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Association rule mining is an astoundingly basic and critical piece of data mining.It will be utilized to Figure the entrancing plans from exchange databases. Apriori count will be a champion among those for all intents and purposes built up computations from guaranteeing association rules, yet all the it require the bottleneck Previously, adequacy. In this article, we recommended a prefixed-itemset-based data structure to create visit itemset, with those help of the structure we made sense of how to improve the viability of the conventional Apriori computation.

arm, apriori, regression, improve apriori,weka data set, indwx, clustering

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[4] Lin M., Lee P. & Hsueh S. Apriori-based Frequent Itemset Mining Algorithms on MapReduce. In Proc. of the 16th International Conference on Ubiquitous Information Management and Communication (ICUIMC „12), New York,NY, USA, ACM: Article No. 76, 2012.
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[7] R.Irena Tudor, Universitatea Petrol-Gaze din ploeiesti,(2008)“Association Rule Mining as a Data Mining Technique”, Bd.Bucuresti 39, ploeiesti,Catedra de Informatica, Vol-LX,No.1.
[8] X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou,
[9] [7] algorithms in data mining,” Knowledge and Information Systems, vol. 14, no. 1, pp. 1–37, Dec. 2007.VLDB Journal2007, pp: 507-521, 2007.
[10] Li N., Zeng L., He & Shi Z. Parallel Implementation of Apriori Algorithm Based on MapReduce. In Proc. of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD „12), Kyoto, IEEE: 236 – 241, 2012.

W. Sarada, P.V. Kumar, "A novel approach for Generating Association Rules pattern matching Using improved Apriori with Regression Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1151-1155, 2018.

Internet nowadays is very important share of our day to day life solving many problems on a daily basis. It turns out to be a helping hand to human in so many ways. Among the many advantages of internet, one is sharing of knowledge. Email is that application which is used by all to fulfill the purpose of sharing information. Our email inbox contains some mails which are not required or are unwanted or whose sender is not an authorized person. These types of mails are called the Spam. To detect the spam among the required mails is one kind of hectic task. So many methods have been implemented for this. A spam could be in the form of picture or text which is very harmful for the computer. Thus, Spam has been categorized into the category of problems which occurs frequently and should be handled by the internet user with the help of some better technique. A number of methods have been designed to overcome the issue of spam messages and mail. Already implemented techniques for spam detection have been described in this paper.

Email, Heuristics, Phishing, Supervised and unsupervised learning, Spam Filter

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[20] T. A. Almeida, T. P. Silva, I. Santos, and J. M. G. Hidalgo, ‘‘Text normalization and semantic indexing to enhance instant messaging and SMS spam filtering,’’ Knowl.-Based Syst., vol. 108, pp. 25–32, Sep. 2016.
[21] Asmeeta Mali, “Spam Detection Using Baysian with Pattren Discovery”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3, July 2013.
[22] T.-J. Liu, W.-L. Tsao and C.-L. Lee, “A High Performance Image-Spam Filtering System”, 19th International Symposium on distributed Computing and Application to Business, Engineering and Science, August 2010
[23] Ann Nossier, khaled kagati, and Islam Taj-Eddin, “Intelligent Word- Based Spam Filter Detection Using Multi Neural Networks”, International Journal of Computer Science Issues, Vol 10, Issues 2, No 1, March 2013 ISSN(Print): 1694 -0814|ISSN(Online): 1664- 0784
[24] Geerthik. S and Anish .T.P, “Filtering Spam: Current Trends and Techniques”, International Journal of Mechatronics, Electrical and Computer Technology Vol. 3(8), Jul, 2013, pp 208-223, ISSN: 2305-0543 © Austrian E-Journals of Universal Scientific Organization
[25] A. M. Adrian, ‘‘A challenge response system for filtering automated SMS spam,’’ M.S. thesis, Dept. Comput. Sci. Inf. Eng., Nat. Taiwan Univ. Sci. Technol., Taipei, Taiwan, 2010.

[26] L. Cao, G. Nie, and P. Liu, ‘‘Ontology-based spam detection filtering system,’’ in Proc. Int. Conf. Bus. Manage. Electron. Inf. (BMEI), May 2011, pp. 282–284.
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K. Yadav, P. Kumaraguru, A. Goyal, A. Gupta, and V. Naik, ‘‘SMSAssas- sin: Crowdsourcing driven mobile-based system for SMS spam filtering,’’ in Proc. 12th Workshop Mobile Comput. Syst. Appl., 2011, pp. 1–6.
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Namrata, Suman, "Review Paper on Spam Detection Antiphishing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1156-1161, 2018.

Predicting Fault-proneness of software modules is the essential for cost effective test planning. Various studies have shown the importance of software metrics in predicting fault-proneness of the software.Chidamber and Kemerer (CK) metrics suite is the most widely used metrics suite for the purpose of object-oriented software fault-proneness prediction. The current paper is aimed to review various studies available in literature to predict software fault-proneness using CK metrics.

Fault-proneness,CK metrics.

[1] Yuming Zhou, BaowenXu, Hareton Leung, “On the Ability of Complexity Metrics to Predict Fault-Prone Classes in Object-Oriented Systems”, Journal of Systems and Software, Vol. 83, Issue 4, April 2010.
[2] SuraiyaParveen& R A Khan: “Fault Proneness Model for Object-Oriented Software” Design Phase Perspective. 2009 International Conference on Computer Engineering and Application.IACSIT Press, Singapore.
[3] S. Chidamber and C.kemerer, “A metrics suite for object oriented design”, IEEE Transaction on Software Engineering, Vol. 20, No. 6, June 1994.
[4] Tibor Gyimothy, Rudolf Ferenc, and IstvanSiket, “Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction”, IEEE Transaction on Software Engineering, Vol 31, No. 10, October 2005.
[5] Yuming Zhou and Hareton Leung, “Empirical analysis of object oriented design metrics for predicting high severity faults”, IEEE Transactions on Software Engineering, Vol. 32, Issue 10, 2006.
[6]K.K.Aggarwal, Yogesh Singh, ArvinderKaur, RuchikaMalhotra,“Investigating effect ofDesign Metrics on Fault Proneness in Object-Oriented Systems”, Journal of Object Technology, December 2007.
[7] Ruchika Malhotra, YogeshSingh,“On the Applicability of Machine Learning Techniques for Object Oriented Software Fault Prediction”, Department of Software Engineering, Delhi Technological University, Delhi, India. International Journal (SEIJ), September 2011.
[8] HeenaKapila, Satwinder Singh, “Analysis of CK Metrics to Predict Software Fault-Proneness using Bayesian Inference”, International Journal of Computer Applications July 2013.
[9]Alexandre Boucher, MouradBadri, “Using Software Metrics Thresholds to Predict Fault-Prone Classes in Object-Oriented Software”,2016 4th Intl Conf on Applied Computing and Information Technology.
[10] Jehad Al Dallal , SandroMorasca,“Investigating the Impact of Fault Data Completeness over Time on Predicting Class Fault-Proneness”, INFSOF 5907, 7 November 2017.
[11] Chao-Ying Joanne Peng, Kuk Lida Lee, Gary M. Ingersoll,“An Introduction To Logistic RegressionAnalysis And Reporting”, The Journal Of Educational Research, September/October 2002 ,Vol. 96,No. 1.
[12] Murat Korkmaz, SelamiGüney, ŞuleYükselYiğîter, “The Importance of Logistic Regression Implementations in the Turkish Livestock Sector and Logistic Regression Implementations/Fields”,J.Agric. Fac. HR.U., 2012, Vol.16, No.2, 25-36.
[13]ImranNaseem,RobertoTogneri,MohammedBennamoun, “Linear Regression for Face Recognition” IEEE Transactions on Pattern Analysis and Machine Intelligence,Volume: 32, Issue: 11, Nov. 2010.
[14]Priya Stephen, Suresh Jaganathan,“Linear regression for pattern recognition”, Green Computing Communication and Electrical Engineering (ICGCCEE), 16 October 2014.
[15]GuoQiang, “Research and improvement for feature selection on naive bayes text classifier”, Future Computer and Communication (ICFCC), June 2010.
[16] Sona Taheri, Musa Mammadov, “Learning the Naive Bayes Classifier with Optimization Models”, Int. J. Appl. Math. Comput. Sci., 2013, Vol. 23, No. 4, 787–795.
[17] SasanKaramizadeh, Shahidan M. Abdullah, Azizah A. Manaf, MazdakZamani, AlirezaHooman, "An Overview of Principal Component Analysis", Journal of Signal and Information Processing, 2013.
[18] GyotirmoySarkara , SnehanshuSahab , SurbhiAgrawalb, “An Efficient Use of Principal Component Analysis in Workload Characterization-A Study”, Conference on Sports Engineering and Computer Science (SECS), 2014.
[19] Victor R. Basili, Fellow, Lionel C. Briand, and Walcelio L. Melo, “A Validation of Object- Oriented Design Metrics as Quality Indicators”, IEEE Transaction on Software Engineering, Vol 22, No. 10, October 1996.
[20] Ping Yu, TarjaSysta and Hausi Muller, “Predicting Fault-Proneness using OO Metrics: An Industrial Case Study”, Proceedings of the Sixth European Conference on Software Maintenance and Reengineering (CSMR.02)1534-5351/02 $17.00 © 2002 IEEE.
[21] Hector M. Olague, Letha H. Etzkorn, Sampson Gholston, and Stephen Quattlebaum, “Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes”, IEEE Transaction on Softwrae Engineering, Vol. 33, N0. 6, June 2007.

Sunil Sikka, Utpal Shrivastava, Pooja, "Role of CK Metrics to Identify Fault-Proneness in Object Oriented Software: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1162-1164, 2018.

Biometrics is the study of human behavior and features using their biological patterns. Nowadays face recognition plays a leading role in biometric for identifying a person without human cooperation. It is most efficient and sophisticated security system. The face recognition system can be applied to a variety of applications, such as searching for a criminal record, searching for a particular crime, finding missing children based on a monitoring site and track crime detection in ATMs. There are two reliable biometric acknowledgment procedures, for example, unique mark and iris acknowledgment. In any case, these methods are meddling and their prosperity depends exceedingly on the client collaboration since the clients are requested to position their eye before the iris scanner or put their finger on the unique finger impression gadget keeping in mind the end goal to finish the procedure. This can be viewed as a convoluted procedure for a typical man. Then again, face recognition is non-meddlesome since it depends on pictures recorded by a removed camera and can be extremely compelling regardless of whether the client doesn`t know about the presence of the face recognition framework. In this paper, a comprehensive investigation of face detection and face recognition strategies together with face databases are delivered.

Face detection, face recognition, face database, SVM, neural networks, SIFT

[1]. Bakhshi, Y., Kaur, S., & Verma, P. (2015). A Study based on Various Face Recognition Algorithms. International Journal of Computer Applications, 129(13), 16-20.
[2]. Bakshi, U., & Singhal, R. (2014). A survey on face detection methods and feature extraction techniques of face recognition. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(3), 233-237.
[3]. Nawaf Hazim Barnouti, (2016) “Face Recognition using PCA-BPNN with DCT Implemented on Face94 and Grimace Databases”, International Journal of Computer Applications 142(6):8-13.
[4]. Barnouti, N. H., Al-Dabbagh, S. S. M., Matti, W. E., & Naser, M. A. S. (2016). Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance. International Journal of Advanced Computer Science and Applications (IJACSA), 7(5), 371-377.
[5]. Bedre, J. S., & Sapkal, S. (2012). Comparative Study of Face Recognition Techniques: A Review. Emerging Trends in Computer Science and Information Technology–2012 (ETCSIT2012) Proceedings published in International Journal of Computer Applications®(IJCA), 12.
[6]. Choi, J. Y., Ro, Y. M., & Plataniotis, K. N. (2012). Color local texture features for color face recognition. IEEE transactions on image processing, 21(3), 1366-1380.
[7]. De Carrera, P. F., & Marques, I. (2010). Face recognition algorithms. Master`s thesis in Computer Science, Universidad Euskal Herriko.
[8]. Draper, B. A., Baek, K., Bartlett, M. S., & Beveridge, J. R. (2003). Recognizing faces with PCA and ICA. Computer vision and image understanding, 91(1-2), 115-137.
[9]. Gollen, M. (2012). Comparative analysis of face recognition algorithms.
[10]. Gottumukkal, R., & Asari, V. K. (2004). An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters, 25(4), 429-436.
[11]. Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled Faces in the wild: A database for studying face recognition in unconstrained environments (Vol. 1, No. 2, p. 3). Technical Report 07-49, University of Massachusetts, Amherst.
[12]. Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. New York: Springer.
[13]. Javed, M., & Gupta, B. (2013). Performance comparison of various face detection techniques. International Journal of Scientific Research Engineering & Technology (IJSRET) Volume, 2, 019-0027.
[14]. Jiang, N., Lu, Y., Tang, S., & Goto, S. (2010, December). Rapid face detection using a multi-mode cascade and separate haar feature. In Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on (pp. 1-4). IEEE.
[15]. Kadam, K. D. (2014). Face Recognition using Principal Component Analysis with DCT. International Journal of Engineering Research and General Science, ISSN, 2091-2730.
[16]. Kamerikar, U. A., & Chavan, M. S. (2014). Experimental Assessment of LDA and KLDA for Face Recognition. International Journal, 2(2).
[17]. Milborrow, S., Morkel, J., & Nicolls, F. (2010). The MUCT landmarked face database. Pattern Recognition Association of South Africa, 201(0).
[18]. Murtaza, M., Sharif, M., Raza, M., & Shah, J. (2014). Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis. International Arab Journal of Information Technology, 11(2), 1-11.
[19]. Murtaza, M., Sharif, M., Raza, M., & Shah, J. (2014). Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis. International Arab Journal of Information Technology, 11(2), 1-11.
[20]. Nagar, A., Nandakumar, K., & Jain, A. K. (2012). Multibiometric cryptosystems based on feature-level fusion. IEEE transactions on information forensics and security, 7(1), 255-268.
[21]. Nandini, M., Bhargavi, P., & Sekhar, G. R. (2013). Face recognition using neural networks. International Journal of Scientific and Research Publications, 3(3), 1.
[22]. Prof. B.S Patil, Prof. A.R Yardi, (2013), Real-time face recognition by varying number of eigenvalues, International Journal of Advanced Scientific and Technical Research Issue 3 volume 1, Jan-Feb 2013.
[23]. Rishiwal, V., & Gupta, A. (2012). Improved PCA algorithm for face recognition. World Applied Programming, 2(1), 55-59.
[24]. Senthilkumaran, N., & Rajesh, R. (2009). A study on edge detection methods for image segmentation. In Proceedings of the International Conference on Mathematics and Computer Science (ICMCS-2009) (Vol. 1, pp. 255-259).
[25]. Sharifara, A., Rahim, M. S. M., & Anisi, Y. (2014, August). A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection. In Biometrics and Security Technologies (IS BEST), 2014 International Symposium on (pp. 73-78). IEEE.
[26]. Sodhi, K. S., & Lal, M. (2013). Comparative analysis of PCA-based face recognition system using different distance classifiers. Int. J. of Appl. or Innovation in Eng. & Manag, 2, 341-348.
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V. Subha, M. Sahaya Pretha, "An Expansive Study of Facial Approaches," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1165-1171, 2018.

This paper proposed an efficient method of irrigation system for farmer so that they utilized water resource in a proper manner. This method is cost effective, reduced traditional method of irrigation system, low power consumption, and eliminate human intervention. The main aspect of our paper is to determine the soil moisture at some instant of intervals in its dry and wet condition with the aid of soil moisture circuit, evaluate the equivalent moisture and irrigate it based on nature. So the proposed system uses Arduino UNO development board and Lab-VIEW. It is programmed in such way that the sensors that is soil moisture sensor and water level sensor will sense moisture content of the soil and water level of the water storage and fed to Arduino, accordingly it will send command signal to pump to switch ON/OFF based on condition provided by the sensors. For better visualization of the sensors values to farmer, a graphical user interface is developed in LAB-VIEW . The various conditions will be indicated on the front panel such as wet and dry condition of soil with the level of water in the tank.

Arduino UNO , LAB-VIEW, LIFA, Soil moisture sensor , Water level sensor

[1] T. Bheema lingaiah, D.Hanumesh Kumar, C.Nagaraja, Solomon Woldet- sadik ,“Development of Humidity and Temperature Measurement Instru- mentation System using Lab-VIEW”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 10, 2013.
[2] S. V. Devika, Sk. Khamuruddeen, Sk. Khamurunnisa, Jayanth Thota, Khalesha Shaik, “Arduino Based Automatic Plant Watering System”, International Journal of Advanced Research in Computer Science and Software Engineering , Volume 4, Issue 10, pp. 449- 456, 2014
[3] P. S. Aswale1, Sonal Sali2, Shruti Zarole3, Pranali Patil4, “Automatic Irrigation Control System based on Lab-VIEW Arduino Interfacing”, | IJIRT | Volume 2 Issue 12, 2016
[4] Sarah Maria Louis, S. Srinithi, 8 “Monitoring of Relative Humidity of Soil Using LabVIEW”, International Journal of Innovative Research & Development, Vol 3 Issue 3, 2014.
[5] LabVIEW Interface for Arduino [LIFA], decibel.ni.com
[6] Rekha Sharma, Heena Das, S.N. Das, “WSN for Computerized Irrigation System in Tea Gardens”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.2, pp.26-30, 2016.
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[8] R. Beckwith. D. Teibel, and P. Bowen Jones, "Report from the Field: Results from an Agricultural Wireless Sensor Network", proceedings of 29th IEEE LCN`04, Tampa, Florida, November 15-17, 2004
[9] Chandraprakash Patidar, “E-IRRIGATION: An Automation of Irrigation using Wireless Networks”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.18-20, 2013.
[10] Srishti Rawal,” IOT based Smart Irrigation System”, International Journal of Computer Applications, Volume 159 – No, 2017

M.N. Gogoi, "An automated garden irrigation system: Controlled and monitored via Arduino and Lab-VIEW," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1172-1176, 2018.

With expeditious escalation of digital imaging and communication technologies, inspection of image is a utmost concern. One of the approaches to resolve this issue is to use neural networks. Neural network is fast and powerful scheme with a great ability to deal with noisy or incomplete information. HVS based framework proposed here aims to analyse two kinds of images: First one is fully blind image where there is no reference image available and second is partially blind image where reference image information is partially available as set of certain features. In the first case competitive leaning based self organizing feature map is used to train the network which makes clusters of the input no-reference images. Partially blind image analysis is achieved by training the network using unsupervised feature learning which classifies input in to specific classes on the basis of perceptual features. Many of the Existing methods utilize natural image statistics or probability distribution model which fails to differentiate images in accordance with subjective opinions. This paper considers perceived image features in order to properly classify different images which in turn ease image analysis.

Artificial neural network (ANN), Full-Reference (FR), Human visual system (HVS), No-Reference (NR), Reduced-Reference (RR), Receiver operating characteristics (ROC), Self organizing feature map (SOFM).

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[16] Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? - A new look at signal fidelity measures,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, Jan. 2009.
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Niveditta Thakur, "Perception based Framework for Full and Partial Blind Image interpretation using Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1177-1182, 2018.

Spelling error pattern analysis of a language is useful in language related technology, such as creation of Natural Language Interfaces, Machine Translation, Optical Character Recognition, Spell Checker and Corrector etc. It includes analysis of various types of errors (insertion, deletion, transposition, substitution, run-on, split word error) Positional analysis, Word length effects, Phonetic errors, First position error analysis, Keyboard effects etc. This paper mainly focuses on the effect of word length in substitution error pattern analysis of Punjabi by doing Statistical Error analysis of Punjabi typed text. It also presents a brief overview of effect of word length on non-word error analysis in Punjabi Typed Text. This paper is based on the analysis done on 20000 misspelled words generated by typists.

Addak, Gurmukhi, Non-word, Bindi

[1] F.J. Damerau (1964) "A technique for computer detection and correction of spelling errors".Commun. ACM. 7(3): 171-176.
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[3] K. Kukich (1992) "Techniques for automatically correcting words in text". ACM Computing Surveys. 24(4): 377-439.
[4] K.W. Church and W.A. Gale (1991) "Probability scoring for spelling correction". Statistical Computing. 1(1): 93-103.
[5] P. Kundu and B.B. Chaudhuri (1999) "Error Pattern in Bangla Text". International Journal of Dravidian Linguistics. 28(2): 49-88.
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[9] Wagner, Robert A. & Fischer, Michael J, `The string-to-string correction problem`, Journal of the A.C.M., vol.21, no.1, pp168-173, January 1974.
[10] Meenu Bhagat, ”Contribution of ‘Addak’and ‘Bindi’ in Non word Error Pattern analysis of Punjabi Typed Text”, “International Journal of Computer Sciences and engineering” vol. 5 issue 9.

Meenu Bhagat, "Contribution of Word length in Substitution Error Pattern analysis of Punjabi Typed Text," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1183-1185, 2018.

CPU scheduling is a technique by which the processes in the system are able to be allocated the processor and by execution they perform their intended task. In order to this various scheduling schemes are available with their own merits and demerits. Lottery scheduling is one of the best algorithms to schedule the processes for the processor so that all processes get executed with the ease of convenience with better efficiency. Lottery scheduling solves many problems of previous scheduling schemes, starvation is one of them, flexible and fair share of resources to each and every process is possible with this scheme. This paper illustrates the various methods provided in the field of CPU scheduling and in the basic function of operating system that is process management to calculate the ready queue estimation time. Ready queue is responsible for holding the processes which needs to be allocated to the CPU for execution. It contains heterogeneous type of processes from the multiprocessor environment. The lottery scheduling scheme allows each process with its specific characteristics to share the resources and execute accordingly. Processes have the dynamic characteristics of creation, activation, waiting, execution, sharing, pre-emption, interruption and termination. The time spent by the processes in the ready queue waiting for CPU is a crucial time because it predicts the performance efficiency of the CPU. The problem of time estimation of ready queue processing is represented in this paper with various associated constraints to better evaluate the results. The working of lottery scheduling scheme and various algorithms which are derived from it are represented here. The summarized version of the existing work represented here to better understand the problem and predict the new possibilities in this area.

scheduling algorithms, multiprocessor, process management, ready queue, estimation, operating system.

[1] C. A. Waldspurger, W. E. Weihl, "Lottery Scheduling: Flexible Proportional-Share resource Management", The 1994 Operating Systems Design and Implementation conference (OSDI`94), Monterey, California, 1994.
[2] D. Shukla, A. Jain and A. Choudhary, "Estimation of Ready Queue Processing Time under SL Scheduling Scheme in Multiprocessors Environment", International Journal of Computer Science and Security, vol.4, Issue.1, pp.74-81, 2010
[3] D. Shukla, A. Jain and A. Choudhary, "Estimation of Ready Queue Processing Time under Usual Group Lottery Scheduling (GLS) in Multiprocessor Environment", International Journal of Computer Applications,Vol.8, Issue.14, pp.39-45, 2010
[4] D. Shukla, A. Jain and A. Choudhary, "Prediction of Ready Queue Processing Time in Multiprocessor Environment using Lottery Scheduling (ULS)", International Journal of Computer Internet and Management, Vol.18, Issue.3, pp 58-65, 2010
[5] D. Shukla and A. Jain, "Analysis of Ready Queue Processing Time under PPS-LS and SRS-LS Scheme in Multiprocessing Environment", GESJ: Computer Science and Tele-communications, Vol. 33, Issue.1, pp.54-65, 2012
[6] D. Shukla and A. Jain, "Estimation of Ready Queue Processing Time using Efficient Factor Type Estimator (E-F-T) in Multiprocessor Environment", International Journal of Computer Applications, Vol.48, Issue.16, pp.20-27, 2012,
[7] D. Shukla and A. Jain, "Ready Queue Mean Time Estimation in Lottery Scheduling using Auxiliary Variables in Multiprocessor Environment", International Journal of Computer Applications, Vol. 55, Issue.13, pp.13-19, 2012
[8] D. Shukla and A. Jain, "Estimation of Ready Queue Processing Time using Factor Type (F-T) Estimator in Multiprocessor Environment", COMPUSOFT, An international journal of advanced computer technology, Vol.2, Issue.8, pp.256-260, 2013
[9] D. Shukla, A. Jain and K. Verma, "Estimation of Ready Queue Processing Time using Transformed Factor-Type (T-F-T) Estimator in Multiprocessor Environment", International Journal of Computer Applications, Vol.79, Issue.16, pp.40-48, 2013
[10] A. Silberschatz and P. Galvin, “Operating System Concepts”, John Wiley and Sons (Asia), Ed.5, 1999.
[11] W. Stalling, “Operating System”, Pearson Education, Singapore, Indian Edition, New Delhi, Ed.5, 2000.
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[14] S. Thompson, "Sampling", Wiley Eastern Publication, 3rd edition

Sarla More, Diwakar Shukla, "A Review on Ready Queue Processing Time Estimation Problem and Methodologies used in Multiprocessor Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1186-1191, 2018.

Fingerprint Matching is a prevailing biometric technique used for providing authentication. In Fingerprint recognition systems a raw image is scanned, then pre-processing is performed, features are extracted as vectors and stored in fingerprint databases. In this paper, a review of different features of fingerprint recognition systems is presented. A brief of different types of fingerprint patterns followed by minutiae based approach is given in this paper. The invariant and discriminatory information present in the fingerprint images are captured using fingerprint ridges known as minutiae. Pattern recognition based approach is also studied followed by wavelet based approaches. The challenges and issues relating to fingerprint recognition system are critically reviewed in this paper. A good quality, noise free image should be used as input in fingerprint recognition system to achieve high accuracy. Different fingerprint image enhancement techniques are also analyzed and discussed in this paper.

Authentication, ,Recognition, Biometrics, Fingerprint, Minutiae

[1] Jain AK, Feng J, Nandakumar K. Fingerprint Matching. Published by the IEEE Computer Society. 2010 Feb.
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[3] Nitin Tiwari, “An Overview and AnalysisBased on Biometric Framework Technique and Fingerprint Biometric Technology”, International Journal of Scientific research in Computer Science and Engineering, Vol. 5, Issue 6, pp.1, 2017
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[8] A.L.H. Jin A. Chekima J.A. Dargham, “Fingerprint identification and recognition using backpropagation neural network”, Research and Development, 2002. SCOReD 2002. Student Conference, Malaysia,2002
[9] H. Xu, Raymond N.J. Veldhuis, Tom A.M. Kevenaar, Ton A.H.M. Akkermans, “A Fast Minutiae-Based Fingerprint Recognition System”,IEEE systems journal,volume 3, Issue 4, 2009
[10] Le Hoang Thai and Ha Nhat Tam ,”Fingerprint recognition using standardized fingerprint model”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 7, May 2010
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[12] Prabhu RR, Ravikumar CN. A Novel Extended Biometric Approach for Human Character Recognition using Fingerprints. International Journal of Computer Applications (0975–8887). 2013 Sep; 77(1):37–44.
[13] Su C, Srihari S. Evaluation of Rarity of Fingerprints in Forensics. Proceedings of Neural Information Processing Systems, Canada. 2010 Dec 6-9:1–9.
[14] Loris N, Lumini A. Local binary patterns for a hybrid fingerprint matcher. Pattern Recognition. 2008 Nov; 41(11):3461–66.
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[15] Rajbhoj SM, Mane PB. An Improved Binarization based algorithm using minutiae approach for Fingerprint Identification. IJEAT. 2012 Aug; 1(6):219–22.
[16] Strasburger H, Rentschler I, Jüttner M. Peripheral vision and pattern recognition: A review. Journal of Vision. 2011 May; 11(5):13.
[17] Badwer IKSB, Kaur S. Image Denoising using different Wavelet Transforms with Hard and Soft Thresholding. International Journal of Advanced Research in Computer Science and Software Engineering. 2014 Aug; 4(8):153246.
[18] Basha MSB, Ramakrishnan M. Color Image Enhancement based on Daubechies Wavelet and HIS Analysis.International Journal of Computer Applications. 2012 Jun; 47(13):8–11.
[19] Rini, D.P., Shamsuddin, S.M. & Yuhaniz, "Particle swarm optimization for ANFIS interpretability and accuracy",S.S. Soft Comput (2016)
[20] U. Hany and L. Akter, "Speeded-Up Robust Feature extraction and matching for fingerprint recognition," 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, 2015, pp. 1-7.
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[22] Ahmed RJ. Image enhancement and noise removal by using new spatial filters. U.P.B. Sci. Bull., Series C. 2011; 73(1):65–74.
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[25] Hui Chen, Jianping Yin, Xin Shu, Chunfeng Hu and Yong Li, "An orientation-based ridge descriptor for fingerprint image matching," 2010 International Conference On Computer Design and Applications, Qinhuangdao, 2010, pp. V1-288-V1-292.
[26] Fernández FA, Fierrez J, Garcia JO, Rodriguez JG, Fronthaler H, Kollreider K, Bigun J. A comparative study of fingerprint image-quality estimation methods. IEEE P. Pakutharivu and M. V. Srinath Vol 8 (35) | December 2015 | www.indjst.org Indian Journal of Science and Technology 7 Transactions on Information Forensics and Security. 2007 Dec; 2(4):734–43.

Anchal Bansal, Shakti Arora, Surjeet Singh, "A Survey on Fingerprint Recognition System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1192-1197, 2018.

Today most of the languages support objects oriented architecture to develop an organized software system and customer base for these system is increasing because incentives in object oriented paradigm is huge. At the same time, drawback is that object oriented systems are complex and difficult to understand. So quality of these system must be monitored and maintained which require lots of money, time and efforts. In software community when prediction is made based on metrics, software quality is one of the most discussed term. Software quality can be estimated by analyzing the key quality attributes of the software system. The objective of this paper is to define a metric suite and designing of automatic tool to evaluate the object oriented software system under complexity analysis in order to analyze the software criticality. Methodology: This paper focusses on automatic quality analysis of object oriented system. For this different metrics and metric suites are analyzed and a tool is developed in .net environment that accept software program as input and perform a metric based analysis. To validate the tool a case study (LMS) is considered as input.

Component, ,Learning Management, Metrics, Object Oriented, Quality, Software

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[3] Paramvir Singh and Hardeep singh, “DynaMetrics: A runtime metric based analysis tool for object oriented software system”, SIGSOFT software engineering Notes, Vol. 33, No. 6, pp. 1-6, 2008.
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[5] M. Lorenz and J. Kidd. Object –Oriented Software Metrics, 1st Edition, Prentice-Hall, Englewood Cliffs, NY, 1994.
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[8] M. Xenos and D. Stavrinoudis and K. Zikouli and D. Christodoulakis, “Object oriented Metrics- A survey”, Proceedings of the FESMA 2000, Federation of European Software Measurement Associations, pp. 1-10, Spain, 2000.
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[16] Brij mohan Goel and Pardeep Kumar Bhatia, “Analysis of reusability of object oriented system using object oriented metric”, ACM Sigsoft Software Engineering Notes, Vol.38, No. 4, pp. 1-5, 2013.
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[18] A. Jatain and Y. Mehta, “Metrics and models for software reliability, “Proceeding sof International Conference on Issues and Challenge in Intelligent Computing Techniques, pp. 210-214, 2014.
[19] Zhiyi Ma, “An approach to improve the quality of object oriented models from novice modelers through project practice” Frontier of Computer Science, Vol. 11, No. 3, pp. 485-198, 2017.

A. Jatain, "Metric Based Automatic Quality Analysis of Object Oriented Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1198-1203, 2018.