Volume-7 , Issue-6 , Jun - 2019 Go Back

The aim of the study was to extract pectin from orange peels. Orange peels are a major commercial source of pectin. Pectin is one of the major important product used in various applications such as food processing industries, pharmaceuticals and its traditional jelling agent for jam and jellies. In the past many researches working on the development of the part of the process technology needed for the extraction of value added products i.e. Pectin from orange peels. Many operating parameters are affecting on the extraction of pectin. It is necessary to understand the effect of various operating parameters on extraction of pectin, in present study effect of Temperature, Solvent used for extraction and time used for extraction where discussed. The effects of pH on extraction of pectin were also discussed; pH is one of the most important crucial parameter which effects on extraction of pectin. When the process conditions were varied, a maximum yield of 52% was obtained, when the temperature at 90°C, pH=1 by using citric acid as a solvent. This study extends the effect of operating parameters an extraction of pectin from orange peels.

Pectin, Extraction, Operating parameters, Orange peels

[1] Bagde, Prashansa P, Sumit Dhenge, and Swapnil Bhivgade. "EXTRACTION OF PECTIN FROM ORANGE PEEL AND LEMON PEEL."2017.
[2] Göğüş, Nihan, et al. "Evaluation of orange peel, an industrial waste, for the production of Aspergillus sojae polygalacturonase considering both morphology and rheology effects." Turkish Journal of Biology 38.4 537-548, 2014.
[3] Thakur, Beli R., et al. "Chemistry and uses of pectin—a review." Critical Reviews in Food Science & Nutrition 37.1 47-73, 1997.
[4] Aina, V. O., et al. "Extraction and characterization of pectin from peels of lemon (Citrus limon), grape fruit (Citrus paradisi) and sweet orange (Citrus sinensis)." British Journal of Pharmacology and Toxicology 3.6 259-262, 2012.
[5] Tiwari, Alok Kumar, et al. "Extraction and Characterization of Pectin from Orange Peels."International Journal of Biotechnology and Biochemistry 13.1 39-47, 2017.
[6] Pandharipande, Shekhar, and Harshal Makode. "Separation of oil and pectin from orange peel and study of effect of pH of extracting medium on the yield of pectin." Journal of Engineering Research and Studies 3.2 06-09, 2012.
[7] Sayah, Mohamed Yassine, et al. "Comparative Study on Pectin Yield According To the State of the Orange Peels and Acids Used." Int J InnovRes Sci Eng Technol 3 15658-15665, 2014.
[8] May, Colin D. "Industrial pectins: sources, production and applications." Carbohydrate polymers 12.1 79-99, 1990.
[9] Devanooru Krishnamurthy Bhavya, Shrilakshmi and Rao Suraksha, Value Added Products from Agriculture: Extraction of Pectin from Agro Waste Product Musa Acuminata and Citrus Fruit, Research Journal of Agriculture and Forestry Sciences ,Vol. 3(3), 13-18, March , 2015.
[10] Karr, A. L.,Cell wall bigenesis, in Plant Biochemistry,Bonner, J. and Varner, J. E., Eds., AcademicPress, New York, 405, 1976.

Lakshmana Naik R, J. Mamatha, J. Bhargavi, T. Nayomi, B. Hema, "Extraction of Pectin from Orange Peels and Optimization of Process Parameters," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1-6, 2019.

Chronic kidney disease (CKD) is a condition characterized by a gradual loss of kidney function over time. It includes risk of cardiovascular disease and end-stage renal disease. In this paper, we use Machine Learning approach for predicting CKD. In this paper, we present a comparative analysis of seven different machine learning algorithms. This study starts with twenty-four parameters in addition to the class attribute and twenty five percent of the data set is used to test the predictions. Algorithms are trained using fivefold cross-validation and performance of the system is assessed using classification accuracy, confusion matrix, specificity and sensitivity.

CKD, Machine Learning, Logistic Regression, Support Vector Machine, Random Forest

[1] Chaitanya Gupte and Shruti Gadewar, “Diagnosis of Parkinson’s Disease using Acoustic Analysis of Voice”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.14-18, 2017.
[2] Pallvi Dehariya, “An Artificial Immune System and Neural Network to Improve the Detection Rate in Intrusion Detection System”, International Journal of Scientific Research in Network Security and Communication, Vol.4, Issue.1, pp.1-4, 2016.
[3] Antje Erler, Martin Beyer, Juliana J. Petersen, Kristina Saal, Thomas Rath, Justine Rochon, Walter E. Haefeli and Ferdinand M. Gerlach, “How to improve drug dosing for patients with renal impairment in primary care – a cluster-randomized controlled trial”, BMC Family Practice, Vol.13, Issue.1, Article.91, pp.1-8, 2012.
[4] S. Venkata Lakshmi, M. K. Meena and N. S. Kiruthika, “Diagnosis of Chronic Kidney Disease using Random Forest Algorithms”, International Journal of Research in Engineering, Science and Management, Vol.2, Issue.3, pp.559-562, 2019.
[5] R. Xi, N. Lin and Y. Chen, “Compression and Aggregation for Logistic Regression Analysis in Data Cubes”, IEEE Transactions on Knowledge and Data Engineering, Vol.21, Issue.4, pp.479-492, 2009.
[6] R. G. Brereton, and G. R. Lloyd, “Support Vector Machines for classification and regression”, Analyst, Vol.135, Issue.2, pp.230-267, 2010.
[7] Galit Shmueli, Nitin R. Patel and Peter C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley Publishing, pp.250-268, 2010.
[8] Afzal Ahmad, Mohammad Asif and Shaikh Rohan Ali, “Review Paper on Shallow Learning and Deep Learning Methods for Network Security”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.45-54, 2018.
[9] J. Ross Quinlan, “C4.5: Programs for Machine Learning by J. Ross Quinlan.”, Morgan Kaufmann Publishers, Inc., pp.17-26, 1993.
[10] Deepika Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.33-37, 2018.
[11] M. S. Anbarasi and V. Janani, “Ensemble classifier with Random Forest algorithm to deal with imbalanced healthcare data”, In International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India, pp.1–7, 2017.
[12] Hanyu Zhang, Che-Lun Hung, William Cheng-Chung Chu, Ping-Fang Chiu and Chuan Yi Tang, “Chronic Kidney Disease Survival Prediction with Artificial Neural Networks”, In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, pp.1-6, 2018.

Bhawna Sharma, Sheetal Gandotra, Utkarsh Sharma, Rahul Thakur, Alankar Mahajan, "A Comparative Analysis of Different Machine Learning Classification Algorithms for Predicting Chronic Kidney Disease," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.8-13, 2019.

Since the rise in social media and interactive systems in recent decades, the automatic classification of age and gender has become relevant to most of the social platforms and human-computer interactions. To achieve this task, many methods are implemented, but somehow those are not effective with real-world images as most of the models are trained using images of limited dataset taken from lab settings which are generally constrained in nature. Such images do not contain variations of appearance which are usually observed in images of the real world such as social networks, online repositories, and websites. In this paper, we try to improve the performance by making use of the deep convolutional neural network (CNN). The proposed network architecture uses Adience benchmark for gender as well as age estimation and its performance are much better with real-world images of the face. Here in this paper, a suitable method is described for the detection of a face at real-time and estimation of their age and gender. It first detects whether a face is there or not in the image captured. If it is present, the face is detected and the region of face content is returned using colored square structure and returns their age and gender as a result. The convenient and easy hardware implementation for this method is by utilizing a Raspberry-Pi kit and camera, as it is a minicomputer of credit card size. To build an effective age and gender estimator, the concept of Deep Convolution Neural Network is used. The input data contains different age groups of male and female face images. Over captured faces’ feature extraction are compared with this input data to evaluate the age as well as the gender of the person.

Raspberry Pi, Human face detection, OpenCV, Age, and Gender detection, Convolutional Neural Network

[1]. Anusha, A. V., J. K. Jayasree, Anusree Bhaskar, and R. P. Aneesh, "Facial expression recognition and gender classification using facial patches," In 2016 International Conference on Communication Systems and Networks (ComNet), pp. 200-204, IEEE, 2016.
[2]. Geng, Xin, Zhi-Hua Zhou, and Kate Smith-Miles, "Automatic age estimation based on facial aging patterns," IEEE Transactions on pattern analysis and machine intelligence 29, no. 12 (2007), pp. 2234-2240, 2007.
[3]. Sethuram, Amrutha, Jason Saragih, Karl Ricanek, and Benjamin Barbour, "Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models," In 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications, and Systems (BTAS), pp. 135-142, IEEE, 2012.
[4]. Da`San, Mohammad, Amin Alqudah, and Olivier Debeir, "Face detection using Viola and Jones method and neural networks," In 2015 International Conference on Information and Communication Technology Research (ICTRC), pp. 40-43, IEEE, 2015.
[5]. Angus, Alvin Titus R., John Alvin P. Guillen, Maurice Laurence G. Lenon, Ray Justin C. Principe, Gerald P. Feudo, and Kanny Krizzy D. Serrano, "A clustering system utilizing acquired age and gender demographics thru facial detection and recognition technology," In 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-6, IEEE, 2017.
[6]. Gauswami, Mitulgiri H., and Kiran R. Trivedi, "Implementation of machine learning for gender detection using CNN on the raspberry Pi platform," In 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 608-613, IEEE, 2018.
[7]. Liu, Xuan, Junbao Li, Cong Hu, and Jeng-Shyang Pan, "Deep convolutional neural networks-based age and gender classification with facial images," In 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), pp. 1-4. IEEE, 2017.
[8]. Aishwarya Admane, Afrin Sheikh, Sneha Paunikar, Shruti Jawade, Shubhangi Wadbude, Prof. M. J. Sawarkar , "A Review on Different Face Recognition Techniques", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5 Issue 1, pp. 207-213, January-February 2019.
[9]. A. K. Gupta, S. Gupta, “Neural Network through Face Recognition”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.2, pp.38-40, April (2018) E-ISSN: 2320-7639.

Sumangala Biradar, Beena Torgal, Namrata Hosamani, Renuka Bidarakundi, Shruti Mudhol, "Age and Gender Detection System using Raspberry Pi," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.14-18, 2019.

Today various type of organizations interact with DATA; while sharing / storing / retrieving / communicating such huge data with Public; such as Government organization, Medical, Agricultures(land records) Insurance domain (in term of storing / archiving data/file) and other try to validate the digital document using digital signature . Some document might be used for a short time period, but here I am talking about sharing / store / archive the document for a longer period i.e. long time value (LTV), because if documents are important for future having future prospective need to be preserved for a longer period than expected. The main problem is how to ensure about, the document authenticity, Integrity, non-repudiation, and proof of existence for long-term. Here we put the document (.pdf) sign, which contains the information’s like (Certificate, certificate chain, signed message digest, revocation information and time stamp), and here also trying to provide the solution for replacement of applet in web based signing, newer approach will be available both for the Standalone as well as window services i.e. Time and Cost saving for the user.

Digital signature Algorithm (DSA), DSC, Alternative of Applet, web based digital signing, Long Term Value (LTV), TSA Certificate

[1] Mart´ın Vigil, Denise Demirel, Sheikh Mahbub Habib, Sascha Hauke,Johannes Buchmann, and Max M¨uhlh¨auser, “LoT: a Reputation-based Trust System for Long-term Archiving”, SECURWARE: The Tenth International Conference on Emerging Security Information, Systems and Technologies,2016.
[2] Martı´n Vigil ,Johannes Buchmann ,Daniel Cabarcas ,Christian Weinert , Alexander Wiesmaier “Integrity, authenticity, non-repudiation, and proof of existence for long-term archiving: A survey”, elsevier ,computer and security 20,16-32 ,2015.
[3] Dimitris Lekkas, Dimitris Gritzalis, “Cumulative notarization for long-term preservation of digital signatures”, elsevier , Computers & Security (2004) 23, 413-424,2004.
[4] Argyris Arnellos , Dimitrios Lekkas , Dimitrios Zissis , Thomas Spyrou ,John Darzentas, “Fair digital signing: The structural reliability of signed documents”, elsevier computers & security 30 (2011) 580-596,2011.
[5] Mart´in A. G. Vigil, Daniel Cabarcas,Alexander Wiesmaier, and Johannes Buch-mann, “Authenticity, Integrity and Proof of Existence for Long-Term Archiving: a Survey”.
[6] S. Ries, S. Habib, M. M¨uhlh¨auser, and V. Varadharajan, “Certainlogic:A logic for mod-eling trust and uncertainty”, in TRUST, vol. 6740, 2011, pp. 254-261.
[7] M. Vigil, J. Buchmann, D. Cabarcas, C. Weinert, and A. Wiesmaier, “Integrity, authenticity, non-repudiation, and proof of existence for longtermarchiving: A survey”, Computers & Security, vol. 50, no. 0, 2015,pp. 16–32.
[8] H. M. Gladney, “Preserving digital information”, Springer, 2007.
[9] C. Jee, “Nhs promises real-time digital health and care records by 2020”,nhs-promises-real-time-digital-health-care-records-by-2020-3585822/ [retrieved: June, 2016].
[10] Diffie, W., Hellman, “M.E.: New directions in cryptography”, IEEE Trans. on Information Theory IT-22(6), 644–654 (1976).
[11] Ravneet Kaur , Amandeep Kaur, “Digital Signature”, International Conference on Computing Sciences, (2012).
[12] Saba Mushtaq, A.H.Mir, “ignature Verification: A Study”, 4th International Conference on Computer and Communication Technology (ICCCT) (2013).
[13] Arvind K. Sharma, Satish.K.Mittal,"A Comprehensive Study on Digital-Signatures with Hash-Functions",International Journal of Computer Sciences and Engineering,Vol.-7, Issue-4, April 2019.
[14] A. Ghosh1, S. Karforma,"Authentication of Study Material in E-Learning using Digital Signature Algorithms",International Journal of Computer Sciences and Engineering,Vol.7, Special Issue.1, Jan 2019.

Anup Kumar Pandey, Anil Kumar Mahto, "Alternative of Applet for Digital Signature Long Term Value in Web Based Signing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.19-23, 2019.

Electronic text on internet can be used for many online activities similarly it can also be used for social movement activities. The electronic text through social movements can also be used to describe an issue, place blame, identify victims, propose a solution and appeal readers to take action on it. Texts such as these are framing documents. Framing is a unique concept in sociology & political science in which people interpret information and speak in favour or claim. Online communities are using frames on social media for their good or bad goals. Thus framing and contents in it have cumulative effect on sentiment of people which needs to be studied. Sentiment analysis explores attitudes, feelings, and expressed opinions regarding products, topics, or issues. The research presented here proposes a framework that applies statistical methods in text analytics to extend research in framing process to find sentiments expressed by people in frame. In research work, first phase is to pre-process text; it uses supervised machine learning methods that create a tone based term matrix. Second phase discover distinct patterns that characterize prominent frames by classifying the corpus into frames and non-frames. last phase aims to classify frames more specific into motivational, investigative and predictive on the basis of sentiments expressed in them so as to find out threat, cause or solution for an issue. The research presented here aims to develop a tool that will help social movement organizations and concerned authorities to portray issue and helps in organizing activities properly.

Sentiment, Tone, Frame, Context- Concept Quadruple

[1] Xiaowen Ding, Bing Liu, and Philip S. Yu “A holistic lexicon-based approach to opinion mining” Proceedings of the international conference on Web Search and web Data Mining, ACM, , pp. 231-240 2008.
[2] Alexander Conrad, Janyce Wiebe, and Rebecca Hwa, “Recognizing arguing subjectivity and argument tags, Proceedings of the Workshop on Extra Propositional Aspects of Meaning in Computational Linguistics” ACM,pp. 80-88 2012.
[3] Jack, R. E., O. G. Garrod, and P. G. Schyns “ Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time.” Current biology 24 (2), 187–192 2014.
[4] Liu, Bing. “Sentiment Analysis and Subjectivity. Handbook of Natural LanguageProcessing”, 2nd ed. Chapman and Hall: Florida, 2010.
[5] Calomiris, C. W., & Mamaysky, H. “How News and Its Context Drive Risk and Returns around the World” SSRN Electronic Journal 2017.
[6] Heston, S., and Sinha, N.. “News versus Sentiment: Predicting Stock Returns from News Stories.” Financial Analysts Journal, 73 2017.
[7] Laura Cruz, José Ochoa, Mathieu Roche, Pascal Poncelet. Dictionary-Based Sentiment Analysis Applied to a Specific Domain. SIMBIg: Symposium on Information Management and Big Data, Sep 2016.
[8] Kar Kei Lo and Michael Chau. “A Penny Is Worth a Thousand? Investigating the Relationship Between Social Media and Penny Stocks” ACM Trans. Manage. Inf. Syst. 9, 4, Article 14 35 pages (March 2019).
[9] Lamiaa Sinif and Bouchaib Bounabat. Approaching an optimizing open linked government data portal. In Proceedings of the 2nd International Conference on Smart Digital Environment (ICSDE`18), Faissal El Bouanani and Ahmed Habbani (Eds.). ACM, New York, NY, USA, 135-139. 2018.
[10] Anastasija Mensikova1, Chris A. Mattmann1,2. Ensemble Sentiment Analysis to Identify Human Trafficking in Web Data. In Proceedings of ACM Workshop on Graph Techniques for Adversarial Activity Analytics (GTA3 2018). ACM, New York, NY, USA 2018.
[11] Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017. A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579.
[12] Gaillat, Thomas, Sousa, Annanda, Zarrouk, Manel, & Davis, Brian “FinSentiA: sentiment analysis in English financial microblogs” Paper presented at the 25th French Conference on Natural Language Processing - TALN2018, Rennes, France, 14-18 May, 2018.
[13] Keith Stuart, Ana Botella, and Imma Ferri “ A Corpus-Driven Approach to Sentiment Analysis of Patient Narratives “ CILC 2016 (EPiC Series in Language and Linguistics, vol. 1), 2016.
[14] Moreno-Ortiz, J Fernández-Cruz “Identifying polarity in financial texts for sentiment analysis: a corpus-based approach“ Procedia-Social and Behavioral Sciences, 2015.
[15] https://en.wikipedia.org/wiki/Framing_(social_sciences)
[16] Eric P. S. Baumer, Elisha Elovic, Ying “Crystal” Qin, Francesca Polletta, Geri K. Gay1,"Testing and Comparing Computational Approaches for Identifying the Language of Framing in Political News",Human Language Technologies: The Annual Conference of the North American, Page No. 1472–1482, 2015.
[17] Bjorn Burscher, DaanOdijk, RensVliegenthart, Maarten de Rijke and Claes H. de Vreese, "Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis",Communication Methods and Measures, Volume 8, Page No. 190–206, 2014.
[18] Daan Odijk Bjorn Burscher Rens Vliegenthart Maarten de Rijke, "Automatic Thematic Content Analysis: Finding Frames in News", International Conference on Social Informatics, Springer, Page No. 333-345, 2013.
[19] Myers, T.A., Nisbet, M.C., Maibach, E. W. & Leiserowitz, A “A public health frame arouses hopeful emotions about climate change: A letter. Climatic Change” 113, 1105–1112 2012.
[20] Choi, Sujin, and Han Woo Park. "An exploratory approach to a Twitter-based community centered on a political goal in South Korea: Who organized it, what they shared, and how they acted." New Media & Society 16.1 2014.

S.V. Balshetwar, R.M. Tuganayat, G.B. Regulwar, "Frame Tone and Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.24-40, 2019.

Multithreaded programming has always presented a problem of race conditions which is one of the most common programming errors. If not handled properly, can lead to bugs with the potential to crash a system. A lot of work has been done in the past for detection of data races with a view to minimise the losses. Datarace can be detected at compile time (static race detection) and at runtime (dynamic race detection). This paper presents a study to understand the concept of parallel programming, race condition, semaphore, synchronization. We have also put in a detailed view on various techniques developed so far for dynamic data race detection.

Parallel Processing, Race Condition, Semaphore, LockSet, Happens Before, Hybrid, Dynamic Data Race Detection

[1] K. H. Eric Baudden, "Aspect Oriented Race Detection in Java," IEEE Transactions on Software Engineering, 2010.
[2] T. I. Konstantin Serebryany, "ThreadSanitizer - Data Race Detection in Practice," Communications of ACM, 2009.
[3] S. N. F. Cormac Flanagan, "Type Based Race Detection for Java," ACM, 2000.
[4] M. R. C Boyapati, "A prameterized type system for race free java program," ACM, 2001.
[5] R. E. S. A. T. David F Bacon, "Guava: A Dialect of Java without datarace," ACM, 2000.
[6] M. B. G. N. P. S. t. A. Stefan Savage, "Eraser: A Dynamic Data Race Detector for Multithreaded Programs," ACM Transactions on Computer Systems, vol. 15, no. 4, pp. 391-411, 1997.
[7] C. A. R. Hoare, "Monitors: An Operating Systems Structuring Concept," Communications of ACM, vol. 17, no. 10, 1974.
[8] L. Lamport, "Time, Clocks, and the ordering of Events in a Distributed System," Communications of ACM, vol. 21, no. 7, 1978.
[9] F. W. Arndt Muhlenfeld, "Runtime race detection for multi-threaded C++ server applications," ACM Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering, 2007.
[10] K. B. V. P. W. T. Ali Janessari, "Helgrind+: An efficient dynamic race detector," 23rd IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2009, Rome, Italy, , 2009.
[11] Z. M. B. B. P. P. Utpal Banerjee, "A Theory of Data Race Detection," Proceedings of the 2006 workshop on Parallel and distributed systems: testing and debugging, 2006.
[12] K. L. A. L. R. O. V. S. M. S. Jong-Deok Choi, "Efficient and Precise Datarace Detection for Multithreaded Object Oriented Programs," ACM, 2002.
[13] X. T. W. T. Markus Metzger, "User Guided Dynamic Data Race Detection," International journal of Parallel Programming, 2015.
[14] D. D. P. M. C. J. F. S. N. Benjamin Wester, "Parallelizing Data Race Detection," ACM, 2013.
[15] T. I. Konstantin Serebryany, "ThreadSanitizer: data race detection in practice," ACM, 2009.
[16] Z. Q. P. W. K Leung, "Data Race: tame the beast," Springer J Supercomput, 2010.
[17] C. Z. G. C. Baris Kasikei, "RaceMob: Crowdsouced Data Race Detection," ACM, 2013.
[18] S. N. F. Cormac Flanagan, "FastTrack: Efficint and Precise Dynamic Race Detection," ACM, 2009.
[19] K. H. Eric Boden, "Racer: Effective Race Detection using AspectJ," ACM, 2008.

Mithilesh Kumar Dubey, Devesh Lowe, Bhavna Galhotra, "A Study on Race Condition & Dynamic Data Race Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.41-46, 2019.

In the era of information, enormous different type of data has become available for decision making. Big data don’t refer to that data sets that is big, but also that is high in variety and velocity, which makes them hard to handle using by traditional tools and techniques. The quantity of data that we harvest and eat up is thriving aggressively in the digitized world. Increasing use of new innovations and social media generate vast amount of data that can earn splendid information if properly analysed. This large dataset generally known as big data, do not fit in traditional databases because of its’ rich size. Organizations need to manage and analyse big data for better decision making and outcomes. So, big data analytics is receiving a great deal of attention today. In healthcare, big data analytics has thepossibility of advanced patient care and clinical decision support. In this paper, we review the background and the various methods of big data analytics in healthcare. This paper also elaborates various platforms and algorithms for big data analytics and discussion on its advantages and challenges. This survey winds up with a discussion of challenges and future directions.

Big Data, Android, Hadoop, Big Data Mining, Predictive Analytics

[1] Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J Biomed Health Inform 2015;19:1193–1208.
[2] Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia Comput Sci 2015;50:408–13.
[3] Borne K. Top 10 big data challenges – a serious look at 10 big data V’s. MAPR, 2014:NO4, 80.
[4] Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, et al. Predictive big data analytics: a study of Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One 2016;11:e0157077.
[5] Wu PY, Cheng CW, Kaddi CD, Venugopalan J, Hoffman R, Wang MD. –Omic and Electronic Health Record Big Data Analytics for Precision Medicine. IEEE Trans Biomed Eng2017;64:263–73.
[6] Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 2016;8:1.
[7] https://www.techopedia.com/definition/5415/android
[8] According to Canalys, In Q2 2009 Android
[9] Gligorijević V, Malod‐Dognin N, Pržulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016;16:741–58
[10]Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 2016;8:1. PubMedWeb of ScienceGoogle Scholar
[11]Gaitanou P, Garoufallou E, Balatsoukas P. The effectiveness of big data in health care: a systematic review. In: Metadata and semantics research. 2014:141–53. Google Scholar
[12]Lillo-Castellano JM, Mora-Jimenez I, Santiago-Mozos R, Chavarria-Asso F, Cano-González A, García-Alberola A, et al. Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services. IEEE J Biomed Health Inform2015;19:125363. Web of ScienceCrossrefPubMedGoogle Scholar
[13]Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J Biomed HealthInform2015;19:1193–1208. CrossrefGoogle Scholar
[14]Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia ComputSci2015;50:40813. CrossrefGoogle Scholar
[15]Borne K. Top 10 big data challenges – a serious look at 10 big data V’s. MAPR, 2014:NO4, 80. Google Scholar
[16]Hermon R, Williams PA. Big data in healthcare: what is it used for? In: Australian Ehealth Informatics and Security Conference. 2014:40–9.
[17] R. Chaiken, et. al. Scope: Easy and Efficient Parallel Processing of Massive Data Sets. In Proc. of VLDB, 2008.
[18] HadoopDB Project. Available at http://db.cs.yale.edu/hadoopdb/hadoopdb.html
[19] MicroStrategy. Available at http://www.microstrategy.com
[20] Mysql list partitioning at http://dev.mysql.com/doc/refman/5.1/en/partitioning-list.html.
[21] White, Tom (2010). Hadoop: The Definitive Guide. O`Reilly Media. ISBN 978-1-4493-8973-4
[22] "Health care system". Liverpool-ha.org.uk. Retrieved 6 August 2011.
[23]New Yorker magazine article: "Getting there from here." 26 January 2009
[24]White F (2015). "Primary health care and public health: foundations of universal health systems". Med PrincPract. 24: 103–116. doi:10.1159/000370197
[25]International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639)
[26] International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256)

Neha Maurya, Anirudh Tripathi, Pankaj Pratap Singh, Amit Kishor, "Big Data Analysis for Predictive Healthcare Information System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.47-51, 2019.

Expert System and Knowledge Based System is very important field of an Artificial Intelligence. Expert System is an application of an Artificial Intelligence. Expert System is also known as Knowledge Based System. Expert Systems are used to solve very critical problems in a particular domain. It is using the extra human intelligence concept and expertise things. A knowledge based expert system used to human knowledge to solve any problems according to require human intelligence. Expert system and knowledge based system is communicate with each other. This paper explains the how to development an expert system, how to use expert system with knowledge based system.

Expert System, Knowledge Based Expert System, Knowledge Based System, Rule Engine, Shell

[1]. https://www.researchgate.net/publication/266013987_A_Review_on_Knowledge-based_Expert_System_Concept_and_Architecture
[2]. https://pdfs.semanticscholar.org/19f8/9f1b19b558389e9c70b75403063376cb8e3d.pdf
[3]. T.-C. Chang, Expert Process Planning for Manufacturing, Addison-Wesley, 1990
[4]. Introduction to Artificial Intelligence and Expert Systems. Author: - Dan W. Patterson (Page No.:- 331-333)

Rajesh Kumar Khator, "Expert System using Knowledge Based System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.52-55, 2019.

Multiplier in Digital Signal Processing (DSP) and Elliptic Curve Cryptography (ECC) are crucial. Thus modern DSP and ECC systems require to develop low power multiplier circuits to reduce the power dissipation and at the same time to increase the speed. One of the efficient ways to reduce power dissipation is by the use of Modified Gate Diffusion Input (MGDI) which at the same time reduces the circuit parameters like transistor count, implementation cost, space required and propagation delay. This paper proposes a new design technique for two-bit binary multiplier and hence multi-bit binary multiplier using the proposed two-bit multiplier circuit. This paper also implements the proposed two-bit multiplier using DSCH 3.5. The proposed technique claims lower power consumption, lower cost, lower space required and also lesser number of transistor than other conventional techniques like CMOS, PTL, CPL etc. A comparative study of the proposed technique has been dealt here clearly which shows the novelty of the proposed technique.

CPL, DSCH 3.5, Karnaugh’ map, Multiplier, PTL, Shannon’s Expansion Theorem

[1] C.S. Wallace, “Asuggestion for a fast multiplier,” IEEE Trans. Elec. Comput, vol. EC – 13, no. 1, pp. 14 – 17, Feb. 1964.
[2] Jagadguru Swami Sri Bharath, Krsna Tirathji, “Vedic Mathematics or Sixteen Simple Sutras From The Vedas,” Motilal Banarsidas, Varanasi (India), pp(s). 23 – 33, 1986.
[3] C. Liu, J. Han, and F. Lombardi, “A Low – power, high – performance approximate multiplier with configurable partial error recovery,” in Proc. Int. Conf. Design Automation, Test in Europe (DATE), Mar. 2014, Art. No. 95.
[4] P. Kulkarni, P. Gupta, and M. D. Ercegovac, ``Trading accuracy for power in a multiplier architecture,`` J. Low Power Electron., vol. 7, no. 4, pp. 490-501, Dec. 2011.
[5] S. Balamurugan and P. S. Mallick, ``Fixed-width multiplier circuits using column bypassing and decompositon logic techniques,`` Int. J. Elect. Eng. Inform., vol. 7, no. 4, pp. 655 – 664, Dec. 2015.
[6] R. Marimuthu, Y. Elsie Rezinold and P.S. Mallick, “Design and Analysis of Multiplier using Approximate 15-4 Compressor”, IEEE Access, vol. 5, pp(s). 1027 – 1036, 2017.
[7] K. Y. Kyaw,W. L. Goh, and K. S. Yeo, ``Low-power high-speed multiplier for error-tolerant application,`` in Proc. IEEE Int. Conf. Electron Devices Solid-State Circuits (EDSSC), Dec. 2010, pp. 1-4.
[8] G. Ganesh Kumar and V. Charishma, “Design of High Speed Vedic Multiplier using Vedic Mathematics Techniques”, International Journal of Scientific and Research Publications, Volume 2, Issue 3, pp(s). 1-5, March 2012.
[9] H. Thapliyal and M.B. Srinivas, “High Speed Efficient N x N Bit Parallel Hierarchical Overlay Multiplier Architecture Based on Ancient Indian Vedic Mathematics”, Transactions on Engineering, Computing and Technology, vol. 2, pp(s). 225 – 228,2004.
[10] Neha Goyal, Khushboo Gupta and Renu Singla, “Study of Combinational and Booth Multiplier”, International Journal of Scientific and Research Publications, Volume 4, Issue 5, pp(s). 1-4, May 2014.
[11] B.S. Premananda, Samarth S. Pai, B. Shashank and Shashank S. Bhat, “Design and Implementation of 8-bit Vedic Multiplier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol 2, issue 12, pp(s). 5877 – 5882, December, 2013.
[12] S. Maitra, “Design and Simulation of 4-bit Parallel Adder using Minimum Number of Transistor”, International Journal of Modern Communication Engineering, vol. no. – 7, issue no. – 3, pp(s). 13 – 18, May, 2019.
[13] R. Uma and P. Dhavachelvam, “Modified Gate Diffusion Input Technique: A New Technique for Enhancing Performance in Full Adder Circuits,” Proceedings of 2nd International Conference on Communication, Computing and Security (ICCS2012), pp(s). 74 – 81.

Subhashis Maitra, "Design and Simulation of Two – bit Multiplier Circuit using MGDI Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.56-61, 2019.

Medical image retrieval plays an more important role in the medical research environment which needs to done fastly and accurately for improved performance. In our previous research method it is done by introducing coiflets wavelet based feature extraction and SVM based classification. However this research method cannot perform well with the presence of increased noise level and the minuter feature information. This is resolved in this research method by introducing method namely Gray Level Co occurrence Matrix Feature Extraction and Fuzzy Based Discriminative Binary Descriptor (GLCMFE-FBDBD). It contains five major steps such as deblurring, preprocessing, feature extraction, detection of most discriminative bin and subspace clustering. In this research method, the image deblurring is accomplished by utilizing Artificial Bee Colony (ABC) algorithm. Preprocessing is done by using min-max normalization; feature extraction is done by using gray level concurrence matrix Then FSK Function is used to discover the most discriminative bin selection. SC is presented for quick image retrieval. The MRI brain tumor images are used for evaluation. Finally, the results show that the proposed work gives greater performance compared to the previous work.

Image retrieval, Edge Scale-Invariant Feature Transform (ESIFT), Image deblurring, Artificial Bee Colony (ABC), Subspace Clustering (SC) algorithm, Fuzzy Sigmoid Kernel (FSK)

[1] Rothganger F, Lazebnik S, Schmid, C, and Ponce, J. “3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints”, International Journal of Computer Vision, vol. 3, Issue :66, pp: 231-259,2006.
[2] Trzcinski T, Christoudias M, Fua P, and Lepetit V, “Boosting binary keypoint descriptors”, In Proceedings of the IEEE conference on computer vision and pattern recognition. pp.2874-2881,2013.
[3] Zhou W, Yang M, Li H, Wang X, Lin Y, and Tian Q, “Towards Codebook-Free: Scalable Cascaded Hashing for Mobile Image Search”, IEEE Trans. Multimedia, vol:16, issue:3, pp. 601-611,2014.
[4] Fang B, Leung CH, Tang YY, Kwok PC and Wong YK, “Off-line signature verification by the tracking of feature and stroke positions”, Pattern recognition, vol.36, issue:1, pp: 91-101,2003.
[5] Yang Y, and Newsam S, “Bag-of-visual-words and spatial extensions for land-use classification”, In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp. 270-279, 2010.
[6] Bhowmik N, González, VR, Gouet-Brunet V, Pedrini H, and Bloch G, “Efficient fusion of multidimensional descriptors for image retrieval”, In Proceedings of the IEEE International Conference on Image Processing, pp. 5766-5770,2014.
[7] Zhang S, Tian Q, Lu K, Huang Q, and Gao W, “Edge-SIFT: Discriminative binary descriptor for scalable partial-duplicate mobile search”, IEEE Trans. on Image Processing, vol.22, issue.7,pp. 2889-2902, 2010.
[8] Zhou W, Lu Y, Li H, and Tian Q, “Scalar quantization for large scale image search”, In Proceedings of the 20th ACM international conference on Multimedia, pp. 169-178, 2012.
[9] Liu Z, and Stasko J., “Mental models, visual reasoning and interaction in information visualization: A top-down perspective”, IEEE Trans. on Visualization & Computer Graphics, vol.6, pp. 999-1008, 2010.
[10] Grauman K and Darrell T, “The pyramid match kernel: Discriminative classification with sets of image features”, In Proceedings of the Tenth IEEE International Conference on Computer Vision,vol.2, pp.1458-1465,2005.
[11] Jegou H, Harzallah, H, and Schmid, “A contextual dissimilarity measure for accurate and efficient image search”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8,2007.
[12] Hong R Li L, Cai J, Tao D, Wang M, and Tian Q, “Coherent semantic-visual indexing for large-scale image retrieval in the cloud”, IEEE Trans. on Image Processing, vol.26,issue.9,pp: 4128-4138,2017
[13] Xu X Liu Z Wang Z Sheng Q Z Yu J and Wang X, "S-ABC: a paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition”, Future Generation Computer Systems, vol. 68, pp. 304-319,2017.
[14] Rosten E Porter R and Drummond T, “Faster and better: A machine learning approach to corner detection”, IEEE Trans. on pattern analysis and machine intelligence, vol.32,issue.1,pp.105-119, 2012.
[15] Shrestha S and Kazama, F, “Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan”, Environmental Modeling & Software, vol.22, issue.4, pp: 464-475,2007.

N.T. Renukadevi, S. Karunakaran, K. Saraswathi, "Gray Level Cooccurrence Matrix Feature Extraction and Fuzzy Based Discriminative Binary Descriptor for Medical Image Retrieval," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.62-70, 2019.