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Cloud computing provides the architecture in which multiple virtual machines run in a single physical machine and delivering different types of services to users in pay per use bases. Due to limited resources of the physical machine, scheduling of available resources in an efficient manner is necessary for any cloud system and integration of such scheduling strategy in the system will reduce the overall execution time of the system and thus improve response time. In this paper novel scheduling algorithm is proposed IMT (Improved Makespan Time) which is built on the comprehensive study of existing scheduling algorithms such as max-min, min-min, SJF LJF Hybrid scheduling algorithm and many more. The algorithm is based on minimum execution time and maximum resource utilization strategy. Performance analysis of the proposed algorithm is carried out by comparing the execution time of the algorithm with other algorithms specified above using workflowsim simulation tool. Simulation results show that the proposed scheduling algorithm over performs existing algorithms in term of completion time.

CPU Utilization, Scheduling, task allocation, time optimization

[1] Chingrace Guite, Kamaljeet Kaur Mangat, “A Study on Energy Efficient VM Allocation in Green Cloud Computing”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.37-40, 2018.
[2] Anjum Mohd Aslam, Mantripatjit Kaur, “A Review on Energy Efficient techniques in Green cloud: Open Research Challenges and Issues”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.44-50, 2018.
[3] Muthucumaru Maheswaran, Shoukat Ali, Howard Jay Siegel, Debra Hensgenand Richard F. Freund, “Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems”, Journal of Parallel and Distributed Computing –ELSEVIER.
[4] Santhosh, B., and D. H. Manjaiah. "A hybrid AvgTask-Min and Max-Min algorithm for scheduling tasks in cloud computing." Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2015 International Conference on. IEEE, 2015.
[5] Alworafi, Mokhtar A., et al. "An Enhanced Task Scheduling in Cloud Computing Based on Hybrid Approach." Data Analytics and Learning. Springer, Singapore, 2019. 11-25.
[6] Sarvabhatla, M., Konda, S., Vorugunti, C. S., &Babu, M. N. “A Dynamic and Energy Efficient Greedy Scheduling Algorithm for Cloud Data Centers.” In 2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 47-52). IEEE.
[7] Er-raji, N., Benabbou, F., &Eddaoui, A. “A New Task Scheduling Algorithm for Improving Tasks Execution Time in Cloud Computing. In Proceedings of the Mediterranean Symposium on Smart City Applications” (pp. 298-304). Springer, Cham.- 2017.
[8] Seth, S., & Singh, N. “Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems”. International Journal of Information Technology, 1-5.
[9] N. Rodrigo, Anton Beloglazov, and Rajkumar Buyya, “CloudSim: A toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning algorithm,” Journal Software-Practice & Experience, Volume 41, Issue 1, India, January 2011.
[10] Weiwei Chen, “WorkflowSim: A toolkit for simulating Scientific Workflows in Distributed Environment” IEEE 8th International Conference, E-Science, United States, October, 2012.

Nidhi P. Galolia, Arvind Meniya , "Optimizing scheduling performance through time slice management based on Max Min strategy in cloud system," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.874-877, 2019.

¬¬¬¬In the recent digital world, the amount of processing of videos is increasing rapidly. For this purpose, video retrieval systems are dominating today’s world. Video retrieval systems include proper analysis of videos for appropriate retrieval. The retrieval of videos can be done based on the text or annotation attached to it. But retrieval based on the content has become more influencing over text-based retrieval as it describes a video in a much better way than described by text. Content-based video retrieval systems analyze the contents of a video such as colour, texture, shape, etc. This system involves many stages with multiple techniques for each one as per the survey done till now. To analyze the different techniques, multiple datasets have been used containing videos of different categories. The best technique applied at each stage for frame extraction, feature extraction, classification and retrieval of videos makes the system more accurate and efficient.

Video retrieval, Key-frame extraction, SURF, SIFT, BRISK, SVM

[1] Prof. Rahul Gaikwad and Jitesh R. Neve, “A Comprehensive Study in Novel Content Based Video Retrieval Using Vector Quantization over a Diversity of Color Spaces”, in the Proceedings of 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication.
[2] Prof. Dipak R. Pardhi and Jitesh R. Neve, “Performance Rise in Novel Content Based Video Retrieval using Vector Quantization”, in the Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016.
[3] Andre Araujo And Bernd Girod , “Large-scale Video Retrieval Using Image Queries”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 28, No. 6, June 2018.
[4] Aasif Ansari, Muzammil H Mohammed, “Content-based video retrieval systems-methods, techniques, trends and challenges”, in the Proceedings of International Journal of Computer Applications (0975 – 8887) Volume 112 – No. 7, February 2015.
[5] Dr. Parag Kulkarni, Bhagyashri Patil, Bela Joglekar, “An effective content based video analysis and retrieval using pattern indexing techniques”, in the Proceedings of 2015 International Conference on Industrial Instrumentation and Control, College of Engineering Pune, India, May 28-30, 2015.
[6] Mohd.Aasif Ansari, HemlataVasishtha, “Content-based video retrieval systems performance based on multiple features and multiple frames using SVM”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 8, 2016.
[7] K.S.Thakre, A.M.Rajurkar, R.R.Manthalkar, “Video Partitioning and Secured Keyframe Extraction of MPEG Video”, in the Proceedings of International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA
[8] Jun Xu , Tao Mei , Ting Yao and Yong Rui, “MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”
[9] Ashwini B, Verina, Dr.Yuvaraju B N, “Feature Extraction Techniques for Video Processing in MATLAB”, International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization),Vol. 4, Issue 4, April 2016.
[10] Stefan Leutenegger, Margarita Chli and Roland Y. Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints”
[11] Wikipedia contributors. (2019, February 19). Scale-invariant feature transform. In Wikipedia, The Free Encyclopedia. Retrieved 08:53, February 28, 2019, from https://en.wikipedia.org/w/index.php?title=Scale-invariant_feature_transform&oldid=884107628
[12] AI Shack, SIFT algorithm steps from http://aishack.in/tutorials/sift-scale-invariant-feature-transform-introduction/
[13] Wikipedia contributors. (2017, August 20). Speeded up robust features. In Wikipedia, The Free Encyclopedia. Retrieved 08:58, February 28, 2019, from https://en.wikipedia.org/w/index.php?title=Speeded_up_robust_features&oldid=796404867
[14] Sledevič, Tomyslav & Serackis, Artūras. (2012). SURF algorithm implementation on FPGA. 291-294. 10.1109/BEC.2012.6376874.
[15] Raj Prasanna Kumar, Raghu & Muknahallipatna, Suresh & McInroy, John. (2016). “An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications”. Journal of Computer and Communications. 04. 18-50. 10.4236/jcc.2016.417002.

Nagariya Maitree, U. K. Jaliya, M. S. Holia, "A Survey on Content-Based Video Retrieval Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.878-883, 2019.

Speech processing is one of the important application area of digital signal processing. For this purpose, speaker recognition is dominating today’s world. Speaker recognition is a process of speaker identification and speaker verification refers to specific tasks. Speaker recognition is the process of identifying a speaker by his/her speech samples. By extracting the speaker-specific features from the speech samples, the recognition task can be done. Speaker recognition technique is one of the most helpful recognition techniques in today world. It is very important to efficiently work without fail of Recognition system and identify correct person. Speaker recognition is to extract, characterize and recognize the information about speaker identity. This system involves many stages with multiple techniques for each. In this paper, the performance of Mel Frequency Cepstral Coefficient (MFCC), VQ vector quantization and Linear Prediction Coding (LPC) speaker recognition system using method. It is found that the MFCC is offer better recognition rate as contrasted to BFCC using VQ vector quantization as speaker modeling technique. The best technique in each stage makes the system more accurate and efficient.

Speaker Recognition, Speaker identification and verification, vector quantization, Mel Frequency

[1] Mahaveer Chougala1’ Novel Text Independent Speaker Recognition Using LPC Based Formants’ 978-1-4673-9939-5/16/$31.00 ©2016 IEEE
[2] Md. R. Hasan, M. Jamil, Md. G. Rabbani, Md. S. Rahman, “Speaker Identification using Mel Frequency Cepstral Coefficients,” Third International Conference on Electrical & Computer Engineering ICECE, Dhaka, 2004
[3] A. Zulfiqar, T. Enriquez, “A Speaker Identification System Using MFCC Features with VQ Technique,” Third International Symposium on Intelligent Information Technology Application, vol.3, pp.115 – 118, Mar. 2009.
[4] Kinnunen T.and Kärkkäinen I., "Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification". Joint IAPR Int. Workshop on Statistical Pattern Recognition (SPR`2002), Windsor, Canada, 681-688, August 2002.
[5] Dorra Gargouri, Med Ali Kammoun, “A Comparative Study of Formant Frequencies Estimation Techniques”, Proceedings of the 5th WSEAS International Conference on Signal Processing, Istanbul, Turkey, May 27-29, 2006.

Parmar Dharmistha R, "A Survey on Speaker Recognition with Various Feature Extraction Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.884-887, 2019.

In this research, Genetic Algorithm is used for feature selection. Genetic Algorithm has been combined with local search, named as Memetic Algorithm and an algorithm is proposed and named as Compound Featuristic Genetic Algorithm. Next, based on, Class Dependent Feature Subset Selection, an algorithm is proposed namely, Core Featuristic Genetic Algorithm. The performance analyses of existing and proposed feature selection algorithms are functioned on heart dataset to predict the heart disease with minimum number of features. Finally, Fuzzy Decision Tree, Fuzzy Naive Bayes and Fuzzy Neural Networks are applied to the reduced set of the Heart dataset, obtained for classification accuracy.

Feature selection, Genetic Algorithm, Compound Featuristic Genetic Algorithm, Core Featuristic Genetic Algorithm, Fuzzy Decision Tree, Fuzzy Naive Bayes and Fuzzy Neural Networks

[1] A.Anushya, A. Pethalakshmi, D.Sheela Jeyarani, R.Raja Rajeswari, “A Comparative Study of Decision tree and Naive Bayesian classifiers on medical datasets”, Proceedings of the International Conference on Computing and Information Technolgy, 2013.
[2] A.Anushya, A.Pethalakshmi, “A Comparative Study of Fuzzy Classifiers on Heart Data “Proceedings of the 3rd International Conference on Trendz in Information Sciences and Computing (TISC-2011), 978-1-4673-0131-2/11, IEEE Digital Library, 2011.
[3] A.Pethalakshmi, A. Anushya, “A comparative analysis of genetic based feature selection on heart data”, International Journal of Computational Intelligence and Informatics, Vol. 2, No. 2, June – September, 2012.
[4] A.Pethalakshmi, A.Anushya, “Dynamic Feature Selection by Genetic on Medical Data”, Sub-saharan Journal Computer Science , Vol. 1, No. 1, (ISSN: 2307-9169), 2013.
[5] A.Pethalakshmi, A.Anushya, ”Effective feature selection via Featuristic genetic on heart data”, International Journal of Computational Intelligence and Informatics, Vol. 2: No. 1, April – June, 2012.
[6] Anbarasi.M, E. Anupriya and N.CH.S.N.Iyengar, “ Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm ,” International Journal of Engineering Science and Technology Vol. 2(10), 2010, 5370-5376, 2010.
[7] Asha Rajkumar and Mrs. G.Sophia Reena, “Diagnosis Of Heart Disease Using Datamining Algorithm”, GJCST, Vol. 10, Issue 10, pp: 38-43, 2010.
[8] Bala Sundar V, T DEVI, N SARAVANAN, “ Development of a Data Clustering Algorithm for Predicting Heart”, International Journal of Computer Applications (0975 – 888) International Journal of Computer Applications (0975 – 888), Vol 48, No.7, 2012.
[9] Dr. K. Usha Rani, “Analysis of Heart Diseases Dataset using Neural Network Approach”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5, 2011.
[10] E.P.Ephzibah, V. Sundarapandian, “A Neuro Fuzzy Expert System for Heart Disease Diagnosis”, Computer Science & Engineering: An International Journal (CSEIJ), Vol.2, No.1, 2012.
[11] G. Subbalakshmi, K. Ramesh, M. Chinna Rao, “Decision Support in Heart Disease Prediction System using Naive Bayes,” Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2, No. 2, pp: 170-176, 2011.
[12] J. Anitha, C. Kezi Selva Vijila, D. Jude Hemanth, “A hybrid Genetic Algorithm based Fuzzy Approach for Abnormal retinal Image Classification”, International Journal of Cognitive Informatics and Natural Intelligence, Vol.4, No.3, pp: 29-43, 2010.
[13] Javed, K. Babri, H.A., Saeed, M, “ Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data”, IEEE Transactions on Knowledge and Data Engineering, Vol: 24 , Issue: 3, pp: 465 -477, 2012.
[14] K. Rajeswari, V. Vaithiyanathan, P. Amirtharaj, “ Prediction of Risk Score for Heart Disease in India Using Machine Intelligence”, International Conference on Information and Network Technology(IPCSIT), Vol.4, 2011.
[15] K.S.Kavitha, K.V.Ramakrishnan, Manoj Kumar Singh, “Modeling and design of evolutionary neural network for heart disease detection”, International Journal of Computer Science Issues, Vol. 7, Issue 5, September, 2010.
[16] K.Srinivas, G.Raghavendra Rao, A.Govardhan, “Analysis of Attribute Association in Heart Disease Using Data Mining Techniques”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue4, 2012.
[17] K.Srinivas, G.Raghavendra Rao, A.Govardhan, “Analysis of Attribute Association in Heart Disease Using Data Mining Techniques”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue4, 2012.

[18] Malin Bj¨ornsdotter & Johan Wessberg, “ A Memetic algorithm for selection of 3D clustered features with applications in neuroscience”, International Conference on Pattern Recognition, IEEE, 2010.
[19] P. Santhi, V. Murali Bhaskaran, ”Improving the Performance of Data Mining Algorithms in Health Care Data”, International Journal of Computer Science and Technology, IJCST Vol. 2, Issue 3, ISSN : 2229 – 4333 ( Print), ISSN : 0976- 8491 (Online), 2011.
[20] R.Bakyalakshmi, Mr.N.Krishna Kumar , S.Karthika, M.Maheswari, “ Minimizing Rules for Medical Dataset using Hybrid Fuzzy Classifier”, International Journal of Communications and Engineering, Vol. 02, No.2, Issue: 01, March, 2012.
[21] Raj Kumar et.al, “Classifiction algorithms for Data Mining”, A Survey,International Journl of Innovations in Engineering and Technology,Vol.1, Issue 2 ,2012.
[22] S. Senthamarai Kannan, N. Ramaraj, “ A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm” Contents lists available at ScienceDirect Knowledge-Based Systems, Vol. 23, pp: 580–585, Elsevier, 2012.
[23] S.Vijiyarani , S.Sudha, “An Efficient Classification Tree Technique for Heart Disease Prediction”, Intn mernational Conference on Research Trends in Computer Technologies (ICRTCT - 2013) Proceedings published in International Journal of Computer Applications® (IJCA) (0975 – 8887), 2013.
[24] Zhou Nina, Lipo Wang, “Class-Dependent Feature Selection for Face Recognition, Advances in Neuro-Information Processing”, Lecture Notes in Computer Science Volume 5507, 551-558, 2009.
[25] Dipti.N.Punjani et.al, “ A comprehensive study of various classification techniques in medical applications using data mining”, International journal of Computer science and Engineering, vol.6, issue 6,June,2018.

A. Anushya, "Genetic Explorations for Feature Selection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.888-892, 2019.

Entry/Exit Exam System, the government has to think about to increase the effort put forth to improve the quality of the education and strengthen the evaluation process. There is no need of these kinds of experiments to impose on students. There is only need to strengthen the Higher Education evaluation System so that the last qualifying examination marks could be considered as final outcomes of students learning.

Entry / Exit Exam, Policy on Education, Curriculum, Evaluation Policy, Experiments on students

[1]. American Federation of Teachers. (1995) Setting Strong Standards: AFT’s criteria for judging the quality and usefulness of student achievement standards. Washington, D.C.: American Federation of Teachers, 1-12.
[2]. Bishop, John. (1993). “The Impact of Academic Competencies on Wages, Unemployment and Job Performance.” Carnegie/Rochester Con- ference Series on Public Policy, edited by Burton Malkiel and Charles Plosser, Vol. 3.
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[6]. Effinger, M. R., Polborn, M. K. (1999). A model of vertically differentiated education. Journal of Economics, 69, 53-69.

Panckaj Garg, "Strengthen and Respect Last Qualifying Examination Rather Than Introducing Entry/Exit Examinations," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.893-895, 2019.

In present the quickening pace of technological change development Wireless technology is growing in broad, there are many research issues arises in the wireless technology, the fundamental requirement in wireless is routing. Wireless mesh network is the emerging technology in which routing is the fundamental characteristic of the wireless network. Wireless mesh networks are very much capable of providing internet access anywhere anytime for stationary or mobile hosts at low costs both for network operators and customers. In wireless networks Routing is the necessary fundamental characteristics .In WMNs routing is the challenging research area due to the occurrence of unexpected changes in the wireless environments. There exist two different ways to enhance the performance of routing protocols in wireless networks. In wireless mesh network have to improve the metric used in the selection path , to modify the routing algorithms by considering new characteristics of the network and can apply cross-layer approach by merging the characteristics of two or more layers. This paper discussed about various types of routing protocols that are used in the wireless mesh networks along with metrics that are used in wireless mesh networks.

Reactive Routing, Proactive routing, Routing Metrics, Routing Protocols, WMNs

[1] E R Kulvir singh and Er. Unny Behal, “A review on routing protocols in wireless mesh networks”, Internal journal of application or innovation in Engineering and Management, Vol 2, Issue 2,Feb 2013.
[2] Zainab Senan Mahmod et.al, “Review and Evaluation of the proposed wireless mesh routing protocols, Proc. Of International conference on computer and communication engineering, 2008 IEEE, pp.894-897.
[3] Shubat S. Ahmeda and Eman A. Essied, “Routing Protocols for Wireless Mesh Networks”, Lecture Notes on Software Engineering, Vol. 1, No. 1, February 2013.
[4]Eric Rozner et.al, “SOAR: Simple Opportunistic Adaptive Routing Protocol for Wireless Mesh Networks”, IEEE Transactions On Mobile Computing, 2009.
[5] Ahmed Al-Saadi, Rossitza Setchi,” Routing Protocol for Heterogeneous Wireless Mesh Networks”, IEEE Transactions on Vehicular Technology, DOI0.1109/TVT.2016.2518931,2015.
[6] R. Regan and 2 J. Martin Leo Manickam,”A Survey on Wireless Mesh Networks and its Security Issues”, International Journal of Security and Its Applications Vol. 10, No. 3 (2016), pp. 405-418
[7] C. E. Perkins and E. M. Royer. Ad hocon-demand distance vector routing. “WMCSA Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications”, IEEE Computer Society Washington, DC, Page 90-99, Feb. 1999.
[8] Srivani. P, “Scalable Heat Routing Protocol for Wireless Mesh Networks”, IJCSMC, Vol. 2, Issue. 7, July 2013, pg.385 – 390
[9] Baumann, Rainer et al, Simon Heimlicher, Vincent Lenders, Martin May, “HEAT: Scalable Routing in Wireless Mesh Networks Using Temperature Fields”, 2207/IEEE international symposium on a world of wireless mobile and multimedia networks. IEEE, 2007.
[10] Guoyou He, “Destination-Sequenced Distance Vector (DSDV) Protocol”, Helsinki university of technology, 2002. Pg 1-9.
[11] Hemanth Narra et al, Destination-sequenced distance vector (DSDV) routing protocol implementation in ns-3,DOI10.4108/icst.simutools.2011.245588,march 2011.
[12] Khaleel Ur Rahman Khan et al, “An Efficient DSDV Routing Protocol for Wireless Mobile Ad Hoc Networks and its Performance Comparison”, DOI 10.1109/EMS.2008.11, IEEE computer society,pg.506-511 2008.
[13] Bow-Nan Che et al, “Orthogonal Rendezvous Routing Protocol for Wireless Mesh Networks”, IEEE/ACM Transactions On Networking, Vol. 17, No. 2, April 2009.
[14] Seung-Chul M. Woo and Suresh Singh,” Scalable Routing Protocol for Ad Hoc Networks”, J. Wireless Networks (WINET), April 2, 2001.
[15] Tapodhir Acharjee, Sudipta Roy,” A Mobility-Aware Cluster Based Routing For Large Wireless Mesh Network”, International conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016.
[16] Routing Protocol for Heterogeneous Wireless Mesh Networks Ahmed Al- Saadi, Rossitza Setchi, Senior Member, IEEE, Yulia Hicks, Member, IEEE and Stuart Allen.

Prema K.N., Ushadevi M.B., "Routing Protocols for Wireless Mesh Networks: A survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.896-901, 2019.

Data mining is broadly utilized in assorted territories. There is various business data mining framework accessible today but there are numerous difficulties in this field. This paper explains use of new terminology (decision tree) in educational data mining. Decision tree are used the data comparison with data mining capability. It helps prior in distinguishing the dropouts and understudies who require exceptional consideration and enable the educator to give suitable exhorting/advising.

Data Mining Applications, Decision tree, Classifications.

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[2]. U. K. Pandey, and S. Pal, “A Data mining view on class room teaching language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN: 1694-0814, 2011.
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[6]. Z. J. Kovacic, “Early prediction of student success: Mining student enrollment data”, Proceedings of Informing Science & IT Education Conference 2010
[7]. Jadhav, R. J. (2011). Churn Prediction in Telecommunication Using Data Mining Technology. International Journal of Advanced Computer Science and Applications - IJACSA, 2(2), 17-19.
[8]. Firdhous, M. F. M. (2010). Automating Legal Research through Data Mining. International Journal of Advanced Computer Science and Applications - IJACSA, 1(6), 9-16.

Brijesh Kumar Bhardwaj , "A Comparative Study on Data Mining Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.902-904, 2019.

Demand of enormous bandwidth is explosively increasing nowadays. It can be fulfilled with the use of wavelength division multiplexing technology(wdm). Using optical wdm network for data transfer applications involves establishing the light paths for the traffic requests. In this paper, traffic scheduling of irregular requests is done with the use of the hybrid heuristic technique of flower pollination and simulated annealing algorithm. Also, the effect of number of wavelength channels present is analyzed using full wavelength conversion and nil wavelength conversion capability at the intermediate nodes. The results obtained after extensive simulation proved that the wavelength count of 16 is the most optimum count of wavelength resulting in proper resource optimization. Also, the hybrid of flower pollination and simulated annealing produces more promising results relative to chaotic particle swarm optimization in the context of the blocking probability for irregular traffic.

Irregular, Blocking Probability, Utilization, Channels, Bandwidth

[1] F. Farahmand, X. Huang and J.P. Jue, “Efficient Online Traffic Grooming Algorithm in WDM Mesh Network with Drop and Continue Node Architecture”, In the Proceedings of First International Conference on Broadband networks,USA, pp. 1-10,2004.
[2] J. Triay, J. and C. Cervello- Pastor, “An ant based Algorithm for Distributed Routing and Wavelength Assignment in Dynamic Optical Networks”, IEEE Journal on Selected Areas in Communications, Vol. 28, Issue 4, pp. 542-55,2010.
[3] A. Wason and R.S. Kaler, “Wavelength Assignment Problem in optical WDM networks”, International Journal of Computer Science and Network Security, Vol 7,No. 4,pp 27-31,2007.
[4] V. Khosia, “A comprehensive Review of Recent Advancement in Optical Communication Networks”, International Journal of Computer Sciences & Engineering, Vol. 6, Issue 9, pp. 617-626,2018. Doi: 10.26438/ijcse/v6i9.617626
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[6] A. Hassan and C. Phillips, “Chaotic Particle Swarm Optimization for Dynamic Routing and Wavelength Assignment in all optical WDM networks”, In the Proceedings Of International Conference On Signal processing And Communication System, Omaha, NE, pp. 1-7,2009.
[7] D. Bisbal, I.D. Miguel, F. Gonzelez, J. Blas, J.C. Aguado, P. Fernadez, J. Duran , R. Duran,R. M. Lorenzo, E.J.Abril and M. Lopez, “Dynamic Routing and Wavelength Assignment in optical networks by means of genetic algorithms”, Photonic Network Communication,Vol. 7,Issue 1, pp.43-58,2004.
[8] S.H. Ngo, X. Jiang and S. Horiguchi, S., “An Ant Based Approach for Dynamic RWA In Optical WDM Networks”, Photonic Network Communications, 11(1), pp. 39-48,2006.
[9] R.M. Krishanaswamy, K.N. Sivaranjan, “Algorithms for routing and wavelength assignment based on solutions of LP relaxations”,IEEE Communications Letters,Vol. 5, Issue 10,pp. 435-437,2001.
[10] G. Shen, S.K. Bose, T.H. Cheng and T.Y. Chai, “Efficient heuristic algorithms for light path routing and wavelength assignment in WDM networks under dynamically varying loads”, Computer Communications, Vol. 24, Issue 3-4, pp. 364-373,2001.
[11] T.K. Ramesh, N. Amrutha Lakshmi, A. Madhu, K. Saumya Ready and P.R. Vaya, “ A Proactive and Self Regulated Ant Based RWA protocol for All Optical WDM Networks”, In the Proceedings Of International Conference On Process Automation Control and Computing , India, pp. 1-5,2011.
[12] T.F. Noronha. and C.C. Ribeiro,“Routing and wavelength assignment by partition colouring”, European Journal of Operational Research, Vol. 171,Issue 3, pp 797-810,2006.
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[14] K. Christodoulopoulos, K. Manousakis and E. Varvarigos, “Offline Routing and Wavelength Assignment in Transparent WDM Networks”, IEEE/ACM Transactions on Networking, Vol. 18,Issue 5,pp. 1557-1560,2010.
[15] Y. Ye, T.Y. Chai, T.H. Chen and C. Lu, “Dynamic routing and wavelength assignment algorithms in wavelength division multiplexed translucent optical network”, Computer Communications, Vol. 29, Issue 15, pp. 2975-2984,2006.
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Harpreet Kaur and Munish Rattan, "Blocking Analysis in Optical WDM network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.905-909, 2019.

We come across the challenge of uncertainty in making decisions in various real life problems. The intelligence, analytical ability, reasoning and available information is used by us to make these decisions. The imprecise nature of the available information is the main source of uncertainty which makes our task more challenging for making decisions. The available mathematical tools like fuzzy sets, rough sets, vague sets and soft sets etc. which are in use have their own limitations with regard to different types of uncertainty. Thus depending on the kinds of uncertainty involved in a decision problem, it is necessary to explore hybrid approaches to address these issues of uncertainty in making decisions in a real environment. In this paper a doubly soft set with a single fuzzy parameter at the second level of hierarchy is proposed to address the combination of two kinds of uncertainty in a decision problem. The doubly soft set takes care of the relationship of elements of the set with the two levels of parameters involved and fuzzy set takes care of relationships among the elements of the parameters at the second level of hierarchy. The proposed model is illustrated with the help of an example of a decision problem

Fuzzy set, Soft set, Multisoftset, Decision making

[1] L.A. Zadeh, “Fuzzy set,” Information and Control., vol. 8, 1965, pp. 338–353.
[2] Pawlak, Zdzisław. "Rough sets." International Journal of Computer & Information Sciences 11(5) ,1982, pp 341-356.
[3] W.L. Gau and J.B Daniel, “Vague sets”, IEEE Transactions on Systems, Man, and Cybernetics 23 , 1993 ,pp 610–614.
[4] D. Molodtsov, “Soft set theory-first results,” Computers and Mathematics with Applications, 37, 1999, pp. 19–31.
[5] P.K. Maji, R. Biswas, and A.R. Roy, “An Application of Soft Sets in A Decision Making Problem”, Computers and Mathematics with Applications, 44, 2002, pp. 1077-1083.
[6] Bhavya Pardasani, Multi Softset for Decision Making, International Journal of Science and Research, 7 ( 11), 2018, pp 55-56.
[7] Amita Jain and K. R. Pardasani, Soft fuzzy model for mining amino acid associations in peptide sequences of Mycobacterium tuberculosis complex , Current Science , 110(4), 2016, p603-618
[8] A Jain and KR Pardasani, Fuzzy soft set model for mining amino acid associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC) , Journal of Intelligent & Fuzzy Systems 31 (1), 2016, pp 259-273
[9] A Gour and KR Pardasani , Statistical and Soft Fuzzy Set Based Analysis of Amino Acid Association Patterns in Peptide Sequence of Swine Influenza Virus , Advanced Science, Engineering and Medicine 10 (2), 2018, pp 137-144

Bhavya Pardasani, "Doubly Soft Set Model with Single Fuzzy Parameter at Second Level of Hierarchy for Making Decisions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.910-913, 2019.

The mobile applications to solve problems for us is not new to us in today’s world. One of the major problems in the field of medical today is to find a convenient way to look for blood donors and receivers in case of an emergency. This is an android application developed in order to solve this major problem. It helps you look for blood donors and receivers and track nearest hospital, fire station, ambulance and police station of the specific location in your city and connects you with them instantly in need. User can call to emergency numbers and call his home and his emergency contact person in single button click. In order to make sure the application is widely used even in remote areas the user interface has been kept extremely simple. This Application runs on the latest Android OS and has an extremely small size of 2.5Mb.

Android, Blood Management, google map

[1] Neil Smyth. (2015). Android Studio 2.2 Development Essentials. 7th ed.
[2] Sandip Mal and Kumar Rajnish, “New Quality Inheritance Metrics for Object-Oriented Design”, International Journal of Software Engineering and Its Applications, Scopus, Vol.7, No.6, pp.185-200, 2013, http://dx.doi.org/10.14257/ijseia.2013.7.6.16.
[3] Sandip Mal and Kumar Rajnish, “Applicability on weyuker’s property 9 to new inheritance metrics” International Journal of Computer Application. Vol-66, Issue-12, PP: 21-26, 2013.
[4] Sandip Mal and Kumar Rajnish, Sanjeev Kumar, “Package Level Cohesion Metric for Object-Oriented Design”, International Journal of Engineering and Technology (Engineering Journal publishers), Scopus, Vol-5, No.3, PP: 2523-2528, 2013.
[5] Sandip Mal and Kumar Rajnish, "Measuring System Complexity Using New Complexity Metric”, Software engineering: An International Journal (SeiJ), Vol. 3, no. 2, PP: 35-43, September 2013.
[6] Sandip Mal and Kumar Rajnish, “Coupling Metric for Understandability and Modifiability of a Package in Object-Oriented Design”, I.J. Information Technology and Computer Science, ISI Web Knowledge, Vol.6, No.8, PP: 72-78, July 2014 in MECS, DOI: 10.5815/ijitcs.2014.08.10.
[7] Sandip Mal and Kumar Rajnish, “New Class Cohesion Metric: An Empirical View”, International Journal of Multimedia and Ubiquitous Engineering, Scopus, Vol.9, No.6, pp.367-376, 2014 http://dx.doi.org/10.14257/ijmue.2014.9.6.35.
[8] Sandip Mal and Kumar Rajnish, “Validation of new cohesion metric against Braind properties”, Advances in Intelligent Systems and Computing Vol: 243, PP: 591-597, Springer, 2014, DOI: 10.1007/978-81-322-1665-0_58.
[9] John Horton. (2015). Android Programming for Beginners. 1st ed. Birmingham, UK.
[10] Shyam Sundaram, T. Santhanam, (2016) Classification of Blood Donors using Data Mining. Proceedings of the Semantic.
[11] Kyle Mew. (2017). Mastering Android Studio 3. 3rd ed. Available at: https://books.google.co.in/books/about/Mastering_Android_Studio_3.html?id=QpZGDwAAQBAJ&printsec=Frontcover&source=kp_read_button&redir_esc=y#v=onepage&q&f=false

Sandip Mal, Deepak Gujar, "Blood Management System Android Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.914-917, 2019.