Volume-7 , Issue-5 , May - 2019 Go Back

Software testing in Software Development Life Cycle (SDLC) can be thought of as a sub process as Software Testing Life Cycle which is the one of the testing methodologies that is followed in the agile developmental model. Understanding quality as a process in the cycle of software development, it is also necessary to understand the importance of quality since quality wins customer. So, to better the process of quality it becomes necessary to think out of the box on how to deal with the quality of the product. Though big players in the software industry have been investing ideas on better test methodologies to raise the quality standard of the product there’s always been a gap between the ideal bug free software product and the realistic one. There are plenty of reasons as for why such a gap is witnessed, some of them are lack of right process model, lack of experienced resources, lack of knowledge on tools and so on. On the other hand, Design Thinking (DT) viz., the key for creative resolution of problems has evolved over time that can be sought as a path for solving problems of varsity, and problems that are less understood to be dealt with. So, why can’t DT be a part in the process of quality of a product? In this paper, I propose an approach of the software testing methodology that involves the synergic effect of design thinking strategy on software testing, trying to bridge the gap between the idealistic and realistic view of a software product, discuss many aspects of how design can be a part of quality and conclude on heading to a new path of creative testing.

Agile process, Design thinking (DT)

[1] Cprime, What is Agile? What is Scrum?, [Online], Available: https://www.cprime.com/resources/what-is-agile-what-is-scrum/
[2] Wikipedia, Design Thinking, [Online], Available: https://en.wikipedia.org/wiki/Design_thinking
[3] Reply, Design Thinking, [Online], Available: http://www.reply.com/en/design-thinking
[4] David Terrar, Enterprise Irregulars, “What is Design thinking”, [Online],
Available:
https://www.enterpriseirregulars.com/125085/what-is-designthinking/
[5] Aditya P Mathur, Foundations of Software Testing, 2nd Edition,
Pearson, 2014
[6] Don Norman, The Design of Everyday things, “Design Thinking – The Double Diamond model for design”, Published by Basic Books, Perseus Books Group, 2013
[7] Roger S. Pressman, Software Engineering: A Practitioner’s Approach, 7th Ed., McGraw Hill International Edition,2017
[8] Ian Sommerville, Software Engineering, 9th ed., Pearson, 2014
[9] Paul C Jorgensen, “Software Testing: A Craftsman’s Approach”,
CRC Press, 2013, 4th Edition.

Sushmitha S Prabhu, Zoya Kapoor , "Think Software? Think Testing? Think Design!," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1611-1616, 2019.

In this paper we have developed a system which is able to extract the facial landmarks like jaw, eyebrows, nose, eye and mouth from human face. This is generally done in order to use the extracted data for analysis of the emotions that is depicted in human face. We have used openCV and Dlib library to detect the facial landmarks. There are many feature extraction techniques like Geometry-based Technique, Template-based Technique, Appearance-based Technique, Colour-based Technique, etc[9]. The Pre-trained file that we used to detect the facial landmarks was trained with an Ensemble of Regression Trees. Using the shape predictor of Dlib we passed the file over the input image and the detection was estimated through pixel intensity. The extracted pixel values were stored using pickle C object in python. Any suitable neural network may be farther used to train a model, from the extracted data from dataset/datasets, which is able to analyse the different emotions on human face. Our aim is to proceed further and train a model with neural network for Expression Analysis with special concentration on children.

Digital Image Processing, Facial Landmark Detection, Face Detection, Computer Vision

[1] Rencan Nie, Dongming Zhou, Min He, Xin Jin, and Jiefu Yu, “Facial Feature Extraction Using Frequency Map Series in PCNN”, Journal of Sensors, Vol. 2016, Article ID 5491341, pp. 1- 9, 2016.
[2] A. Azeem, M. Sharif, J.H. Shah, M. Raza, “Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction”, Journal of Applied Research and Technology, Vol. 13, pp. 3, 2015.
[3] M.H Siddiqi, R Ali, M Idris, A.M Khan, E.S Kim, et al., “Human facial expression recognition using curvelet feature extraction and normalized mutual information feature selection”, Multimedia Tools and Applications, Vol. 75, pp. 935-959, 2016.
[4] S.K.A Kamarol, M.H Jaward, J Parkkinen, Rajendran Parthiban, “Spatiotemporal feature extraction for facial expression recognition”, IET Image Processing, Vol. 10(7), pp. 534-541, 2016.
[5] M. Rabiei, A. Gasparetto, “System and method for recognizing human emotion state based on analysis of speech and facial feature extraction; Applications to Human-Robot Interaction”, In the Proceedings of the 4th International Conference on Robotics and Mechatronics(ICROM 2016), Tehran, Iran, pp. 266-271, 2016.
[6] Swati jadon, Mahendra Kumar, Y ogesh Rathi, “Face Recognition Using Som Neural Network With Ddct Facial Feature Extraction Techniques”, IEEE International Conference on Communications and Signal Pocessing (ICCSP 2015) , India, UAE, pp. 1070-1074, 2015.
[7] Haibin Liao, “Facial age feature extraction based on deep sparse representation”, Multimedia Tools and Applications, Vol. 78, pp. 2181–2197, 2018.
[8] Karin Sobottka, Ioannis Pitas, “A novel method for automatic face segmentation, facial feature extraction and tracking”, Signal Processing: Image Communication, Vol. 12(3), pp. 263-281, 1998.
[9] Sanjeev Dhawan, Himanshu Dogra, “Feature Extraction Techniques for Face Recognition”, International Journal of Engineering, Business and EnterpriseApplications (IJEBEA), Vol. 2(1), pp. 1-4, 2012.
[10] V Kazemi, J Sullivan, “One Millisecond Face Alignment with an Ensemble of Regression Trees”, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, pp. 1867-1874, 2014.
[11] Ramesha K, K B Raja , Venugopal K R, L M Patnaik, “Feature Extraction based Face Recognition, Gender and Age Classification”, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No.01S, 14-23, , 2010.
[12] Vikramsingh R. Parihar1, Anagha P. Dhote, “A Novel Approach to Real Time Face Detection and Recognition”, (IJCSE) International Journal on Computer Science and Engineering, Vol. 5, Issue. 01S, 62-67, , 2017.
[13] Md. T. Akhtar, S. T. Razi, K. N. Jaman ,A. Azimusshan ,Md. A. Sohel, “Fast and Real Life Object Detection System Using Simple Webcam”, (IJSRCSE) International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue. 4, pp. 18-23, 2018.
[14] A. Shakin Banu ,P.Vasuki , S. Mohamed Mansoor Roomi, A. Yusuf Khan, “SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, (IJSRNSC) International Journal of Scientific Research in Network Security and Communication, Vol. 6, Issue. 3, pp. 13-17, 2018.

Takrim Ul Islam Laskar, Parismita Sarma, "Facial Landmark Detection for Expression Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1617-1622, 2019.

Brain-Computer Interfaces (BCI) is a trending way for human beings to smartly interact with a computer by using only their brain. The work leads for a new concept to help the special people to use computer. The main aim is to control mouse cursor movement by eyes via brain signals. Electroencephalogram (EEG) marks and records brain wave pattern. The new technology Electroencephalography is to detect brain signals that help in controlling the mouse cursor. This project deals with machine learning techniques for classifying neural signals and their using them for Brain-Computer Interfacing.

BCI, EEG Signals, Electroencephalography

[1] V.R. Kannan and A. Mariyammal, “Aa eeg-based brain controlled design with an itinerant robot”, International Journal of Management, Information Technology and Engineering,2013.
[2] P. S. Vaidya and P. P. Sahare, “Design and implementation of ‘office boy robot’ using arduino uno”, 2017.
[3] R. Pahuja and N. Kumar, “Android mobile phone controlled bluetooth robot using 8051 microcotroller”, International Journal of Scientific Engineering and Research, 2014.
[4] Nandita and C. P. A, “Eeg-based brain controlled robo and home appliances,” International Joural of Engineering Trends and Technology, 2017.
[5] B. J. A. Rani and A. Umamakeswari, “Electroencephalogram- based brain controlled robotic wheelchair”, International Journal of Science and Technology, 2015.
[6] M. Teplan, “Fundamentals of eeg measurement,”, Measurement Science Review, 2002.
[7] Arslan Qamar Malik, and Jehanzeb Ahmad, “Retina Based Mouse Cursor (RBMC)”,2007.
[8] Debashis Das Chakladar and Sanjay Chakraborty, “Multi-Target Way of Cursor Movement in Brain Computer Interface using Unsupervised Learning”, 2018.
[9] Mr. Nilesh Zodape, Dr. Narendra Bawane and Pratik Hazare, “1-D Cursor Movement using Brain Computer Interface”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 8, August 2015
[10] Mohammad H. Alomari, Ayman AbuBaker, Aiman Turani, Ali M. Baniyounes and Adnan Manasreh, “ EEG Mouse:A Machine Learning-Based Brain Computer Interface”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 5, No. 4, 2014
[11] Manne Vamshi Krishna, Gopu Abhishek Reddy, B. Prasanthi and M. Sreevani, “Green Virtual Mouse Using OpenCV”, (IJCSE) International Journal of Computer Sciences and Engineering,
Vol.-7, Issue-4, April 2019
[12] Praveen kumar, M.Govindu, A.Rajaiah, “Automatic Home Control System Using Brain Wave Signal Detection”, (IJESC) International Journal of Engineering Science and Computing, October 2014

Sweety Buragohain, Kishore Kashyap, "Mouse Cursor Control using EEG Signal and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1623-1627, 2019.

Decision trees, logistic regression and support vector machine are very popular algorithms for predicting the customer churn with comprehensibility and well-built predictive performance and. Regardless of the strengths they are having flaws, decision trees having problem to handle the linear relations among the variables, logistic regression is having difficulties to handle interaction effects among the variables, and support vector machine performs marginally better than logistic regression. Consequently a new hybrid algorithm named as support leaf model (SLM) was proposed to classify the data. The idea following the support leaf (SLM) is that implementation of different models on segments of the data gives better predictive performance rather than on the entire dataset, the comprehensibility is maintained from the models which are constructed on the leaves. The SLM consists of two phases, one is segmentation phase and another one is prediction phase. In first stage by using decision tree the customer segments are identified and in the second stage a model is created for every leaf of the tree. To measure the predictive performance area under the receiver operating characteristics curve (AUC) and top decile lift (TDL) are used. Based on the performance metrics AUC and TDL, logit leaf model (LLM) works well when compared with support leaf model (SLM).

Customer churn prediction, Hybrid algorithm, Logit leaf model, Support leaf Model, Predictive analytics

[1] Michael C. Mozer, Richard Wolniewicz, David B. Grimes,“Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry,” IEEE Transactions On Neural Networks, Vol. 11, pp. 690-696, September 2000.
[2] Colgate, M., Stewart, K., & Kinsella, R. (1996). Customer defection: A study of the student market in Ireland. International Journal of Bank Marketing, 14(3), 23–29.
[3] Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65–87.
[4] Shaffer, G., & Zhang, Z. J. (2002). Competitive one-to-one promotions. Management Science, , 48(9), 1143–1160.
[5] Blattberg, R. C., Kim, B. D., & Neslin, S. A. (2010). Database marketing: Analyzing and managing customers. New York, NY: Springer.
[6] Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211–229.
[7] Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimension, and triggers on customer retention. Journal of Marketing, 69(4), 210–218.
[8] Seret, A., Verbraken, T., Versailles, S., & Baesens, B. (2012). A new SOM-based method for profile generation: Theory and application in direct marketing. European Journal of Operational Research, 220, 199–209.
[9] Ali Dehghan, Theodore B. Trafalis, “Examining Churn and Loyalty Using Support Vector Machine”, Scienceedu Press, Vol. 1, (4), pp. 153- 161, December 2012.
[10] Marie Fernandes, “Data mining: A Comparative Study of its various Techniques and its”, IJSRCSE, Volume-5, Issue-1, pp.19-23, February (2017).
[11] G. Sathyadevi, “Application of CART Algorithm in hepatitis Disease Diagnosis”, IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011.
[12] mi zhou, “A hybrid feature selection method based on fisher score and genetic algorithm” , Journal of Mathematical Sciences: Advances and Applications Volume 37, 2016, Pages 51-78
[13] Coussement, K., Lessman, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication. Decision Support Systems, 95, 27–36.
[14] Arno De Caigny, Kristof Coussement, Koen W. De Bock, “A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees”, European journal of operational research 269 (2018) 760-772.
[15] S. JabeenBegum, B. Swaathi, “A Survey for identifying Parkinson’s disease by Binary Bat Algorithm”, IJSRCSE, Vol 7,Issue 2, pp.17-23, April (2019).

Kunchaparthi Jyothsna Latha, Markapudi Baburao, Chaduvula Kavitha, "A Comparative study on Logit leaf model (LLM) and Support leaf model (SLM) for predicting the customer churn," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1628-1632, 2019.

The objective of the project is to design an efficient automatic vehicle identification system using cameras. There exists a number of vehicles on road. In the modern world the criminal misappropriation using vehicles are becoming a greatest challenge. In the existing system, vehicle identification is done manually and the information is passed through vehicle authorities through communicating devices. So in order to overcome such disadvantage we need a automatic vehicle identification system. Automatic vehicle identification at signal point using number plate to identify the vehicle and informs to vehicle department authorities. The system detects and extracts the number plate of all vehicles in traffic signal and email or messages regarding the vehicle are send to vehicle authorities probably within 10 km radius so that immediate actions can be initiated against any remarked vehicles according to the data received. Thus this can be used as a real time system for vehicle identification

location, camera, vehicle, traffic signal

[1]. Sharma G “performance analysis of vehicle number plate recognition system using template matching techniques”. J Inform Tech.pp.2018
[2]. Prof. Amit Kukreja1 Swati Bhandari2, Sayali Bhatkar3, Jyoti Chavda4, Smita Lad5, “indian vehicle number plate detection using image processing” Electronics And Telecommunication, Mumbai University, K.J.Somaiya Institute Of Engineering And T, Mumbai, Maharashtra , India(IRJET) e-ISSN: 2395 -0056 Vol.4, Issue.4, pp.2017
[3]. E. R. Lee, P. K. Kim, H. J. Kim, “Automatic recognition of a vehicle license plate using color image processing,” International Conference On Image Processing (ICIP’94), Vol. 2, pp. 301-305, 1994.
[4]. CelilOzkurt “Automatic Traffic Density Estimation And Vehicle Classification For Traffic Surveillance Systems Using Neural Networks.”
[5]. “Mathematical and Computational Applications,” Vol.14, Issue. 3, pp. 187- 196, 2009.
[6]. WisnuJatmiko,,“Detection and Counting of Vehicles Based on Video Processing In Distributed Traffic System”, International Conference on Advance
[7]. Computer Science and Information System 2010
[8]. K.K. KIM, K.I., KIM, J.B. KIM, and H.J. KIM, “Learning-Based
Approach for License Plate Recognition” Proceeding of IEEE Signal Processing Society Workshop, Vol. 2, pp.614-623, 2000.
[9]. Ronan O’Malley “Rear-Lamp Vehicle Detection and Tracking in Low- Exposure Color Video for Night Conditions”. IEEE Transactions on Intelligent Transportation Systems, Vol. 11, Issue.2, pp.June 2010
[10]. Zhang Y.,Zhang C:“New Algorithm for Character Segmentation of License Plate”, Intelligent vehicles Symposium,IEEE,pp 200

Sachu Alex, Krishnendu S., Shoby Ann Sunny, Sudhi G. K., Imthiyas M.P., "Remarked Vehicle Detection and Identification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1633-1642, 2019.

The prediction analysis is the approach which can predict the future possibilities based on the current information. The diabetes prediction is the approach which is applied to predict the diabetes based on the various attributes. The diabetes dataset has various attributes and based on that attributes diabetes can be predicted. In the previous year’s approach of SVM is applied for the diabetes prediction. To improve accuracy of diabetes prediction voting based classification is applied in this paper. The proposed model is implemented in python and results are analyzed in terms of accuracy, execution time.

Diabetes, SVM, Voting

[1] Abdelghani Bellaachia and Erhan Guven (2010), “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Washington DC 20052, vol. 6, 2010, pp. 234-239.
[2] Azhar Rauf, Mahfooz, Shah Khusro and Huma Javed (2012), “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research, vol. 12, 2012, pp. 959-963.
[3] Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang (2017), “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, 2017, IEEE, vol. 15, 2017, pp- 215-227
[4] Akhilesh Kumar Yadav, Divya Tomar and Sonali Agarwal (2014), “Clustering of Lung Cancer Data Using Foggy K-Means”, International Conference on Recent Trends in Information Technology (ICRTIT), vol. 21, 2013, pp.121-126.
[5] Kajal C. Agrawal and Meghana Nagori (2013), “Clusters of Ayurvedic Medicines Using Improved K-means Algorithm”, International Conf. on Advances in Computer Science and Electronics Engineering, vol. 23, 2013, pp. 546-552.
[6] [10] Chew Li Sa, Bt Abang Ibrahim, D.H., Dahliana Hossain, E. and bin Hossin, M. (2014), "Student performance analysis system (SPAS)", in Information and Communication Technology for The Muslim World (ICT4M), 2014 The 5th International Conference on, vol.15, 2014, pp.1-6.
[7] Han Wu, Shengqi Yang, Zhangqin Huang, Jian He, Xiaoyi Wang, “Type 2 diabetes mellitus prediction model based on data mining”, ScienceDirect, Vol. 11, issue 3, pp. 12-23, 2018.
[8] Prova Biswas, Ashoke Sutradhar, Pallab Datta, “Estimation of parameters for plasma glucose regulation in type-2 diabetics in presence of meal”, IET Syst. Biol., 2018, Vol. 12 Iss. 1, pp. 18-25, 2018.
[9] Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal 15 (2017) 104–116
[10] Zhiqiang Ge, Zhihuan Song, Steven X. Ding, Biao Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning”, 2017 IEEE. Translations and content mining are permitted for academic research only, vol. 5, pp. 20590-20616, 2017.
[11] Alexis Marcano-Cede˜no, Diego Andina, “Data mining for the diagnosis of type 2 diabetes”, IEEE, Vol. 11, issue 3, pp. 9-19, 2016.
[12] Bayu Adhi Tama, Afriyan Firdaus, Rodiyatul FS, “Detection of Type 2 Diabetes Mellitus with Data Mining Approach Using Support Vector Machine”, Vol. 11, issue 3, pp. 12-23, 2008.
[13] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly, “DIAGNOSIS OF DIABETES USING CLASSIFICATION MINING TECHNIQUES”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.1, 2015.

Munendra Kumar, Anuj Kumar, "Ensemble Classification Model for Diabetes Prediction in Data Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1643-1647, 2019.

Recognition of text and non-text images is a major challenge in the field of computer vision so as to efficiently extract the text from that image. The algorithm used for the extraction of the text from the images would have a higher efficiency if it is known beforehand that the image is a text image or a non-text image. However, there are many images such as old manuscripts where the extraction of the text becomes very difficult. In that case, the algorithm for the distinction between the text and non-text becomes very easy for text detection and have high accuracy and fast in detecting the text from the image. This method can also be applied to detect and extract the text from the signboards also. In our approach, we had built a system that takes any sort of image as an input. After the input of the image, it is then processed and converted into a binary image. Distance transform method is then applied and the measure of the distance between the various points in the image are then calculated. From the calculated points, duplicate points are merged into one point and are sorted in ascending order. The total area of the binary image is then calculated and also the image corresponding to each of the distance transform points are then calculated. The total area of the binary image is then divided by each of the area value of the corresponding distance transform points are the value extracted is known as the feature values. After getting all the feature values the whole value is then divided into small intervals and is then processed through the classifier. The accuracy of the classifier is then calculated and evaluated. This method is a very simple and accurate method for the calculation of the average accuracy of purely text and purely non-text images which can be further used to distinguish between text and non-text images. Experiment have been done with simple text and non-text image dataset and the efficiency of the proposed method is then demonstrated.

text and non-text, distance transform, SVM classifier

[1]. Najwa Maria Chidiac, Pascal Damein and Charles Yacoub, “A robust algorithm for text extraction from images”, 39th International conference on Telecommunication and Signal Processing, 2016.
[2]. Radhika Patel and Suman K Mitra, “Extracting text from degraded documents”, 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2015.
[3]. R. Malik and SeongAh chin, “Extraction of text in images”, Proceedings of International Conference on Information Intelligence and Systems, 1999.
[4]. Sezer Karaoglu, Ran Tao, Theo Gevers and Arnold W. M. Smeulders, “Words matter: Scene Text for Image Clssification and Retrieval”, IEEE transactions on multimedia, vol. 19, no. 5, may 2017.
[5]. Chengquan Zhang, Cong Yao, Baoguang Shi and Xiang Bai, “Automatic discrimination of text and non-text natural images”, 13th International Conference on Document Analysis and Recognition, 2015.
[6]. Vishal Chowrasia, Sanjay Shilakari and Rajeev Pandey, “Implementation of Optical Character Recognition Using Machine Learning”,International Journal of Computer Science and Engineering, Vol.-6, Issue-6, jun 2018.

Chowdhury Md. Mizan, Pradipta Karmakar, Sayak Dasgupta, Tridib Chakraborty, "Mean Average Accuracy for Text And Non-Text Images Using SVM Classifier," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1648-1651, 2019.

Disease prediction techniques play a major role in the recent times since it is crucial to predict the risks of a disease in advance for leading a healthier life. Diabetes is one of the diseases that affect lots of people. Since it is increasing rapidly, more and more people are being affected by diabetes based diseases like Diabetes Nephropathy (DN) and Diabetes Mellitus (DM). Most people suffering from diabetes do not know a lot about their health quality or the risk factors faced until they get diagnosed with the disease. This disease is a major cause of renal failure, blindness, stroke, and cardiovascular diseases. Most of the deaths occurring from Type 2 DM and the linked diseases take place at the initial stages. In this study, a novel machine learning technique is implemented that combines Back Propagation Neural Network (BPNN) and Naïve Bayes(NB) classifiers for predicting diabetes, and thereby detecting the associated diseases like DM and DN. Further, the proposed technique is analyzed for different evaluation metrics like accuracy, precision, recall and false positive rate. Finally, the performance of the proposed approach is compared with existing techniques like BPNN and NB. The proposed approach has a prediction accuracy of 93% which is higher than the conventional techniques.

Diabetes, Prediction, BPNN, NB, Ensemble

[1] S. Thomas and J. Karalliedde, “Diabetic nephropathy,” Medicine (Baltimore)., vol. 47, no. 2, pp. 86–91, Feb. 2019.
[2] The Statistics Portal, “Diabetes - Statistics and Facts,” 2019. [Online]. Available: https://www.statista.com/topics/1723/diabetes/. [Accessed: 29-Apr-2019].
[3] World Health Organization, “Global Status Report On Noncommunicable Diseases,” 2014. [Online]. Available: https://apps.who.int/iris/bitstream/handle/10665/148114/9789241564854_eng.pdf?sequence=1. [Accessed: 31-Jan-2019].
[4] M. Motwani et al., “Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.,” Eur. Heart J., vol. 38, no. 7, pp. 500–507, Feb. 2017.
[5] A. Chandiok and D. K. Chaturvedi, “Machine learning techniques for cognitive decision making,” in 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), 2015, pp. 1–6.
[6] W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: promise and potential,” Heal. Inf. Sci. Syst., vol. 2, no. 1, p. 3, Dec. 2014.
[7] I. Segura-Bedmar, C. Colón-Ruíz, M. Á. Tejedor-Alonso, and M. Moro-Moro, “Predicting of anaphylaxis in big data EMR by exploring machine learning approaches,” J. Biomed. Inform., vol. 87, pp. 50–59, Nov. 2018.
[8] Anupriya, Saranya, and Deepika, “Mining Health Data in Multimodal Data Series for Disease Prediction,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 6, no. 2, pp. 96–99, 2018.
[9] R. Wang-Sattler et al., “Novel biomarkers for pre-diabetes identified by metabolomics,” Mol. Syst. Biol., vol. 8, Sep. 2012.
[10] S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, and N. Qureshi, “Can machine-learning improve cardiovascular risk prediction using routine clinical data?,” PLoS One, vol. 12, no. 4, p. e0174944, Apr. 2017.
[11] A. Samant and S. Kadge, “Classification of a Retinal Disease based on Different Supervised Learning Techniques,” Int. J. Sci. Res. Netw. Secur. Commun., vol. 5, no. 3, pp. 1–5, 2017.
[12] A. Dagliati et al., “Machine Learning Methods to Predict Diabetes Complications,” J. Diabetes Sci. Technol., 2018.
[13] M. Makino et al., “Artificial Intelligence Predicts Progress of Diabetic Kidney Disease-Novel Prediction Model Construction with Big Data Machine Learning,” Diabetes, vol. 67, no. Supplement 1, pp. 539-P, May 2018.
[14] C. Zhu, C. U. Idemudia, and W. Feng, “Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques,” Informatics Med. Unlocked, p. 100179, Apr. 2019.
[15] H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Informatics Med. Unlocked, vol. 10, pp. 100–107, 2018.
[16] B. M. Patil, R. C. Joshi, and D. Toshniwal, “Hybrid prediction model for Type-2 diabetic patients,” Expert Syst. Appl., vol. 37, no. 12, pp. 8102–8108, Dec. 2010.
[17] A. Ahmad, A. Mustapha, E. D. Zahadi, N. Masah, and N. Y. Yahaya, “Comparison between Neural Networks against Decision Tree in Improving Prediction Accuracy for Diabetes Mellitus,” in Digital Information Processing and Communications, 2011, pp. 537–545.
[18] A. Marcano-Cedeño, J. Torres, and D. Andina, A Prediction Model to Diabetes Using Artificial Metaplasticity, vol. 6687. 2011.
[19] V. V. Vijayan and C. Anjali, “Decision support systems for predicting diabetes mellitus — A Review,” in 2015 Global Conference on Communication Technologies (GCCT), 2015, pp. 98–103.
[20] D. Jeevanandhini, E. G. Raj, V. D. Kumar, and N. Sasipriyaa, “Prediction of Type2 Diabetes Mellitus Based on Data Mining,” Int. J. Eng. Res. Technol., vol. 6, no. 4, 2018.
[21] G. Yirui, L. Yuqian, W. G. Xiaotian, Z. Luning, Z. Hongyan, and W. Bingyuan, “Application of artificial neural network to predict individual risk of type 2 diabetes mellitus,” J. Zhengzhou Univ. Sci., vol. 49, no. 3, 2014.
[22] N. LaPierre, C. J.-T. Ju, G. Zhou, and W. Wang, “MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction,” Methods, Mar. 2019.
[23] S. Mezzatesta, C. Torino, P. De Meo, G. Fiumara, and A. Vilasi, “A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis,” Comput. Methods Programs Biomed., vol. 177, pp. 9–15, Aug. 2019.
[24] M.-Y. Day and C.-C. Tsai, “A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering,” in 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), 2016, pp. 513–519.
[25] Kaggle, “Pima Indians Diabetes Database,” Kaggle, 2019. [Online]. Available: https://www.kaggle.com/uciml/pima-indians-diabetes-database. [Accessed: 31-Jan-2019].

Issac P. J., Allam Appa Rao, "Prediction of Diabetes with a BPNN-NB ensemble classifier," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1652-1657, 2019.

The supplier selection process is an important activity in any organization whether it is a manufacturing unit or educational institution to procure products or raw materials with the right price, strictly following the delivery schedule and the right quantities ordered. Anything that goes wrong in the form of delay in delivery or shortage of items ordered would lead to a chaotic situation in the organization. It often happens in the case of systems that are not properly integrated. The aim of this paper is to introduce a particular way of selecting a supplier by using Multi-agent system (MAS) based Case Based Reasoning system (CBRS) using Data mining (DM) techniques. The method that is suggested in this paper is to combines MAS, CBRS and DM for supplier selection. Finally this integrated model MAS-CBRS-DM is illustrated by an example in a seller selection process.

Multi-agent system, Case base reasoning system, Data mining

[1] Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59.R. Solanki, “Principle of Data Mining”, McGraw-Hill Publication, India, pp. 386-398, 1998.
[2] Abutair, H. Y., & Belghith, A. (2017 a). A Multi-Agent Case-Based Reasoning Architecture for Phishing Detection. Procedia Computer Science, 110, 492-497.
[3] Ayzenshtadt, V., Langenhan, C., Bukhari, S. S., Althoff, K. D., Petzold, F., & Dengel, A. (2016, February). Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs. In ICAART (1) (pp. 149-156).
[4] Berman, A. F., Maltugueva, G. S., & Yurin, A. Y. (2018). Application of case-based reasoning and multi-criteria decision-making methods for material selection in petrochemistry. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 232(3), 204-212.
[5] Byrski, A., Dreżewski, R., Siwik, L., & Kisiel-Dorohinicki, M. (2015). Evolutionary multi-agent systems. The Knowledge Engineering Review, 30(2), 171-186.
[6] De Mantaras, R. L., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., ... & Keane, M. (2005). Retrieval, reuse, revision and retention in case-based reasoning. The Knowledge Engineering Review, 20(3), 215-240.
[7] Eibe Frank, Mark A. Hall and Ian H. witten. The WEKA Workbench. Fourth Edition, 2016
[8] Haque, B. U., Belecheanu, R. A., Barson, R. J., & Pawar, K. S. (2000). Towards the application of case based reasoning to decision-making in concurrent product development (concurrent engineering). Knowledge- Based Systems, 13(2-3), 101-112.
[9] Jani, H. M., & Mostafa, S. A. (2011). Implementing case-based reasoning technique to software requirements specifications quality analysis. International Journal of Advancements in Computing Technology, 3(1).
[10] Kwon, O., Im, G. P., & Lee, K. C. (2007). MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties. Expert Systems with Applications, 33(3), 690-705.
[11] Jiawei Han, Micheline kamber, Jian pei. DATA MINING Concepts and Techniques, 3rd Edition, 2011
[12] Leake, D. B. (1996). Case-Based Reasoning: Experiences, lessons and future directions. MIT press
[13] .Mostafa, S. A., Ahmad, M. S., & Firdaus, M. (2012). A soft computing modeling to case-based reasoning implementation. International Journal of Computer Applications, 47(7), 14-21.
[14] Richter, M. M., & Weber, R. O. (2016). Case-based reasoning. Springer-Verlag Berlin An.

Jagjit Singh Dhatterwal, Sarvottam Dixit, S. Srinivasan, "The role of MAS based CBRS using DM Techniques for the supplier selection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1658-1665, 2019.

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009. As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research. This paper gives a survey of cloud computing and mobile cloud computing services models.

cloud computing, cloudlet, mobile cloud computing, service models

[1] RAJKUMARBUYYA, CHEE SHIN YEO, SRIKUMARVENUGOPAL, JAMES BROBERG, AND IVONABRANDIC. CLOUD COMPUTING AND EMERGING IT PLATFORMS: VISION, HYPE, AND REALITY FOR DELIVERING COMPUTING AS THE 5TH UTILITY. FUTURE GENER.COMPUT. SYST., 25(6):599–616, 2009.
[2] 2.R Buyya, C S Yeo, S Venugopal, J Broberg, I Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,Future Generation Computer Systems, Vol. 25 No. 6.(June 2009), pp. 599-616.
[3] M. Walshy, "Gartner: Mobile to Outpace Desktop Web by2013", Online Media Daily, 2010.
[4] Nike Inc., [online] Available: http://www.nike.com.
[5] B. Chun, "Clonecloud: Elastic Execution between Mobile Device and Cloud", Proc. 6th Conf. Computer Systems, pp. 301-14, 2011.
[6] E. Cuervo, "Maui: Making Smartphones Last Longer with Code Offload", Proc. Int`l. Conf. Mobile Sys. Applications and Services, 2010.
[7] S. Kosta, "ThinkAir: Dynamic Resource Allootion and Parallel Execution in Cloud for Mobile Code Offloading", Proc. IEEE INFOCOM, 2012.
[8] A. Covert, "Google Drive iCloud Dropbox and More Compared: What`s the Best Cloud Option?", Technical Review, 2012.
[9] P. Stued, I. Mohomed, D. Terry, "Wherestore: Location-based Data Storage for Mobile Devices Interacting with the Cloud", Proc. 1st ACM Wksp. Mobile Cloud Computing & Services: Social Networks and Beyond, 2010.
[10] D. Neumann, "Stacee: Enhancing Storage Clouds Using Edge Devices", Proc. 1st ACM/IEEE Wksp. Autonomic Computing in Economics, 2010.
[11] J. Oberheide, E. Cooke, F. Jahanian, "CloudAV: N-Version Antivirus in the Network Cloud", Proc. 17th USENIX Security Symp., 2008-July.
[12] D. H. Le Xu, V. Nagarajan, W.-T. Tsai, "Secure Web Referral Services for Mobile Cloud Computing", IEEE Int`l. Symp. Mobile Cloud Com-puting and Service Engineering, 2013.
[13] Zscaler Inc., [online] Available: http://www.zscaler.com.
[14] Google Inc., [online] Available: http://www.google.com/wallet.
[15] R. Fakoor, "An Integrated Cloud-Based Framework for Mobile Phone Sensinq", Proc. ACM SIGCOMM MCC Wksp., 2012.
[16] H. Wu, D. Huang, "Personal On-Demand Execution Environment for Mobile Cloud Computing", 2013, [online] Available: http://poem.mobicloud.asu.edu.
[17] OSGi Alliance, [online] Available: http://www.osgi.org.

Merugu. Gopichand, "A Survey on Service Models in Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1666-1671, 2019.