and relatively large search space. In the first category, reducing soft constraints is challenging, while in the second category, it is very difficult to produce a solution without hard constraints. In order to reduce the complexity of the proposed algorithm and to properly orient the quantification of events, here events are clustered based on the number of students. Then, the initial population is created innovatively with the aim of minimizing hard constraints. The results on the BenPaechter database indicate the fact that the proposed algorithm offers better solutions in most instances than other similar algorithms.

  Key-Words / Index Term
  UCTP, Timetabling, Clustering, Meta-heuristic, Optimization, Constraints

  References
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  Citation
  Tooba Bardestani, Musa Mojarad, Hassan Arfaeinia, "Solving University Course Timetabling Problem Using a Meta-Heuristic Algorithm Based on Clustering," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.1-8, 2021

• Open Access   Article

  Melanoma Skin Cancer Detection Using Support Vector Machines and Convolutional Neural Networks

  Christopher Ebuka Ojukwu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.9-12, Dec-2021

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  Speed remains an important factor in the detection of skin cancer. Like any other medical condition, it worsens with time. In this paper, two machine learning models are built using support vector machines and convolutional neural networks to detect malignant melanoma skin cancer. An open-source dataset of seven hundred malignant and benign skin cancer images was downloaded from the Kaggle website. Eighty percent of the dataset was used for training the models while twenty percent of the dataset was used for testing the models. The models were executed in Jupyter Notebook while using pandas, NumPy, and TensorFlow. The goal of this paper is to train two models using the same dataset, hardware specifications, and software environment to ascertain the individual accuracies of the models in skin cancer detection and foster a fair comparison.

  Key-Words / Index Term
  Machine Learning; Convolutional Neural Networks; Support Vector Machines; Skin Cancer Detection

  References
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  [4] G. N. K. Babu and V. J. Peter, “Skin Cancer Detection Using Support Vector Machine With Histogram of Oriented Gradients Features,” ICTACT J. Soft Comput., vol. 6956, no. January, pp. 2301–2305, 2021, doi: 10.21917/ijsc.2021.0329.
  [5] P. S. Gaikwad, A. S. Shete, M. H. Patil, and A. S. Rane, “and Engineering Trends Skin Cancer Detection Using Image Processing,” vol. 6, no. 1, pp. 72–76, 2021.
  [6] M. A. Arasi, E.-S. A. El-Dahshan, E.- Sayed, M. El-Horbaty, and A.-B. M. Salem, “Malignant Melanoma Detection Based on Machine Learning Techniques: A Survey,” Egypt. Comput. Sci. J., vol. 40, no. 03, pp. 1110–2586, 2016, [Online]. Available: http://www.ecsjournal.org/Archive/Volume40/Issue3/1.pdf.
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  [18] T. J. Brinker et al., “Skin cancer classification using convolutional neural networks: Systematic review,” Journal of Medical Internet Research, vol. 20, no. 10. 2018, doi: 10.2196/11936.
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  [21] R. Liu, F. Nageotte, P. Zanne, M. de Mathelin, and B. Dresp-Langley, “Deep reinforcement learning for the control of robotic manipulation: A focussed mini-review,” Robotics, vol. 10, no. 1, 2021, doi: 10.3390/robotics10010022.
  [22] L. C. Leonard, “Web-Based Behavioral Modeling for Continuous User Authentication (CUA),” in Advances in Computers, vol. 105, 2017.
  [23] P. Gokila Brindha, R. R. Rajalaxmi, S. Kabhilan, C. Sangitkumar, and L. Sanjeevan, “Comparative study of SVM and cnn in identifying the types of skin cancer,” J. Crit. Rev., vol. 7, no. 11, 2020, doi: 10.31838/jcr.07.11.117.

  Citation
  Christopher Ebuka Ojukwu, "Melanoma Skin Cancer Detection Using Support Vector Machines and Convolutional Neural Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.9-12, 2021

• Open Access   Article

  Using of AODV Protocol to Develop Trust-Based Routing Algorithms in Wireless Sensor Networks

  Musa Mojarad, Sayed Ebrahim Mousavipoor, Ali Kohan Mansourkhani

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.13-19, Dec-2021

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  Abstract
  Wireless sensor networks (WSNs) are the most widely used field of high-potential network generations in unpredictable and dynamic environments. However, these networks are vulnerable due to their open media, changing and dynamic topology and routing algorithms. Case networks and wireless networks support various features such as dynamic self-organization, self-configuration, self-correction, easy maintenance, high scalability and reliable services. Despite the many features mentioned, this network is very susceptible to damage caused by attacks. In this paper, an energy-aware routing algorithm for WSNs is presented that uses level-to-level clustering. This technique is used to increase the network lifetime, where the network area is divided into different levels according to the radio range of the nodes and clustering is done separately in each level. This routing method is developed based on the AODV routing protocol and uses a multi-path routing technique. The simulation was performed by MATLAB software in the presence of a DoS attack. The experimental results show that the evaluation criteria in the proposed algorithm such as throughput and network lifetime against DoS attack have improved compared to other similar algorithms.

  Key-Words / Index Term
  AODV Protocol; Wireless Sensor Networks; Clustering; Trust-Based Routing

  References
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  Citation
  Musa Mojarad, Sayed Ebrahim Mousavipoor, Ali Kohan Mansourkhani, "Using of AODV Protocol to Develop Trust-Based Routing Algorithms in Wireless Sensor Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.13-19, 2021

• Open Access   Article

  A New QoS-Oriented Ant-Colony-Based Cluster Head Selection Protocol using Fuzzy Logic in Mobile Ad Hoc Networks

  GATETE Marcel, HARUBWIRA Flaubert

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.20-34, Dec-2021

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  Abstract
  A Mobile Ad hoc Network is a type of wireless network that comprises a collection of wireless nodes that can connect on the fly (W. Castellanos, J. C. Guerri, P. Arce, 2016). Dynamic network topology and link variations are the issues with MANET and QoS (Quality of Service) requirements are not fully supported. To achieve high QoS in MANET, the location and mobility of nodes are some of the important factors that need to be taken into consideration. The cluster-based algorithm is required to provide one of the best solutions to this problem. The fuzzy logic approach is also needed to provide us with the method to find the degree of trustiness of the node (S. Biradar and P. Kulkarni, 2015). In this study, we propose a QoS-oriented and ant-colony-based cluster head selection protocol using ACO and Fuzzy Logic technique (QAC). We have to first form the cluster, pick a cluster head node among other nodes using the cluster formation technique; this is done using the Ant-colony optimization (ACO) algorithm, the aim is to choose a node with a high energy level, better trusted than others. The experiments were evaluated using the NS-2 simulator which compared the new protocol (QAC) with the ones existing in Literature namely Quality of Service-Ad-hoc On-demand Distance Vector [QoS-AODV], Bandwidth aware multi-path routing protocol in mobile Ad Hoc networks [BMR], and Dynamic Multi-Path Source Routing Method [MSR]. Different scenarios were studied applying various prominent routing metrics such as PDR, End-to-End Delay, and throughput. The new scheme outperformed the existing ones as it proved to be efficient in transmitting both ordinal and multimedia data. This research is beneficial to the development of science and wireless users in general.

  Key-Words / Index Term
  MANET, Energy, Mobility, Clustering, ACO with Fuzzy Logic, QoS, Routing

  References
  [1] V. Park and M. S. Corson, “A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks”, In Proc. INFOCOM, Kob, 1997
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  [5]Elizabeth M. Royer Charles Perkins and Samir R. Das, “Quality of Service for Ad Hoc On-Demand Distance Vector Routing”, In Internet-Draft, draftietf-manet-aodvqos-00.txt, Work in Progress, 2015.
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  [8] Sahaya Rose Vigita, and Golden Julie, “Reliable Link-Based Routing Protocol for Highly Dynamic Mobile Adhoc Networks”, International Journal of Engineering Trends and Technology, Vol.4, PP.1636-1642, 2013.
  [9] S. Biradar and P. Kulkarni, (2015). "An Improved Quality of Service Using R-AODV Protocol in MANETs," 3rdInternational Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, 2015, pp. 402-407.
  [10] M. Hay, B. Saeed, C. H. Lung, T. Kunz and A. Srinivasan, (2013). "Network Coding and Quality of Service metrics for Mobile Ad-hoc Networks," 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, pp. 521-526.
  [11] Pradeep, B.S. and Soumya S., “A new method for load balancing and QOS in on demand protocols – in the MANET’s perspective”, International Journal of Advanced Networking and Applications, Vol. 1, No. 4, pp.275–281, 2010.
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  Citation
  GATETE Marcel, HARUBWIRA Flaubert, "A New QoS-Oriented Ant-Colony-Based Cluster Head Selection Protocol using Fuzzy Logic in Mobile Ad Hoc Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.20-34, 2021

• Open Access   Article

  A perspective on natural potential compounds against SARS COV-2: The case of Bioflavonoids against 3C-like proteinase (3Cl-pro) and the role of Benzylisoquinoline Alkaloids, (BIAs), against SARS-CoV-2 Spike

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

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.35-40, Dec-2021

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  Abstract
  In this paper, we have investigated two different typology natural compounds, by rapid molecular docking approach, using Autodock Vina with Pyrx Software. The main idea was focused to evaluated different bioflavonoids, against SARS COV-2 3C-like proteinase, (3Cl-pro). From our results of Molecular Docking, in the Ligand Binding Site pocket, we have proposed six active flavonoids, equipped with high Binding Energy Score (kcal/mol), about -10 kcal/mol-1. They are Sequoiaflavone, Bilobetin, Cupressuflavone, Amentoflavone, Ginkgetin, and Theaflavin, respectively, potentially useful against 3Cl-pro. Moreover, we have also performed another category, equipped with anticancer properties. They are Benzylisoquinoline Alkaloids ( BIAs), in which they are potentially active against SPIKE-SARS-CoV-2 RBD, demonstrating that BIAs exhibited significantly higher binding Energy values of ca -10; -11 kcal mol-1, in the Receptor Binding Domain of Spike Glycoprotein.

  Key-Words / Index Term
  Docking; Benzylisoquinoline Alkaloids; Bioflavonoids

  References
  [1] Hu, B., Guo, H., Zhou, P., & Shi, Z. L.” Characteristics of SARS-CoV-2 and COVID-19”, Nature reviews. Microbiology, Vol. 19, Issue.3, pp.141–154. 2021. https://doi.org/10.1038/s41579-020-00459-7.
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  Citation
  I.V. Ferrari, M. Di Mario, R. Narducci, A. Bracco, P. Patrizio, "A perspective on natural potential compounds against SARS COV-2: The case of Bioflavonoids against 3C-like proteinase (3Cl-pro) and the role of Benzylisoquinoline Alkaloids, (BIAs), against SARS-CoV-2 Spike," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.35-40, 2021

• Open Access   Article

  Network Performance and Security Analysis of Software Defined Networking

  Ayman Haggag, Dalia Hanafy

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.41-47, Dec-2021

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  Abstract
  Software-Defined Networks (SDN) have gained a great interest among both academic and industry professionals. As this direction has not yet reached maturity, there is still a lot of research needed to help make it a dominant industrial technology choice. This paper presents the current status and identifies the future trends of SDN networks. Several SDN network topologies are implemented using Mininet and various benchmarking tools are applied for performance evaluation. Simulation results show that the global view of the SDN controller greatly enhances network performance at heavy network traffic loads and allows for efficient load balancing. On the other hand, the SDN controller represents a single point of attack vulnerability that requires special security precautions against DoS and DDoS attacks.

  Key-Words / Index Term
  Software-Defined Networks; Open flow; Network Management

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  Citation
  Ayman Haggag, Dalia Hanafy, "Network Performance and Security Analysis of Software Defined Networking," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.41-47, 2021

• Open Access   Article

  Survival Model of Prostate Cancer Patients Using Machine Learning Neural Networks Techniques

  Shreya Chaudhary, Paromita Datta

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.48-54, Dec-2021

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  Prostate cancer is the most common cancer in men in the United States. Death in prostate cancer patients is often related to medical conditions other than prostate cancer itself. Hence, it is important to predict survival so personalized care can be offered to the patients. The researcher developed a simple and credible model for estimating all cause-mortality in patients with prostate cancer using inputs of age, race, comorbidities, and baseline PSA. The researcher used a de-identified database of approximately 1000 patients with prostate cancer diagnosed between 1995-2016. After cleaning the data and omitting confounding variables, the database contained approximately 800 patients. The researcher ran different statistical and computer models to develop an accurate model to determine survival. This was done by breaking the dataset into training and testing subsets. The best algorithm was neural network regression with a Stochastic Gradient Descent optimizer and mean absolute error loss. This model had a mean squared error of approximately 1.75 months. Finally, the researcher created an intuitive user interface for doctors to use at bedside. The researcher believes that this customized survival estimation will provide valuable information to oncologists on top of the traditional TNM cancer staging. This can guide which patient needs aggressive therapy versus not, hence protecting patients from unnecessary side effects, improving quality of life, and saving health care dollars. Integrating Artificial Intelligence in medical decision-making can lead to the development of personalized care plans for cancer patients based on age, ethnicity, and co-morbidities.

  Key-Words / Index Term
  Prostate Cancer, Cancer, Survival, Neural Networks, Linear Regression, Artificial Intelligence, Machine Learning, Statistics

  References
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  Citation
  Shreya Chaudhary, Paromita Datta, "Survival Model of Prostate Cancer Patients Using Machine Learning Neural Networks Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.48-54, 2021

• Open Access   Article

  Statistical Analysis of Effects of Fabric Thickness, Loop Shape Factor, Fabric Tightness Factor and Aerial Weight on Water Vapor Permeability of Single Jersey Polyester Knitted Fabric

  Neway Seboka Debele

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.55-62, Dec-2021

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  Abstract
  on a daily basis, human being is subjected to different activities. Thus, generation of sweat will occur as the activities are carried out. This will make us uncomfortable. So as to minimize this problem the current research tries to analyze and correlate the effects of fabric thickness, loop shape factor, tightness factor, and fabric aerial weight on water vapor permeability of single jersey polyester knitted fabrics. For conducting the study, sample polyester knitted fabrics have been purchased from local market based on the differences of the stated factors. For statistical analysis, Design expert software version 11 is used. Following Box Behnken design (BBD), 30 experimental runs with 6 center points have been conducted. Accordingly, the experimental analysis shows fabric aerial weight, thickness, tightness factor and loop shape factor significantly affects water vapor permeability of the single jersey polyester knitted fabrics. In addition, the combined effect of fabric aerial weight & tightness factor and the interaction of thickness with tightness factor also affect water vapor permeability significantly. The developed 3D surface graph shows all the stated factors are having an inverse relation with the water vapor permeability behavior of polyester single jersey fabric. Using a confidence level of 95%, the actual versus predicted values are plotted showing the model is adequate and fitting. The calculated correlation coefficient between the actual and predicted value of water vapor permeability is 86.23%. For determination of an optimized water vapor permeability value of the fabrics, regression equation is developed on the basis of actual factors.

  Key-Words / Index Term
  Single jersey, fabric aerial weight, thickness, tightness factor, loop shape factor, water vapor permeability, BBD

  References
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  [10]. Uttam, D., Active Sportswear Fabrics. Int. J. IT Eng. Appl. Sci. Res. vol. 2, pp. 34–40. 2013.
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  [12]. Mohamed Ghaith Chakroun, Sofien Benltoufa, Faten Fayala , The effect of fabric’s structure on the breathability and the drying rate properties , COMMUNICATIONS IN DEVELOPMENT AND ASSEMBLING OF TEXTILE PRODUCTS, Vol. 2 No. 1, pp. 61-69, 2021. DOI 10.25367/cdatp.2022.2.p61-69.
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  [15]. Neway Seboka Debele, "Determination of effect of input yarn tension on weight (GSM) of single jersey knitted fabrics using ANOVA model," International Journal of Scientific Research in Multidisciplinary Studies, Vol.7, Issue.11, pp.35-41, 2021.
  

  Citation
  Neway Seboka Debele, "Statistical Analysis of Effects of Fabric Thickness, Loop Shape Factor, Fabric Tightness Factor and Aerial Weight on Water Vapor Permeability of Single Jersey Polyester Knitted Fabric," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.55-62, 2021

• Open Access   Article

  Evolutionary Training of Binary Neural Networks by Genetic Algorithm

  Hidehiko Okada

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.63-68, Dec-2021

  Abstract

  Full-Text HTML

  References

  Citation

  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) to the training of binary neural networks and evaluates its ability. This paper applies Genetic Algorithm (GA), another instance of evolutionary algorithms, and compares GA with ES. The experimental results with a classification task revealed that GA could also optimize parameter values well so that the trained model accurately classified both trained and untrained data. No significant difference was observed between classification accuracies by GA and ES.

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

  References
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  [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.
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  [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.
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  Citation
  Hidehiko 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.63-68, 2021

• Open Access   Article

  Telatinib, as potential Drug in Structure of Plasmodium falciparum

  Ivan Vito Ferrari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.6 , pp.69-71, Dec-2021

  Abstract

  Full-Text HTML

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  Citation

  Abstract
  Thioredoxin reductase (TrxR) is crucially involved in redox homeostasis and essential for Plasmodium falciparum. This communication, reported a first-time important Docking study by in Silico approach, using AutoDock Vina. After a selective analysis of over 300 drugs, processed with Pyrx (a Virtual Screening software into the active site of protein ( ID PDB 4J56 Thioredoxin reductase 2 Chain A), it investigated the excellent value of Binding Energy of Telatinib estimated by Pyrx software, ca. -12 kcal/mol. These results are comparable to the crystallized ligand FAD ( Flavin-Adenine-Dinucleotide), situated in the above-mentioned protein.

  Key-Words / Index Term
  Autodock Vina, and Telatinib

  References
  [1] Fritz-Wolf, K., Jortzik, E., Stumpf, M., Preuss, J., Iozef, R., Rahlfs, S., and Becker, K.” Crystal structure of the Plasmodium falciparum Thioredoxin reductase–Thioredoxin complex”, Journal of molecular biology, Vol.425, Issue.18, pp.3446-3460, 2013.
  [2] Antinori, S., Galimberti, L., Milazzo, L., and Corbellino, M. “Biology of human malaria plasmodia including Plasmodium knowlesi”,Mediterranean journal of hematology and infectious diseases, Vol.4, Issue. 1, pp. e2012013, 2012.
  [3] Jortzik, E., and Becker, K. “ Thioredoxin and glutathione systems in Plasmodium falciparum”, International Journal of Medical Microbiology, Vol. 302, Issue. 4-5, pp.187-194, 2012.
  [4] 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.
  [5] Fritz-Wolf, K., Jortzik, E., Stumpf, M., Preuss, J., Iozef, R., Rahlfs, S., and Becker, K.”Crystal structure of the Plasmodium falciparum Thioredoxin reductase–Thioredoxin complex.”.Journal of molecular biology, Vol. 425, Issue. 8,pp. 3446-3460, 2013.
  [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 flexiblity”, J. Computational Chemistry Vol. 30, Issue. 16, pp. 2785-91, 2009.
  [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.
  [8] 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.
  [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] 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] Steeghs, N., Gelderblom, H., Op`t Roodt, J., Christensen, O., Rajagopalan, P., Hovens, M., .and de Koning, E. “ Hypertension and rarefaction during treatment with telatinib, a small molecule angiogenesis inhibitor”, Clinical Cancer Research, Vol.14, Issue.11, pp.3470-3476, 2008.

  Citation
  Ivan Vito Ferrari , "Telatinib, as potential Drug in Structure of Plasmodium falciparum," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.69-71, 2021