Volume-7 , Issue-2 , Feb - 2019 Go Back

The growing global demand for power supply is likely to exhaust available resources soon. It is advisable to avoid wastage of electricity as it may overburden consumer adversely. In the present study, we propose an IoT based solution to reduce electric power wastage in organizations. As the organizations are generally divided into sub sections or departments, a frame work can be proposed which allows the managers and supervisors to keep an online track of the ON/OFF status of appliances in their respective departments/sectors. The access to appliances can be provided with a Secure Shell connection through a dedicated server which keeps monitoring all the appliances in the whole organization continuously. Each manager and in-charge along with other officials can be provided with a user ID and password to login with. Each of them is likely to entertain with different level of rights to control various gadgets of the department. This frame work can prove itself to be useful in reducing the problem of various appliances ON in an organization. The frame work has provision for further improvements such that with slight modification it can be implemented for controlling and monitoring a weather station situated in the remote places.

RPi (Raspberry Pi), Arduino UNO, SSH, GSM

[1] Faisal Baig, Saira Baig, and Fahad Khan, Muhammad. Controlling home appliances remotely through voice command. International Journal of Computer Applications, 48(17):0975-888, June 2012.
[2] Shivanka, Ashu Grover, and Nikhil Arora. Controlling electrical appliances through pc and gsm technology. International Journal of Computer Applications, 76(2):09758887, August 2013.

Swaleha Zubair, Uzair Aalam, "An Advanced IoT Based Frame Work to Save Electrical power in an Organization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.257-260, 2019.

Preparing for job interviews is very difficult. A lot of candidates are not prepared for the interviews and so they are not able to fetch their dream jobs. Mostly candidate`s selection is based on the answers given in the interview. People will definitely hire those candidates who show interest and positive attitude. Using deep learning techniques we are proposing an application framework which would help candidates in preparing for the interviews. This involves multiple neural networks working separately for predicting different sections of an interview and providing real-time feedback and a report. Using CNN emotional analysis is performed on the video stream and Recurrent neural networks are utilized for sentiment analysis. Further, this Recurrent neural network with LSTM units is utilized for chat-bot interaction during the process. The chat-bot ask the questions to the candidate and candidate`s response is recorded and analysis is done. All these analyses together will try to prepare the candidate for the interview as a whole.

Chatbot , Deep Learning, Facial Expression Recognition, Sentiment Analysis

[1] Chandrahas Gaikwad, Satish Akolkar, Reshma Khodade, “Generic PDF to Text Conversion using Machine Learning”,International Journal of Computer Applications (0975–8887), Volume 106 – No. 12, November 2014.
[2] Shinn, M. G. (1985). Campus interviews — “What do they want from me?” Approach on-campus interviews by considering what is right for you. IEEE Potentials, 4(4), 11–12. doi:10.1109/mp.1985.6500202
[3] K. Tarunika ; R.B Pradeeba ; P.Aruna “Applying Machine Learning Techniques for Speech Emotion Recognition” 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Year: 2018
[4] Yuanlu Kuang ; Lijuan “Speech emotion recognition of decision fusion based on DS evidence theory Li “2013 IEEE 4th International Conference on Software Engineering and Service Science Year: 2013
[5] Richard Socher, Alex Perelygin, Jean Y. Wu,Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts,”Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” ,Proceedings of the conference on empirical methods in natural language processing (EMNLP), volume 1631, page 1642. Citeseer, 2013
[6] Y. C. Pan ; M. X. Xu ; L.Q. Liu ; P. F. Jia “Emotion-detecting Based Model Selection for Emotional Speech Recognition “, IMACS Multiconference on "Computational Engineering in Systems Applications"(CESA),October 4-6, 2006, Beijing, China.
[7] T. V. Sagar K. S. Rao ;S. R. M. Prasanna ; S. Dandapat Shiqing Zhang, “Speech emotion recognition based on Fuzzy Least Squares Support Vector Machine”,7th World Congress on Intelligent Control and Automation , 2008
[8] Ashwini Yerlekar, Devika Deshmukh , ”Investigating Sentiment analysis using Clustering and NLP tools ”, International Journal of Computer Sciences and Engineering ,Vol .7 , Issue.1, pp.344-347 Jan-2019
[9] Y. M. Rajput, S. Abdul Hannan, M. Eid Alzahrani, Ramesh R. Manza, Dnyaneshwari D. Patil ,“EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques – A Review”, International Journal of Computer Sciences and Engineering ,Vol.7 , Issue.1 , pp.615-618, Jan-2019
[10] Pratik Ghosalkar, Sarvesh Malagi, Vatsal Nagda, Yash Mehta, Pallavi Kulkarni ,”English Grammar Checker”,International Journal of Computer Sciences and Engineering ,Vol.4 , Issue.3 , pp.98-100, Mar-2016
[11] Pooja Shivratri1, Preeti Kshirsagar, Rashmi Mishra Ronit Damania and Nandana Prabhu , “Resume Parsing and Standardization”,,International Journal of Computer Sciences and Engineering Vol.3 , Issue.3 , pp.129-131, Mar-2015
[12] Suma S L, Sarika Raga ,”Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm”, International Journal of Scientific Research in Computer Sciences and Engineering ,Vol.6 , Issue.5 , pp.6-10, Oct-2018
[13] Ketan Sarvakar, Urvashi K Kuchara,”
Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, International Journal of Scientific Research in Computer Sciences and Engineering , Vol.6 , Issue.3 , pp.8-12, Jun-2018
[14] G.Sowmiya, V. Kumutha, ”Facial Expression Recognition Using Static Facial Images”,International Journal of Scientific Research in Computer Sciences and Engineering ,Vol.6 , Issue.2 , pp.72-75, Apr-2018

S.K. Wagh, D.O. Agarwal, L.S Hire, J.N. Choudhary, "MockAI : Smart Recruitment Counseling using Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.261-265, 2019.

In today`s world the wireless sensor network has great significant in application like defense surveillance, patient health monitoring, traffic control etc. As WSN utilize radio frequencies so there is threat of interference in network. These threats also include distributed denial of service in which the messages that are sent over the network may be attacked by unauthorized user. It would harm the confidentiality of the network user and the services of network. There are various algorithm that are utilized to detect Sybil attack in WSN but these schemes only stress on prevention of attack after it is occurred. This would leads to the loss of data and more consumption of limited network resources. So in this work we introduce a new algorithm that is based on clustering based KNN along with Euclidean distance. It would detect earlier the Sybil attack in WSN and prevent the data loss. The parameters like throughput, energy consumption etc are utilized to analyze the performance of this technique.

KNN, WSN, Sybil detection

[1] C. Science and K. Mangalore, “A Two-tier Network based Intrusion Detection System Architecture using Machine Learning Approach,” pp. 42–47, 2016.
[2] P. Singh and A. Tiwari, “An Efficient Approach for Intrusion Detection in Reduced Features of KDD99 Using ID3 and Classification with KNNGA,” Proc. - 2015 2nd IEEE Int. Conf. Adv. Comput. Commun. Eng. ICACCE 2015, pp. 445–452, 2015.
[3] K. J. Chabathula, C. D. Jaidhar, and M. A. Ajay Kumara, “Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms,” pp. 1–6, 2015.
[4] H. Haddad Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Trans. Emerg. Top. Comput., vol. 6750, no. c, pp. 1–1, 2016.
[5] A. R. Onik, N. F. Haq, and W. Mustahin, “Cross-breed type Bayesian network based intrusion detection system (CBNIDS),” 2015 18th Int. Conf. Comput. Inf. Technol., pp. 407–412, 2015.
[6] Y. Canbay and S. Sagiroglu, “A Hybrid Method for Intrusion Detection,” 2015 IEEE 14th Int. Conf. Mach. Learn. Appl., pp. 156–161, 2015.
[7] M. Xie and J. Hu, “Evaluating host-based anomaly detection systems: A preliminary analysis of ADFA-LD,” Proc. 2013 6th Int. Congr. Image Signal Process. CISP 2013, vol. 3, no. Cisp, pp. 1711–1716, 2013.
[8] C. Huijun, S. Hong, and Z. Hong, “Early recognition of Internet service flow,” Proc. - 2013 Wirel. Opt. Commun. Conf. WOCC 2013, pp. 464–468, 2013.
[9] S. Behrozinia, R. Azmi, M. R. Keyvanpour, and B. Pishgoo, “Biological inspired anomaly detection based on danger theory,” IKT 2013 - 2013 5th Conf. Inf. Knowl. Technol., pp. 102–106, 2013.
[10] A. Daneshpazhouh and A. Sami, “Semi-supervised outlier detection with only positive and unlabeled data based on fuzzy clustering,” 5th Conf. Inf. Knowl. Technol., pp. 344–348, 2013.
[11] T. Weiming and C. Hongzhi, “An Improved Feature Selection Algorithm Based on MAHALANOBIS Distance for Networl < Intrusion Detection,” pp. 69–73, 2013.
[12] S. Gopal, Y. Yang, K. Salomatin, and J. Carbonell, “Sctatistical learning for file-type identification,” Proc. - 10th Int. Conf. Mach. Learn. Appl. ICMLA 2011, vol. 1, no. DiiD, pp. 68–73, 2011.
[13] P. M. Mafra, V. Moll, J. Da Silva Fraga, and A. O. Santin, “Octopus-IIDS: An anomaly based intelligent intrusion detection system,” Proc. - IEEE Symp. Comput. Commun., pp. 405–410, 2010.
[14] H. Yu, P. P. K. Chan, W. W. Y. Ng, and D. S. Yeung, “Apply randomization in KNN to make the adversary harder to attack the classifier,” 2010 Int. Conf. Mach. Learn. Cybern. ICMLC 2010, vol. 1, no. July, pp. 179–183, 2010.
[15] Z. Wang et al., “Detecting Malicious Server Based on Server-to-Server Realation Graph,” 2016 IEEE First Int. Conf. Data Sci. Cybersp., pp. 698–702, 2016.

Rajeev Bedi, Baljinder Singh, Meenakshi Devi, "Position Depended Sybil Attack Detection using Efficient KNN technique with Clustering," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.266-272, 2019.

In current scenario the rapid growth in the size of generated data is so huge and complex that traditional data processing application tools and platforms are inadequate to deal with it. Therefore, the big data require suitable analysis mechanisms for data processing and analysis in an efficient and effective manner. Consequently, developing and designing new scalable data mining techniques is very important and necessary mission for researchers and scientists in the last years. Scaling is the ability of the system to adapt to increased demands in terms of data processing. To support big data processing, different platforms incorporate scaling in different forms. We had tried to analyze these platforms on the basis of their performance in different environment.

Big data, Scalability, Hadoop

[1] Shao, H., L. Rao, Z. Wang, X. Liu, Z. Wang and K. Ren., “Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets”, IEEE Trans. Parall. Distr. Syst., Vol. 25, No. 10, pp. 2659–2669 , 2014.
[2] SWDS Li, J., Bao, Z. and Z. Li, “Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs”, IEEE Trans. on Smart Grid, Vol. 6, No. 2, pp. 737–747, 2015.
[3] Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014.
[4] Singh, K. and R. Kaur, “Hadoop: Addressing Challenges of Big Data”, 2014 IEEE Int. Advance Computing Conf., Navi Mumbai, pp. 686-689, India, 2014.
[5] Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014
[6] Shao, H., L. Rao, Z. Wang, X. Liu, Z. Wang and K. Ren., “Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets”, IEEE Trans. Parall. Distr. Syst., Vol. 25, No. 10, pp. 2659–2669 , 2014.
[7] Singh, K. and R. Kaur, “Hadoop: Addressing Challenges of Big Data”, 2014 IEEE Int. Advance Computing Conf., Navi Mumbai, pp. 686-689, India, 2014.
[8] Sun, D., G. Fu, X. Liu and H. Zhang, “Optimizing Data Stream Graph for Big Data Stream Computing in Cloud Datacenter Environments”, Int. J. of Advancements in Computing Technology, Vol. 6, No. 5, pp. 53–65, 2014.
[9] K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi, "Challenges and Opportunities with Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16- 20, 2017
[10] Sun, D., G. Zhang, S. Yang, Zheng W., S. U.Khan and K. Li, “Re-stream: Realtime and Energy-efficient Resource Scheduling in Big Data Stream Computing Environments”, Information Sciences, No. 319, pp. 92-112, 2015.
[11] Mantripatjit Kaur, Anjum Mohd Aslam, "Big Data Analytics on IOT: Challenges, Open Research Issues and Tools", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018
[12] V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017.
[13] Shilpa Manjit Kaur, “BIG Data and Methodology- A review” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.

Ajay Kumar Bharti, Neha Verma, Deepak Kumar Verma, "A Review on Big Data Analytics Tools in Context with Scalability," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.273-277, 2019.

Nowadays, every individual has become health-conscious and wants to be protected against diseases. Everyone wants to eat a balanced diet and also keep a track of the daily calorie intake. This work in the image processing domain serves this purpose as it determines the calorie content from the image itself. For the purpose of calorie calculation, an image of the food sample is required. Initially, a person captures an image of apple; which is later processed in MATLAB. One of the key requirements of this work is that the images be taken at a constant distance of 25-35 cms from the apple. The different varieties of apples that are taken into consideration are: dark red, lighter red and one with red with yellowish parts. In the pre-processing stage, this image is read and converted into gray. Later, at the segmentation stage, the image is analyzed using K means clustering algorithm to extract the image of apples. After this, the feature extraction process takes place, which includes extraction of features like color, shape, size, weight and texture. The determination of weight is undertaken by calculating the number of pixels. Next, in the classification step, SVM classifier is used in which, the apple will be analyzed using some nutritional tables and the calorie value will be displayed to the person.

Image processing, k-means, MATLAB, segmentation, SVM classifier

[1] M. B. E. Livingstone, P. J. Robson, and J. M. W. Wallace, “Issues in dietary intake assessment of children and adolescents,” British Journal of Nutrition, vol. 92, no. S2, pp. S213–S222, 2004.
[2] S. Mingui, L. Qiang, K. Schmidt, Y. Lei, Y. Ning, J. D. Fernstrom, et al., “Determination of food portion size by image processing,” in Proc. 30th Annu. Int. Conf. Eng. Med. Biol. Soc., Aug. 2008, pp. 871–874.
[3] B. Schölkopf, A. Smola, R. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput., vol. 12, no. 5, pp. 1207–1245, May 2000.
[4] L. E. Burke, M. Warziski, T. Starrett, J. Choo, E. Music, S. Sereika, et al., “Self-monitoring dietary intake: Current andfuture practices,” J. Renal Nutrition Off. J. Council Renal Nutrition Nat. Kidney Found., vol. 15, no. 3, pp. 281–290, 2005.
[5] C. Gao, F. Kong, and J. Tan, “Healthaware: Tackling obesity with health aware smart phone systems,” in Proc. IEEE Int. Conf. Robot. Biometics, Dec. 2009, pp. 1549–1554.
[6] B. Kartikeyan and A. Sarkar, “An identification approach for 2-D autoregressive models in describing textures,” CVGIP, Graph. Models Image Process., vol. 53, no. 2, pp. 121–131, 1993.
[7] A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters,” Pattern Recognition., vol. 24, no. 12, pp. 1167–1186, 1991.
[8] C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowl., vol. 2, no. 2, pp. 121–167, 1998.
[9] J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297
[10] Jain, Ramesh & Kasturi, Rangachar & G. Schunck, Brian. (1995). Machine Vision.
[11] Cristianini, Nello; and Shawe-Taylor, John; An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000, ISBN 0-521-78019-5 (SVM Book).

Lavanya Bhaskar, Lathika, "Measurement of Calorie from Image of an Apple," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.278-280, 2019.

In academic institutes or universities, there is always a need to find experts in different subjects. The experts are required as resource persons for various workshop, seminars, and conferences. There is always need of experts for working on various research committees. There are various online research groups available on World Wide Web. Many researchers from various domains relate to each other via these online research groups. This paper describes a conceptual framework which finds experts from online research groups for various academic events and committees.

Expert Finding System, Academic event, Research committee, Online research group, ResearchGate, Google Scholar

[1] Rostami Peyman and Mahmood Neshati, “T-shaped grouping: Expert finding models to agile software teams retrieval”, Expert Systems with Applications 118, PP: 231-245, (2019).
[2] Gharebagh, Sajad Sotudeh, Peyman Rostami, and Mahmood Neshati, “T-Shaped Mining: A Novel Approach to Talent Finding for Agile Software Teams”, European Conference on Information Retrieval, PP: 411-423, Springer, Cham, (2018).
[3] Wang, Xianzhi, Chaoran Huang, Lina Yao, Boualem Benatallah, and Manqing Dong, “A survey on expert recommendation in community question answering”, Journal of Computer Science and Technology 33, no. 4 PP: 625-653, (2018).
[4] Kundu Dipankar and Deba Prasad Mandal, “Formulation of a hybrid expertise retrieval system in community question answering services”, Applied Intelligence PP: 1-15, (2018).
[5] Liang Shangsong, Xiangliang Zhang, Zhaochun Ren, and Evangelos Kanoulas, “Dynamic embeddings for user profiling in twitter”, In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1764-1773. ACM, (2018).
[6] Liu Chao, Dan Yang, Xiaohong Zhang, Baishakhi Ray, and Md Masudur Rahman, “Recommending GitHub Projects for Developer Onboarding”, IEEE Access 6 PP: 52082-52094, (2018).
[7] Silvello Gianmaria, Georgeta Bordea, Nicola Ferro, Paul Buitelaar, and Toine Bogers, “Semantic representation and enrichment of information retrieval experimental data”, International Journal on Digital Libraries 18, no. 2 PP: 145-172, (2017).
[8] Liang Shangsong, and Maarten de Rijke, “Formal language models for finding groups of experts”, Information Processing & Management 52, no. 4 PP: 529-549, (2016).
[9] Alsaleh Saad, and Haryani Haron, “The Most Important Functional and Non-Functional Requirements of Knowledge Sharing System at Public Academic Institutions: A Case Study”, Lecture Notes on Software Engineering 4, no. 2 PP: 157, (2016).

Snehalata B. Shirude, Satish R. Kolhe, "A Conceptual Framework of Expert Finding System for Academic Events and Committess," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.281-283, 2019.

This paper presents the use of a Thyristor controlled reactor with local signal to mitigate subsynchronous resonance in a power system. The dynamic phasor base model and dq0 model of two area power system is considered in this paper. Modal speed deviation is used as local signal in both models. This local signal modulates the firing angle of TCR through PI controller in dynamic phasor base model and dq0 model of power system . This paper shows that the designing parameter of PI controller and mitigation behaviour of SSR using local signal in dynamic phasor based model and dq0 model closely match with each other.

Dynamic Phasor Model, Thyristor controlled reactor , subsynchronous resonance

[1] K. R. Padiyar,” Power System Dynamics: Stability and Control” India: BS Publications, 2002
[2] A. A. Edris, “Subsynchronous resonance countermeasure using phase imbalance ”IEEE Transaction on Power System, vol. 8, no. 4, pp. 1438–1447, Nov. 1993.
[3] L. Fan, Z. Miao “Mitigating SSR Using DFIG-Based Wind Generation” IEEE Transaction on Sustainable Energy,vol.3, No. 3, July 2012
[4] IEEE Subsynchronous Resonance Task Force, “First Benchmark Model for Computer Simulation of Subsynchronous Resonance ” IEEE Transaction on Power System, vol. no. 5, pp. 1565–1572, Sept. 1977.
[5] A.Stankovic,B.Lesieutre,T.Aydin, “Modeling and analysis of single-phase induction machines with dynamic phasors” IEEE Transaction on Power System, vol. 14, no. 1, pp. 9–14, Feb. 1999.
[6] A. M. Stankovic, S. R. Sanders, T. Aydin, “Dynamic phasors in modeling and analysis of unbalanced polyphase AC machines ” IEEE Transaction on Energy Convers., vol. 17, no. 1, pp. 107–113, Mar. 2002.
[7] A. M. Stankovic, B. C. Lesieutre, T. Aydin, “Applications of generalized averaging to synchronous and induction machines ” in Proc.North Amer. Power Symp., 1996, pp. 277–282.
[8] A. M. Stankovic , T. Aydin, “Analysis of asymmetrical faults in power systems using dynamic phasors ” IEEE Transaction on Power System, vol.15, no. 3, pp. 1062–1068, Aug. 2000.
[9] T. H. Demiray,“Simulation of power system dynamics using dynamic phasor models” Ph.D. dissertation, Dept. Elect. Eng., Swiss Fed. Inst.Technol.,, Zurich, Switzerland, 2008.
[10] P. Mattavelli, G Verghese, A. M. Stankovic, “Phasor dynamics of thyristor-controlled series capacitor systems” IEEE Transaction on Power System, vol. 12, no. 3, pp. 1259–1267, Aug. 1997.
[11] Mahipalsinh C. Chudasama and A. M. Kulkarni, “Dynamic Phasor Analysis of SSR Mitigation Schemes Based on Passive Phase Imbalance,” IEEE Trans. Power Syst, vol. 26, no. 3, pp. 1668-1676, Aug. 2011.
[12] A.R.Sedighi,” A new model for high impedance fault in electrical distribution system”, Isroset-Journal (IJSRCSE), Vol.2 , Issue.4 , pp.6-12, Jul-2014
[13] A. Yadav, V.K. Harit,”Fault identification in substation by using neuro-fuzzy technique”, Isroset-Journal (IJSRCSE), Vol.4 , Issue.6 , pp.1-7, Dec-2016

M.G.Siddh, "Damping Subsynchronous Resonance in Dynamic Phasor and dq0 model using Thyristor Controlled Reactor," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.284-288, 2019.

In today’s era there is a huge amount of data available with health care; however, the knowledge about the data is rather poor. So there is a need to process that enormous size of medical dataset instead of just storing extract valuable information or useful knowledge. Data mining is the process of extracting hidden knowledge from large volumes of raw data using techniques like statistical analysis, machine learning, clustering, neural networks and genetic algorithms. A logical combination of multiple pre-existing techniques or Hybrid algorithms for data mining to enhance performance and provide better results. Data mining is used to discover hidden patterns and relationships out of data and presenting it in a form that can be easily understood. Data mining plays an important role in disease prediction. Data Mining is used intensively in the medical field to predict diseases such as heart disease, diabetes, breast cancer etc. In this paper, a survey is carried out on several single and hybrid data mining approaches used for disease prediction.

Data mining, Data Mining Techniques, Hybrid Approach, Diseases

[1] N. Prabakaran, R. Kannadasan, “Prediction of Cardiac Disease Based on Patient’s Symptoms”, In the Proceedings of Second International Conference on Inventive Communication and Computational Technologies, India, pp. 794 – 799, 2018.
[2] P. Giudici, “Applied Data Mining: Statistical Methods for Business and Industry”, Wiley Publisher, New York, 2003.
[3] J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kauffmann Publishers, San Francisco, 2001.
[4] G. Singh, K. Bagwe, S. Shanbhag, S. Singh , S. Devi, “Heart Disease Prediction using Naïve Bayes”, International Research Journal of Engineering and Technology, Vol. 4, No.3, pp.1-3,2017.
[5] F. Babič, J. Olejár, Z. Vantová, J. Paralič, “Predictive and Descriptive Analysis for Heart Disease Diagnosis”, In the Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, Czech Republic, pp. 155 - 163, 2017.
[6] Carlos O., “Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction”, Ieee Transactions on Information Technology in Biomedicine, Vol. 10, No. 2, pp.334–343, 2006.
[7] P. Manivannan , P. Isakki Devi, “Dengue Fever Prediction Using K-Means Clustering Algorithm”, In the Proceedings of IEEE International Conference on Intelligent Techniques In Control, Optimization and Signal Processing, India, pp. 1 – 5, 2017.
[8] A. Ahlawat, B. Suri, “Improving classification in data mining using hybrid algorithm”, In the Proceedings of the 2016 1st India International Conference on Information Processing, India, pp. 1-4, 2016.
[9] C. S. Dangare, S. S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications, Vol. 47, No.10, pp.44-48, 2012.
[10] B. Milovic, “Prediction and Decision Making in Health Care using Data Mining”, International Journal of Public Health Science, Vol. 1, No. 2, pp. 69-78, 2012.
[11] L. Sun, S. Yoshida, Y. Liang, “A novel support vector and K-Means based hybrid clustering algorithm”, Proceedings of the 2010 IEEE International Conference on Information and Automation, China, pp.126-130, 2010.
[12] Cp. Prathibhamol, A. Suresh, G. Suresh, “Prediction of Cardiac Arrhythmia type using Clustering and Regression Approach (P-CA-CRA)”, In the Proceedings of International Conference on Advances in Computing, Communications and Informatics, India, pp. 51 – 54, 2017.
[13] H. Kahramanli, N. Allahverd, “Design of a hybrid system for the diabetes and heart diseases”, Expert Systems with Applications, Vol. 35, No.1-2, pp.82-89, 2008.
[14] A. Khemphila, V. Boonjing, “Heart Disease Classification Using Neural Network and Feature Selection”, In the Proceedings of the 2011 21st International Conference on Systems Engineering, USA, pp.406-409, 2011.
[15] A. G. Karegowda, V. Punya, M. A. Jayaram, A. S. Manjunath, “ Rule based classification for diabetic patients using cascaded k-means and decision tree c4.5”, International Journal of Computer Applications, Vol. 45, No.12, pp.45-50, 2012.
[16] S. U. Amin, K. Agarwal, R. Beg, “Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors”, In the Proceedings of the IEEE Conference on Information and Communication Technologies, India, pp. 1227-1231, 2013.
[17] N. Yilmaz, O. Inan, M. S. Uzer, “A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases” , Springer Science + Business Media, New York, pp.38-48, 2014.
[18] N. Singhal, M. Ashraf, “Performance Enhancement of Classification Scheme in Data Mining using Hybrid Algorithm”, In the Proceedings of the International Conference on Computing, Communication and Automation, India, pp.138-141, 2015.
[19] K. Gomathi, D. S. Priyaa, “Multi Disease Prediction using Data Mining Techniques”, International Journal of System and Software Engineering, Vol. 4, No.2, pp.12-14, 2016.
[20] C. Kalaiselvi, “Diagnosis of Heart Disease Using K-Nearest Neighbor Algorithm of Data Mining”, In the Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development , India, pp. 3099-3103, 2016.
[21] K. Bhatia, R. Syal, “Predictive Analysis Using Hybrid Clustering in Diabetes Diagnosis”, In the Proceedings of the Recent Developments in Control, Automation & Power Engineering, India, pp. 447 – 452, 2017.
[22] S. Palaniappan, R. Awang, “Intelligent heart disease prediction system using data mining techniques”, IJCSNS International Journal of Computer Science and Network Security, Vol. 8, No.8, pp. 108-115, 2008.

Niyati I. Patel, Hiren R. Patel, "A Survey on Prediction of Disease with Data Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.289-293, 2019.

Cloud computing provides storing and accessing of your data over the internet. Cloud computing delivers computing services (servers, databases, networking, software etc.) over the internet. There are various advantages of cloud computing including Virtual computing environment, On-demand services, Maximum resource utilization and easier use of services etc. Still, there are numerous issues in cloud computing related to Security, Resource provisioning, Server consolidation, and Virtual machine migration. Load Balancing is an essential task in the Cloud Computing environment to achieve maximum utilization of resources, minimize the response time and maximize the throughput of the overall system. Load balancing algorithms increase the efficiency of the system by equally distributing the workload among the completion process. In this paper, we have presented the performance analysis of various load balancing algorithms based on various dependent parameters by considering two main load balancing approaches: static and dynamic. The both types of the load balancing algorithm have some advantages as well as disadvantages. The main purpose is to analyze different algorithms based on the time factor.

Cloud computing, Load balancing, Static load balancing, Dynamic load balancing

[1] R. Buyya, J. Broberg, A.M. Goscinski, “Cloud computing: Principles and paradigms”, John Wiley & Sons, 2010.
[2] Y. Jadeja, K. Modi, “Cloud computing-concepts, architecture and challenges”, In Computing, International Conference on Electronics and Electrical Technologies, Kumaracoil, India, pp. 877-880, 2012.
[3] G. Rastogi, R. Sushil, “Analytical literature survey on existing load balancing schemes in cloud computing”, International Conference on In Green Computing and Internet of Things, Noida, India, pp. 1506-1510, 2015.
[4] A. S. Milani, N. J. Navimipour, “Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends.” Journal of Network and Computer Applications, Vol.71, pp.86-98, 2016.
[5] N. Rathore, I. Chana, “Load balancing and job migration techniques in grid: a survey of recent trends.” Wireless personal communications, Vol.79, No.3, pp. 2089-2125, 2014.
[6] K. Mahajan, A. Makroo, D. Dahiya, “ Round robin with server affinity: a VM load balancing algorithm for cloud based infrastructure”, Journal of information processing systems, Vol.9, No.3, pp. 379-394, 2013.
[7] P. Samal,P. Mishra, “Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing”, International Journal of computer science and Information Technologies, Vol.4, No.3, pp.416-419, 2013.
[8] V. Shinde, A. Dange, M.A. Lambay, “Load Balancing Algorithms in Cloud Computing”, International Journal of computer science trends and technology, No.4, pp.75-81, 2016.
[9] U. Bhoi, P.N. Ramanuj, “Enhanced max-min task scheduling algorithm in cloud computing”, International Journal of Application or Innovation in Engineering and Management, Vol.2, No.4, pp.259-264, 2013.
[10] H. Chen, F. Wang, N. Helian, G. Akanmu, “User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing”, National Conference on Parallel Computing Technologies, Bangalore, India, pp.1-8, 2013.
[11] S. Ray, A. De Sarkar, “Execution analysis of load balancing algorithms in cloud computing environment”, International Journal on Cloud Computing: Services and Architecture, Vol.2, No.5, pp.1-13, 2012.
[12] O. M. Elzeki, M. Z. Reshad, M. A. Elsoud, “Improved max-min algorithm in cloud computing”, International Journal of Computer Applications, Vol.50, No.12, pp.22-27, 2012.
[13] J. Uma, V. Ramasamy, Kaleeswaran, “A Load Balancing Algorithms in Cloud Computing Environment-A Methodical Comparison”, International Journal of Advanced Research in Computer Engineering & Technology, Vol.3, No.2, pp.79-82, 2014.
[14] C. L. Hung, H. H. Wang, Y. C. Hu, “Efficient load balancing algorithm for cloud computing network”, International Conference on Information Science and Technology, pp.28-30, 2012.
[15] K. Kaur, A. Narang, K. Kaur, “Load balancing techniques of cloud computing”, International Journal of Mathematics and Computer ReseDori, Vol.1, No.3, pp.103-108, 2013.
[16] M. Dorigo, “Optimization, Learning and Natural Algorithms”. Ph.D. Thesis, Politecnico di Milano, Italy, 1992.
[17] M. Padmavathi, S. M. Basha, “Dynamic and elasticity ACO load balancing algorithm for cloud computing”, International Conference on Intelligent Computing and Control Systems, Madurai, India, pp.77-81, 2017.
[18] S. K. Mishra, B. Sahoo, P. S. Manikyam, “Adaptive scheduling of cloud tasks using ant colony optimization”, In Proceedings of the 3rd International Conference on Communication and Information Processing, Tokyo, Japan, pp.202-208, 2017.
[19] P. V. Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments”, Applied Soft Computing, Vol.13, No.5, pp.2292-2303, 2013.
[20] H. Gupta, K. Sahu, “Honey bee behavior based load balancing of tasks in cloud computing”, International journal of Science and Research, Vol.3, No.6, 2014.
[21] A. Soni,G. Vishwakarma,Y. K. Jain, “A bee colony based multi-objective load balancing technique for cloud computing environment”, International Journal of Computer Applications, Vol.114, No.4, 2015.
[22] K. Dasgupta,B. Mandal, P. Dutta,J. K. Mandal,S. Dam, “A genetic algorithm (ga) based load balancing strategy for cloud computing”, Procedia Technology, No.10, pp.340-347, 2013.
[23] S. A. Hamad, F. A. Omara, “Genetic-based task scheduling algorithm in cloud computing environment”, International Journal of Advanced computer Science and Applications, Vol.7, No.4, pp.550-556, 2016.
[24] T. Wang, Z. Liu, Y. Chen, Y. Xu, X. Dai, “Load balancing task scheduling based on genetic algorithm in cloud computing”, In International Conference on Dependable, Autonomic and Secure Computing, Dalian, China, pp.146-152, 2014.
[25] G. Joshi,S. K. Verma, “Load balancing approach in cloud computing using improvised genetic algorithm: a soft computing approach”, International Journal of Computer Applications, Vol.122, No.9, 2015.
[26] A. A. Beegom, M. S. Rajasree, “ A particle swarm optimization based pareto optimal task scheduling in cloud computing”, In International Conference in Swarm Intelligence, Springer, Cham, pp.79-86, 2014.
[27] K. Pan, J. Chen, “Load balancing in cloud computing environment based on an improved particle swarm optimization”, In International Conference on Software Engineering and Service Science, Beijing, China, pp.595-598, 2015.
[28] M. A. Rodriguez, R. Buyya, “Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds”, IEEE transactions on Cloud Computing, Vol.2, No.2, pp.222-235, 2014.

J. M. Tandel, H. R. Patel, "A Survey of Load Balancing Algorithms in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.294-299, 2019.

Big Data revolution is transforming the way we live.Healthcare industry generates huge data about every patient but accessing, managing and interpreting the data are critical to creating actionable insights for better care and efficiency.Clinical trends also play a role in the rise of Big Data in Healthcare.Furthermore, big data and machine learning will continually and drastically improve every area of the healthcare industry over the next decade as big data techniques become more refined. This paper presents the role of Big Data analysis in healthcare and various shortcomings of traditional machine learning algorithms.

Big Data, Machine learning, Healthcare Analysis, Feature Selection

[1] F.J. Martin-Sanchez, V. Aguiar-Pulido, G.H. Lopez-Campos, N. Peek, L. Sacchi, Secondary use and analysis of big data collected for patient care, IMIA Yearbook 26 (2017) 1–10, http://dx.doi.org/10.15265/IY-2017-008.
[2] Y. Wang, N. Hajli, Exploring the path to big data analytics success in healthcare, J.Bus. Res. 70 (2017) 287–299, http://dx.doi.org/10.1016/j.jbusres.2016.08.002.
[3] B. Cyganek, M. Graña, B. Krawczyk, A. Kasprzak, P. Porwik, K. Walkowiak, M. Woźniak, A survey of big data issues in electronic health record analysis, Appl.Artif. Intell. 30 (2016) 497–520, http://dx.doi.org/10.1080/08839514.2016.1193714.
[4] Suthaharan, Shan. "Big data classification: Problems and challenges in network intrusion prediction with machine learning." ACM
SIGMETRICS Performance Evaluation Review 41.4 (2014): 70-73.
[5] RAO, BLS PRAKASA. "of the Notes: Brief Notes on BIG DATA: A Cursory Look." (2015).
[6] Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health Information Science and Systems 2.1 (2014): 3.
[7] Hermon, Rebecca, and Patricia AH Williams. "Big data in healthcare: What is it used for?." (2014).
[8] Groves, Peter, et al. "The ‘big data revolution in healthcare." McKinsey Quarterly (2013).
[9] Rama Satish, K. V., and N. P. Kavya. "Big data processing with harnessing Hadoop-MapReduce for optimising analytical workloads."Contemporary Computing and Informatics (IC3I), 2014 International Conference on. IEEE, 2014.
[10] Yu, Lei, and Huan Liu. "Feature selection for high-dimensional data: A fast correlation-based filter solution." ICML. Vol. 3. 2003.
[11] Liu, Huan, and Lei Yu. "Toward integrating feature selection algorithms for classification and clustering." Knowledge and Data Engineering, IEEE Transactions on 17.4 (2005): 491-502.
[12] Liu, Huan, and Hiroshi Motoda, eds. Computational methods of feature selection. CRC Press, 2007.
13] Tomar, Divya, and Sonali Agarwal. "A survey on Data Mining approaches for Healthcare." International Journal of Bio-Science and Bio-Technology 5.5 (2013): 241-266.
[14] Wang, Dingxian, Xiao Liu, and Mengdi Wang. "A DT-SVM Strategy for Stock Futures Prediction with Big Data." Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on. IEEE,2013.
[15]. Fahad, Adil, et al. "A survey of clustering algorithms for big data: Taxonomy and empirical analysis." Emerging Topics in Computing, IEEE Transactions on 2.3 (2014): 267-279.
[16]Dharavath, Ramesh, and Abhishek Kumar Singh. "Entity Resolution-Based Jaccard Similarity Coefficient for Heterogeneous Distributed Databases." Proceedings of the Second International Conference on Computer and Communication Technologies. Springer India, 2016.
[17]Kesavaraj, G., and S. Sukumaran. "A study on classification techniques in data mining." Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on.IEEE, 2013.
[18] Cavallaro, Gabriele, et al. "On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods." (2015).
[19]Jen, Chih-Hung, et al. "Application of classification techniques on development an early-warning system for chronic illnesses." Expert Systems with Applications 39.10 (2012): 8852-8858.
[20]Zuo, Wan-Li, et al. "Effective detection of Parkinson`s disease using an adaptive fuzzy k-nearest neighbour approach." Biomedical SignalProcessing and Control 8.4 (2013): 364-373.
[21] Fahad, Adil, et al. "A survey of clustering algorithms for big data: Taxonomy and empirical analysis." Emerging Topics in Computing, IEEE Transactions on 2.3 (2014): 267-279.
[22]Belciug, Smaranda, et al. "Clustering-based approach for detectingbreast cancer recurrence." Intelligent Systems Design and Applications(ISDA), 2010 10th International Conference on. IEEE, 2010.
[23] Tomar, Divya, and Sonali Agarwal. "A survey on Data Mining approaches for Healthcare." International Journal of Bio-Science andBio-Technology 5.5 (2013): 241-266.
[24] Bijalwan, Vishwanath, et al. "KNN based machine learning approach for text and document mining." International Journal of Database Theory and Application 7.1 (2014): 61-70.
[25] Gelman, Andrew, et al. Bayesian data analysis. Vol. 2. London:Chapman & Hall/CRC, 2014.

A. Thammi Reddy, M. Nagendra, "A Survey: Big data Analysis in Healthcare using machine Learning Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.300-307, 2019.