Journal Vol – 15 No -6, June 2020

Abstract:

In the current article, we would give some results related to concave function and introduce the definition of concaviable function and new notion of concaviable functions and obtain the new results in which involved concaviable function and we would obtain new major ization type results for weighted concaviable function. This article also recaptures the similar results for concave function as well as for convex function.

Keywords:

Concave Function,Convex Function,Concavifiable Function,Majorization,

Refference:

Adil Khan, Majorization theorems for convexifiable functions, Math. Commun.,18 (2013), 61–65.

Asif R. Khan and FarazMehmood, Some Remarks on Functions with Non-decreasing Increments, Journal of Mathematical Analysis, 11 (1) (2020), 1–16.

Asif R. Khan, FarazMehmood, Faisal Nawaz and AamnaNazir, Some Remarks on Results Related to ∇−Convex Function, Submitted.

Asif R. Khan, FarazMehmood and M. AzeemUllahSiddique, Some Results Related to Convexifiable Functions, to appear.

EhtishamKarim, Asif R. Khan and SyedaSadia Zia, On Majorization Type Results, Commun. Optim.Theory, 2015, 2015:5, 1–17.

FarazMehmood, On Function with Nondecreasing Increments, (Unpublished doctoral dissertation), Department of Mathematics, University of Karachi, Karachi, Pakistan, 2019.

G. H. Hardy, J. E. Littlewood, G. Po ́lya, Inequalities, 2nd Ed. Cambbridge University Press, England, (1952).

I. C. P. Niculescu and L. E. Persson, Convex functions and their applications,A contemporary approach, Springer, New York, (2006).

J. E. Pec ̌aric ́, F. Proschan and Y. L. Tong, Convex functions, partial orderings and statistical applications, Academic Press, New York, 187(1992).

J. Karamata, Sur une ine ́galite ́ realitive aux fonctions convexes, Publ. Math. Univ. Belgrade, 1 (1932), 145–148.

Jr. W. A. Thompson and Darrel W. Parke, Some Properties of Generalized Concave Functions, Operations Research 21 1 (Jan. – Feb., 1973), 305–313.

L. Maligranda, J. E. Pecaric and L. E. Persson, Weighted favard and berwald inequalities, J. Math. Anal. Appl, (1995), pp.248–262.

Malcolm Pemberton and Nicholas Rau, Mathematics for Economists: An Introductory Textbook, Oxford University Press (2015) 363–364. ISBN 978-1-78499-148-7.

Marshall, A. W., and Olkin, I., Inequalities: Theory ofMajorization and Its Applications, Academic Press, New York (1979).

Martin J. Osborne, Mathematical methods for economic theory, version 2019-09-05, site built on the core of the OJS system.

Muhammad Adnan, A. R. Khan and FarazMehmood, Positivity of sums and integrals for higher order ∇−convex and completely monotonic functions, arXiv:1710.07182v1, [math.CA], 13 Oct 2017.

S. Zlobec, Characterization of convexiable function, Optimization 55(2006), 251–261.
Simon, Convex and concave function, Chapter 21, p. 505–522.

Z. Kadelburg, D. Dukic ́, M. Lukic ́, I. Matic ́, Inequalities of Karamata, Schur and Muirhead,and some application, The Teaching of Mathematics VIII (2005), 31–45.

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Abstract:

Aimed at the problems of Intensity, Contour and orientation information, a Human Action Recognition (HAR) method based on Fused Feature Vector (FFV) is proposed in this paper. The FFV is constructed based on three different features such as Intensity features, Gradient features, and Orientation features. These three set of features are obtained through three different feature extraction methods based on Gaussian Filter, Gradient Filter and Gabor filter. Further to ensure optimal discriminant subspace, Kernel Discriminant Analysis is employed as a dimensionality reduction technique. Given the FFV of each action image, Support Vector Machine (SVM) is employed for classification. The proposed recognition model is evaluated systematically on the three public datasets such as KTH dataset, Weizmann dataset and the challenging UCF YouTube action dataset. Experimental results prove that our method outperforms the conventional approaches in terms of recognition accuracy.

Keywords:

Human action recognition,Gaussian,Gradient,Gabor,Kernel ,Discriminant Analysis,Support vector Machine,Recognition Accuracy,

Refference:

I. A. Bobick and J. Davis, “The recognition of human movement using temporal templates,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 3, pp. 257–267, 2001.

II. A. Klaser and M. Marszalek, “A Spatio-temporal descriptor based on 3dgradients,” in Proc. Eur. Conf. Comput. Vision, 2008.

III. B. Mandal, How-Lung Eng, “Regularized Discriminant Analysis for Holistic Human Activity Recognition”, IEEE intelligent systems, 2012

IV. Bo Lin and Bin Fang, “A new spatial-temporal histograms of gradients descriptor and HOD-VLAD encoding for human action recognition”, International Journal of Wavelets, Multi-resolution and Information Processing, Vol. 17, No. 02, 2019.

V. B. Sch¨olkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT press, 2002.

VI. Chakraborty, B.; Holte, M.B.; Moeslund, T.B.; Gonzalez, J. Selective Spatio-temporal interest points. Comput. Vis. Image Underst. 2012, 116, 396–4100

VII. C Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local SVM approach,” in Proc. Int. Conf. Pattern Recognit., vol. 3, 2004, pp. 32–36.

VIII. C.Yang, M. Schmalz, W. Hu, and G. Ritter, “Center-surround filters for the detection of small targets in cluttered multispectral imagery: Background and filter design,” in Proc. SPIE, 1995, vol. 2496, pp. 637–648

IX. D.K. Vishwakarma and C. Dhiman, “A unified model for human activity recognition using spatial distribution of gradient and difference of Gaussian kernel”, Vis Comput. 35, 1595-1613, 2019.

X. D.K. Vishwakarma, PrachiRawat, and RajivKapoor, “Human Activity Recognition Using Gabor Wavelet Transform and Ridgelet Transform”, Procedia Computer Science,Volume 57, 2015, Pages 630-636.

XI. D. Song and D. Tao, “Biologically inspired feature manifold for scene classification,” IEEE Trans. Image Process., vol. 19, no. 1, pp. 174–184, Jan. 2010.

XII. Duta. I. C, Uijlings, J. R, Ionescu B, et al. Efficient Human Action recognition using Histograms of motion gradients and VLAD with descriptor shape information. Multimed Tools Appl. 76, 22445-22475, 2017.

XIII. D. Weinland and E. Boyer, “Action recognition using exemplar-based embedding,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2008, pp. 1–7.

XIV. G.Cheng, Y. Wan, A. N. Saudagar, K. Namuduri, and B. P. Buckles, “Advances in human action recognition: A survey,” New J. Phys., vol. 17, no. 8, pp. 1_30, 2015.

XV. G.Willems, T. Tuytelaars, and L. Van Gool, “An efficient dense and scale-invariant Spatio-temporal interest point detector,” in Proc. Eur. Conf. Comput. Vision, 2008, pp. 650–663.

XVI. I Laptev and T. Lindeberg, “Space-time interest points,” in Proc. IEEE Int. Conf. Comput. Vision, 2003, pp. 432–439.

XVII. J. Arunnehru1 and M. KalaiselviGeetha, “Motion Intensity Code for Action Recognition in Video Using PCA and SVM”, In: prasath R., Kathirvalavakumar T. (eds) Mining Intelligence and knowledge Exploration Lecture notes in computer science, Vol.8284, Spriger, Cham.

XVIII. Jin Wang et al. “Human action recognition based on Pyramid Histogram of Oriented Gradients”, IEEE International Conference on Systems, Man, and Cybernetics, AK, USA, 2011.

XIX. J. Liu, J. Luo and M. Shah, Recognizing realistic actions from videos “in the wild”, CVPR 2009, Miami, FL.

XX. Kishore K. Reddy, NareshCuntoor, AmithaPerera, Anthony Hoogs, “Human Action Recognition in Large-Scale Datasets Using Histogram of Spatiotemporal Gradients”, IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, Bejing China, 2012.

XXI. K. Ruben Raju, Yogesh Kumar Sharma, BirruDevender, “Composite Feature Vector Assisted Human Action Recognition through Supervised Learning”, International Journal of Recent Technology and Engineering (IJRTE), Volume-8 Issue-6, March 2020.

XXII. K. Soomro and A. R. Zamir, “Action recognition in realistic sports videos,” in Advances in Computer Vision and Pattern Recognition, vol. 71. Cham, Switzerland: Springer, 2014, pp. 181_208.

XXIII. M. B. Abidine, B. Fergani, “Evaluating a new classification method using PCA to human activity recognition”, ICCMA 2013: IEEEInternational Conference on Computer Medical Applications, At Sousse, Tunisia.

XXIV. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” in Proc. IEEE Int. Conf. Comput. Vision, vol. 2, 2005, pp. 1395–1402.

XXV. M. Bregonzio, S. Gong, and T. Xiang, Recognizing action as clouds of space-time interest points, in CVPR, 2009.

XXVI. Md. Zia Uddin, J.J. Lee, and T.-S. Kim, “Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model”, In: Nguyen N. T., Borzemski L., Grzech A., Ali M. (eds) New frontiers in applied artificial intelligence. IEA/AIE 2008. Lecture notes I computer science, vol. 5027, Springer, Berlin, Heidelberg.

XXVII. M. Guo and Z. Wang, “A feature extraction method for human action recognition using body-worn inertial sensors”, IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD), itlay, 2015.

XXVIII. Michael W. Davidson, Mortimer Abramowitz, “Molecular Expressions Microscopy Primer: Digital Image Processing – Difference of Gaussians Edge Enhancement Algorithm”, Olympus America Inc., and Florida State University.

XXIX. Ning Ii, De Xu, “2D Log-Gabor wavelet based action recognition”, IEICE Trans.Inf& Sys. Vol.E92-D, No.11, 2009.

XXX. P A. Dhulekar, S. T. Gandhe, “Action recognition based on Histogram of Oriented gradients and Spatio-temporal interest points, IJET, 7(4), 2018, 2153-2160.

XXXI. ParulArora, SmritiSrivastava, KunalArora, ShreyaBareja, “Improved Gait Recognition using Gradient Histogram Gaussian Image”, Procedia Computer Science 58 ( 2015 ) 408 – 413.

XXXII. P. Doll´ar, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” in Proc. Joint IEEE Int. Workshop Visual Surveillance Perform. Eval. Tracking Surveillance, 2005, pp. 65–72.

XXXIII. R. Gonzalez and R. Woods, Digital image processing. Pearson/Prentice Hall, 2008.

XXXIV. R. Poppe, “A survey on vision-based human action recognition”, Image and Vision Computing 28 (2010) 976–990

XXXV. S Kanagamalliga, and S. Vasuki “Contour-based object tracking in video scenes through optical flow and Gabor features”, Optik, Volume 157, March 2018, Pages 787-797.

XXXVI. S. Maheswari and P. ArockiaJansi Rani, “RVM-based human action classification through Gabor and Haar feature extraction”, Int. J. Computational Vision and Robotics, Vol. 6, Nos. 1/2, 2016.

XXXVII. Su, Y., Li, Y. & Liu, A., “open-view human action recognition based on Linear Discriminant Analysis”, Multimedia tools Appl, 78, 767-782, 2019

XXXVIII. T. B. Moeslund, A. Hilton, and V. Kr¨uger, “A survey of advances in vision-based human motion capture and analysis,” Computer Vision and Image Understanding, vol. 104, no.2-3, pp. 90–126, 2006.

XXXIX. T. Ko, “A survey on behavior analysis in video surveillance for homeland security applications,” in Proc. 37th IEEE Appl. Imagery Pattern Recog. Workshop, Washington, DC, 2008, pp. 1–8.

XL. Uddin M, Lee JJ, Kim T.S., “Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model. ”, In: ConfProc of IEEE Eng Med Biol Soc. 2008;2008:5168-71.

XLI. VikasTripathi, DurgaprasadGangodkar, AnkushMittal, VishnuKanth, “Robust Action Recognition framework using Segmented Block and Distance Mean Histogram of Gradients Approach”, Procedia Computer Science, Volume 115, 2017, Pages 493-500

XLII. V. Thanikachalam and K.K. Thyagarajan, “Human Action Recognition using Accumulated motion and gradient of motion from video”, ICCCNT 2012.

XLIII. Y. Ke, R. Sukthankar, and M. Hebert, “Event detection in crowded videos,” in Proc. IEEE Int. Conf. Comput. Vision, 2007, pp. 1–8.

XLIV. Yuan Shen, Zhenjiang Miao, “Oriented Gradients for Human Action Recognition”, ICIMCS’10, December 30–31, 2010, Harbin, China.

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Abstract:

The objective of this paper is to monitor and control the environmental conditions for proper food grain repository. We have implemented a monitoring and controlling system that monitors and controls the weather parameters like Temperature, Humidity, Gas and Light intensity. The users can control and monitor the above said parameters of the repository using IOT. These sensor values are sent to the cloud. When these values get exceeded by the threshold values then the user can take an action against the conditions. By using of Thingspeak to retrieve the cloud sensor data is monitored and controlled.

Keywords:

ESP32,Temperature and Humidity Sensor (DHT11),Gas Sensor,Buzzer,Light Dependent Resistor (LDR),Thing Speak,

Refference:

I. Deepak, N., Rajendra Prasad, C., & Sanjay Kumar, S. (2018). “Patient health monitoring using IOT”, International Journal of Innovative Technology and Exploring Engineering, 8(2), 454–457. https://doi.org/10.4018/978-1-5225-8021-8.ch002.
II. Kannamma, M. &Baskar, Chanthini & Manivannan, D.. (2013). Controlling and monitoring process in industrial automation using Zigbee, pp:806-810. 10.1109/ICACCI.2013.6637279.
III. Mukesh. K and Ch. Rajendra Prasad, “Web Based Monitoring System for Nuclear Power Plant” International Journal of research and Applications July –September 2015 Transactions 2(7): 346-350(ISSN: 2394-4544), Volume 2 Issue 7. DOI: 10.17812/IJRA.2.7(59)2015.
IV. Prasad, C. R., & Bojja, P. (2020), “The energy-aware hybrid routing protocol in WBBSNs for IoT framework”, International Journal of Advanced Science and Technology, Volume-29, Issue-4, pp:1020–1028.
V. Pravalika, V., & Rajendra Prasad, C. (2019), “Internet of things based home monitoring and device control using Esp32”. International Journal of Recent Technology and Engineering, 8 (1 Special Issue 4), 58–62.
VI. Priyanka D., et.al, “Smart Food Quality Testing and Ordering System Using at Mega328 in Restaurants”, International Journal of Scientific Research and Engineering Development,Volume-3, Issues-1, Jan- Feb, 2020, pp: 645-650.
VII. Ramchandar Rao, P., Srinivas, S., & Ramesh, E. (2019). A report on designing of wireless sensor networks for IoT applications”, International Journal of Engineering and Advanced Technology, Volume-8, Issue-3, 2005-09, https://doi.org/10.35940/ ijeat.F1236.0986S319.
VIII. Sanjay Kumar, S., Ramchandar Rao, P., &Rajendra Prasad, C. (2019), “Internet of things based pollution tracking and alerting system”, International Journal of Innovative Technology and Exploring Engineering, volume-8, issue-8, pp: 2242–2245.
IX. Shreyas S K, Shridhar Katgar, Manjunath Ramaji, Yallaling Goudar, Ramya Srikanteswara, “Efficient Food Storage Using Sensors, Android and IoT”, International Journal Of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), Volume-3,Special Issue-23, April 2017, pp.8-12.
X. Suryawanshi VS &Kumbhar MS, “Real Time Monitoring & Controlling System for Food Grain Storage”, International Journal of Innovative Research in Science, Engineering and Technology, Volume-3, 2014, pp:734-738.
XI. TSGC, Tri-States Grain Conditioning, Inc., “Grain Temperature Monitoring Systems” www.tsgcinc.com
XII. Verdouw, Cor & Wolfert, Sjaak & Beulens, Adrie & Rialland, Agathe. (2015). Virtualization of food supply chains with the internet of things. Journal of Food Engineering. 176. 10.1016/j.jfoodeng.2015.11.009.
XIII. Vinayaka H, Roopa J “Intelligent System for Monitoring and Controlling Grain Condition Based on ARM 7 Processor”, India International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS),Volume-5, Issue-7, July 2016, pp:6-10, ISSN 2278-2540

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Abstract:

Recommender systems are implemented as information filtering agents. In most of the conventional recommender systems, the data about domain is available in limited volumes and suggestions are made to users based on their profile information. This lead to two major problems, insufficient representation of domain knowledge, called 'data sparsity' and lack of user-item interaction, called cold start. These two issues can be addressed with ontology based recommender systems, as they cam map domain information with user preferences without losing the semantic richness of the content. This work uses knowledge based method in knowledge aware recommendations to recommend most relevant research papers in digital literature collections. It uses simple methods to construct ontology knowledge graph and uses it for training incremental k-means clustering model. Finally, learning to rank, Adarank algorithm is used to list the top most recommendations for the given user query. The experiments were conducted based on real world unstructured datasets, and results have shown that the proposed model performs well over some of the state-of-the-art baselines.

Keywords:

Ontology,NLP,Recommender System,Knowledge Graph,Incremental Learning,Hybrid model,Semantic data model,

Refference:

I Agarwal, N., Haque, E., Liu, H., & Parsons, L. (2005, October). Research paper recommender systems: A subspace clustering approach. In International Conference on Web-Age Information Management. Springer, Berlin, Heidelberg. (pp. 475-491)
II Beel, J., Gipp, B., Langer, S., &Breitinger, C. (2016). paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4), 305-338.
III BessSchrader(2020). What’s the Difference Between an Ontology and a Knowledge Graph? Global Knowledge & Information Management Services. https://enterprise-knowledge.com/cms/assets/uploads/2020/01/Ontologies-vs.-Knowledge-Graphs.pdf.

IV Cao, Y., Wang, X., He, X., Hu, Z., & Chua, T. S. (2019, May). Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference. (pp. 151-161).
V Cheyer, A. (2018). U.S. Patent No. 10,002,189. Washington, DC: U.S. Patent and Trademark Office.
VI Garanina, N., Sidorova, E., Kononenko, I., &Gorlatch, S. (2017). Using multiple semantic measures for coreference resolution in ontology population. International Journal of Computing, 16(3), 166-176.

VII Ge, J., Chen, Z., Peng, J., & Li, T. (2012, August). An ontology-based method for personalized recommendation. In 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing (pp. 522-526). IEEE.
VIII George, G., &Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
IX GiorgosPapachristoudis.(2019). Popular evaluation metrics in recommender systems explained. https:// medium.com/qloo/popular-evaluation-metrics-in-recommender-systems-explained-324ff2fb427d.
X Grossmann, R. (2019). The existence of the world: an introduction to ontology. Routledge. 06-Mar-Philosophy – 146 pages.
XI Iannacone, M., Bohn, S., Nakamura, G., Gerth, J., Huffer, K., Bridges, R., …&Goodall, J. (2015, April). Developing an ontology for cyber security knowledge graphs. In Proceedings of the 10th Annual Cyber and Information Security Research Conference (pp. 1-4).
XII Isinkaye, F. O., Folajimi, Y. O., &Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
XIII James Mishra. (2017).Mean Reciprocal Rank (MRR). https://machinelearning.wtf/terms/mean-reciprocal-rank-mrr/.
XIV Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2017, August). Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum (Vol. 51, No. 1, pp. 4-11). New York, NY, USA: Acm.
XV Kawamura, T., Sekine, M., & Matsumura, K. (2017). Detecting Hypernym/Hyponym in Science and Technology Thesaurus Using Entropy-Based Clustering of Word Vectors. International Journal of Semantic Computing, 11(04), 433-449.
XVI Konys, A. (2018). Knowledge systematization for ontology learning methods. Procedia computer science, 126, 2194-2207.
XVII Li, G., & Chen, Q. (2016). Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering.

XVIII Li, H. (2011). A short introduction to learning to rank. IEICE TRANSACTIONS on Information and Systems, 94(10), 1854-1862.
XIX Lv, F., Jin, T., Yu, C., Sun, F., Lin, Q., Yang, K., & Ng, W. (2019, November). SDM: Sequential deep matching model for online large-scale recommender system. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2635-2643).
XX Munir, K., &Anjum, M. S. (2018). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116-126.
XXI Neethukrishnan, K. V., &Swaraj, K. P. (2017, February). Ontology based research paper recommendation using personal ontology similarity method. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-4). IEEE.
XXII Obeid, C., Lahoud, I., El Khoury, H., &Champin, P. A. (2018, April). Ontology-based recommender system in higher education. In Companion Proceedings of the The Web Conference 2018 (pp. 1031-1034).
XXIII Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert systems with applications, 39(11), 10059-10072.
XXIV Parlak, B., &Uysal, A. K. (2019). On classification of abstracts obtained from medical journals. Journal of Information Science, 0165551519860982
XXV Pedregosa et al., (2011).Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830.

XXVI Prokofyev, R., Tonon, A., Luggen, M., Vouilloz, L., Difallah, D. E., &Cudré-Mauroux, P. (2015, October). SANAPHOR: Ontology-based coreference resolution. In International Semantic Web Conference (pp. 458-473). Springer, Cham.
XXVII Raj.(2019). Creating Smart — Knowledge Base Systems (KBS) using advanced NLP library.Towards Data Science.May 4. https://towardsdatascience.com/creating-smart-knowledge-base-systems-kbs-using-advanced-nlp-library-b5c21dfafcd1.

XXVIII Sheridan, P., Onsjö, M., Becerra, C., Jimenez, S., &Dueñas, G. (2019). An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet, 11(9), 182.
XXIX Sure, Y., Tempich, C., &Vrandecic, D. (2006). Ontology engineering methodologies. Semantic Web Technologies: Trends and Research in Ontology‐based Systems, 171-190.
XXX Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., &Guo, M. (2019). Exploring high-order user preference on the knowledge graph for recommender systems. ACM Transactions on Information Systems (TOIS), 37(3), 1-26.
XXXI Weng, S. S., & Chang, H. L. (2008). Using ontology network analysis for research document recommendation. Expert Systems with Applications, 34(3), 1857-1869.
XXXII Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligence magazine, 13(3), 55-75.
XXXIII Zhang, F., Yuan, N. J., Lian, D., Xie, X., & Ma, W. Y. (2016, August). Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 353-362).
XXXIV Thangaraj .M &ArunaSaraswathy.P. (2019, October). Ontology Based Recommender System using Fuzzy Clustering Technique. International journal of Engineering and Advanced Technology (IJEAT). 9(1). 6412-6418.

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Abstract:

The rapid development in the technology of information and the necessity of transferring the information lead to the importance of the valuable and sensitive information protection is the major demand of users.‏ Current research papers presenting a method for protection of a secret gray scale image and it is composed of four phases. First phase calculates the hash value using the SHA-256 type of hash function to make sure that there is no manipulating, altering or changing on the content of the secret image. The second phase is the encryption process for the secret image using the AES encryption algorithm. Third phase applied Shamir secret sharing scheme by splitting the encryption key of the encryption algorithm used in the previous phase into a number of shares. The final phase is for embedding secret image into an appropriate cover image using Discrete Wavelet Haar (DWH), the cover image is divided into four or more parts according to the iteration numbers that chooses manually. The Least Significant Bit (LSB) technique used for hiding the secret image in a cover image. The results obtained from the proposed method approved that the secret image completely restored without any change, moreover the correlation coefficient between the secret and the retrieved image is high. After the process of reconstruction of the stego image by the proposed method, the test results of quality of image were good with MSE 1.63 and PSNR 46.008 in Lena image.

Keywords:

DWH,AES,LSB,MES,PSNR,

Refference:

I. Ashutosh Gupta, and Sheetal Kaushik. “A Review: RSA and AES Algorithm.” IITM Journal of Management and IT, Vol.8, Issue 1, pp: 82-85, 2017.‏
II. Dahat, V. Ankush, and V. ChavanPallavi. “Secret sharing based visual cryptography scheme using CMY color space.” Procedia Computer Science, Vol 78, Issue C, pp: 563-570, 2016.‏
III. Essam H. Houssein, Mona AS Ali, and Aboul Ella Hassanien. “An image steganography algorithm using haar discrete wavelet transform with advanced encryption system.” 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp:641-644, 2016.‏
IV. Je SenTeh, Kaijun Tan, and Moatsum Alawida. “A chaos-based keyed hash function based on fixed point representation.” Cluster Computing, Vol. 22, Issue 2, pp: 649-660, 2019.‏
V. Jr. Wenceslao, V. Felicisimo “Enhancing the Performance of the Advanced Encryption Standard (AES) Algorithm Using Multiple Substitution Boxes.” International Journal of Communication Networks and Information Security, Vol. 10, issue 3, p.496, 2018.
VI. Kaiser J. Giri, Mushtaq Ahmad Peer, and P. Nagabhushan. “A robust color image watermarking scheme using discrete wavelet transformation.” IJ Image, Graphics and Signal Processing, Vol. 1, pp: 47-52, 2015.‏
VII. K. Shankar, and P. Eswaran. “Sharing a secret image with encapsulated shares in visual cryptography.” Procedia Computer Science, Vol. 70, pp: 462-468, 2015‏
VIII. K. Shankar, and P. Eswaran. “A new k out of n secret image sharing scheme in visual cryptography.” 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp:1-6, 2016.‏
IX. K. Shankar, M. Elhoseny, R. S. Kumar, S. K. Lakshmanaprabu and X. Yuan, “Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique.” Journal of Ambient Intelligence and Humanized Computing, pp: 1-13, 2018.‏
X. L. Liu, Y. Lu, X. Yan, and S. Wan, “A progressive threshold secret image sharing with meaningful shares for gray-scale image.” 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), pp:380-385, 2016.‏
XI. M. Abdullah, “Advanced encryption standard (AES) algorithm to encrypt and decrypt data”. Cryptography and Network Security, Vol. 16, 2017.
XII. M. M. Abdulwahid, O. A. S. Al-Ani, M. F. Mosleh and R. A. Abd-Alhmeed. “Optimal access point location algorithm based real measurement for indoor communication”. In Proceedings of the International Conference on Information and Communication Technology, pp: 49-55, 2019.‏
XIII. M. S. Sudha, and T. C. Thanuja. “Randomly tampered image detection and self-recovery for a text document using Shamir secret sharing.” 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, pp: 688-691, 2016.‏
XIV. Po-Cheng Wu, and Liang-Gee Chen. “An efficient architecture for two-dimensional discrete wavelet transform.” IEEE Transactions on circuits and systems for video technology, Vol. 11, Issue 4, pp: 536-545, 2001.‏

XV. R. Rahim, N. Kurniasih, F. Handayanna, L. S. Dewi, E. G. Sihombing, E. Arisawati, and I. Sulistiyowati. “Enhanced pixel value differencing with cryptography algorithm”. In MATEC Web of Conferences, Vol. 197, p. 03011. EDP Sciences‏ 2018.
XVI. Sahar A. El_Rahman, “A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information.” Computers & Electrical Engineering, Vol. 70, pp: 380-399, 2018.‏
XVII. Thakral, Shaveta, and PratimaManhas. “Image Processing by Using Different Types of Discrete Wavelet Transform.” International Conference on Advanced Informatics for Computing Research. Springer, Singapore,pp: 499-507, 2018.‏
XVIII. V. Kalist, P. Ganesan, B. S. Sathish and J. M. M. Jenitha “Possiblistic-Fuzzy C-means clustering approach for the segmentation of satellite images in HSL color space”. Procedia Computer Science, Vol. 57, pp: 49-56, 2015.

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Abstract:

Let be an additive mapping of a 2-torsion free semiprime inverse semiring in to itself, satisfying holds for all , then is a centralizer.

Keywords:

Semiprime Semiring,Inverse Semiring,Commutator,Centralizer,Left (right) Centralizer,

Refference:

I. Bandlet and Petrich, Subdirect products of rings and distributive lattices, Proceedings of the Edinburgh Mathematical Society, 25, 155-171 (1982).
II. Golan, The theory of semirings with applications in mathematics and theoretical computer science, Longman Scientific & Technical; New York: Wiley, (1992).
III. Javed, Aslam and Hussain, On Condition (A2) of Bandlet and Petrich for inverse semirings, International Mathematical Forum, Vol.7, 2903−2914 (2012).
IV. Karvellas P.H., Inversivesemirings, J. Aust. Math. Soc. 18, 277 – 288 (1974).
V. Maryam K. Rasheed, Abdulrahman. H. Majeed, Some results of (α, β) derivations on prime semirings, Iraqi Journal of Science, Vol. 60, No.5, pp: 1154-116 (2019).
VI. Sara, Aslam and Javed, Oncentralizer of semiprime inverse semiring, Discuss. Math. Gen. Algebra and Applications, 36, 71 – 84 (2016).
VII. M. K. Sen and S. K. Maity, Regular additively inverse semirings, Acta Math. Univ. Comenianae, 1, 137-146 (2006).
VIII. Vukman, An identity related to centralizers in semiprime rings, Comment. Math. Univ. Carolin. 40, 3, 447–456 (1999).

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Abstract:

The concept of intuitionistic fuzzy d-filter of d-algebra introducing in this paper and also several properties are discussing, with studding some relations on this notation with the concept of intuitionistic fuzzy d-algebra.

Keywords:

d-algebra,filter,d-filter,intuitionistic fuzzy set,fuzzy set,

Refference:

I. A. K. Hassan, “fuzzy filter spectrum of d-algebra”, M.Sc. Thesis, Faculty of Education for Girls, University of Kufa. (2014)
II. D. Coker, “An introduction to intuitionistic fuzzy topological spaces”, Fuzzy Sets and Systems 88 (1997), 81–89.
III. J. Neggers, A. Dvurecenskij and H. S. Kim, “On d-fuzzy Function in d-algebras” foundations of physics, 30(2000), No. 10, 1807-1816.
IV. J. Neggers and H. S. Kim, “on d-algebra “, Math. Slovaca. 49(1999) No.1, 19-26.
V. K. Iseki, “An algebra Relation with Propositional Calculus” Proc. Japan Acad, 42 (1966) 26-29.
VI. K. T. Atanassov, “Intuitionistic fuzzy sets” , Fuzzy sets and Systems 35 (1986), 87–96.
VII. L. A. Zadeh, “Fuzzy set”,Inform. And Control. 8(1965), 338-353.
VIII. P. A. Ejegwa, S.O. Akowe, P.M. Otene, J.M. Ikyule,”An Overview On Intuitionistic Fuzzy Sets ” International Journal of scientific & technology research , 3(2014) , 3, 2277-8616
IX. P. J. Allen, H. S. Kim, and J. Neggers, “Companion d-algebra” , Math. Slovaca 57(2007), No. 2 , 93-106
X. Y. B. Jun, H. S. Kim and D.S. Yoo, “Intuitionistic fuzzy d-algebra”, Scientiae Mathematicae Japonicae Online, e-(2006), 1289–1297.
XI. Y. Iami and K. Iseki, “On Axiom System of Propositional Calculi XIV” Proc. Japan Acad, 42 (1966) 19-20.

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Abstract:

The aim of this project is to develop an android application software package “EASY CHITS” for small scale chit organizers who could not afford chit fund software. This is an end-to-end application  which covers almost all the activities involved in managing a chit, everything in this application is systematically organized and arranged for both the chit organizers and users, unlike other applications each and every activity is arranged in three modules namely total balance, chit details, history , which makes it simple to use and navigate through the entire application for chit organizers, In addition to that all the necessary information is included for users at the user end.Chit Funds are indigenous monetary establishments in India that consolidates credit and investment funds in a solitary plan. In a chit support plot, a gathering of people meet up for a foreordained timespan and add to a typical pool at customary interims. The quantity of chit plans enlisted has been diminishing throughout the years. The chit support individuals show that as much as 72 percent of the individuals take an interest in chit assets for sparing. Moreover, 96 percent of the current and non-current chit finance individuals feel that chit reserves are sheltered. Larger part of the current and non-current chit support individuals have a place with low-salary family units. Our discoveries point to the way that however chit reserves are a significant wellspring of money for independent companies and low-pay family units in India; there has been a general mass migration of low worth chit plans from the enrolled chit support showcase. This is for the most part in light of the fact that enlisted chit subsidizes think that its less worthwhile to serve the poor because of the expanded expense of working such plans forced by the controllers. We find that the chit finance industry tends to the reserve funds needs of individuals, is viewed as sheltered and furthermore offers credits at lower loan costs than moneylenders.               

Keywords:

Classification,Cluster,Easy chits,Android,UPI,

Refference:

I. http://business.mapsofindia.com/investment-industry/chit-funds.html

II. https://faculty.iima.ac.in/~iffm/literacy/Chit-fund-field-survey-report.pdf

III. https://ijrar.com/upload_issue/ijrar_issue_1459.pdf

IV. https://journals.sagepub.com/doi/abs/10.1177/097492921100300305.

V. http://shreyaschits.com/faq_aboutchits.html

VI. Mohammed Ali Shaik, P. Praveen, Dr. R. Vijaya Prakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.

VII. P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm of minimum Spanning Tree,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 131-135.doi: 10.1109/AEEICB.2017.7972398

VIII. P. Praveen, B. Rama, “An Efficient Smart Search Using R Tree on Spatial Data”,Journal of Advanced Research in Dynamical and Control Systems, Issue 4,ISSN:1943-023x.

IX. Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering-BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore.

X. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027

XI. Sallauddin Mohmmad, Dr. M. Sheshikala, Shabana,” Software Defined Security (SDSec):Reliable centralized security system to decentralized applications in SDN and their challenges”, Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 10-Special Issue, 2018, pp. (147-152).

XII. M. Sheshikala, D. Rajeswara Rao and R. Vijaya Prakash, Computation Analysis for Finding Co– Location Patterns using Map–Reduce Framework, Indian Journal of Science and Technology, Vol 10(8), DOI: 10.17485/ijst/2017/v10i8/106709, February 2017.

XIII. https://www.drishtiias.com/to-the-points/paper3/chit-fund

XIV. https://www.dvara.com/wp-content/uploads/2011/03/REPORT-Chit-Funds-Innovative-Access-to-Finance.pdf

XV. http://www.mca.gov.in/Ministry/pdf/Chit_Fund_Companies_6nov2008.pdf

XVI. http://www.telegraphindia.com/1130428/jsp/7days/story_16836319.jsp#.U4ghv_mSwoo

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Abstract:

One of the significant areas of Indian Economy is Agriculture. Work to practically half of the nation’s workforce is given by Indian horticulture segment. As a part of Agriculture, Cotton plays a major role in economic resource of Telangana. Huge number of farmers grows cotton in their fields as the lands fit to that crop. Beside the advantage the major problem affecting the crop are the diseases that are unknown to the farmers at early stages and losing the entire crop when he gets aware on that.  As a solution, we can identify the disease in the early stage and rectify before it affects the entire crop. This can be done by looking into images collected from the crop and given it as a test sample to the convolution neural network, where we test the sample with the existing training data and identify the major areas that are affected with the disease.  As an improvement we can also identify the disease that is also affected and apply the required pesticides. As a result, 91% of the diseases were correctly identified.

Keywords:

Neural Networks,Layers,Filter,Pooling,Padding,softmax,

Refference:

I. Aakanksha Rastogi, Ritika Arora, Shanu Sharma, “Leaf Disease Detection and Grading using Computer Vision Technology &Fuzzy Logic,” presented at the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 2015, pp. 500–505.
II. A. Harshavardhan, S. Babu and T. Venugopal, “An Improved Brain Tumor Segmentation Method from MRI Brain Images,” 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT), Tumakuru, 2017, pp. 1-7.
III. Barbedo, J.G., 2018. Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 172, pp.84-91.
IV. Harshavardhan, A. Mohammad, M.D. S. Ramesh, D. RaviChythanya, K,” Design methods for detecting sensor node failure and node scheduling scheme for WSN”, International Journal of Engineering and Advanced Technology 9 (1) ,pp.5430, 2019
V. Khirade, Sachin D. and A. B. Patil. “Plant disease detection using image processing.” In 2015 International conference on computing communication control and automation, pp. 768-771. IEEE, 2015.
VI. Liu, Bin, Yun Zhang, DongJian He, and Yuxiang Li. “Identification of apple leaf diseases based on deep convolutional neural networks.” Symmetry 10, no. 1 (2018): 11
VII. Md. Nazrul Islam, M.A. Kashem, MahmudaAkter and Md. Jamilur Rahman, “An Approach to Evaluate Classifiers for Automatic Disease Detection and Classification of Plant Leaf,” presented at the International Conference on Electrical, Computer and Telecommunication Engineering, RUET, Rajshahi-6204, Bangladesh, 2012, pp. 626–629.
VIII. Prakash, RajanalaVijaya, and SrinathTaduri. “Safe Navigation for Elderly and Visually Impaired People Using Adhesive Tactile Walking Surface Indicators in Home Environment.” In Information and Communication Technology for Sustainable Development, pp. 771-778. Springer, Singapore, 2020.
IX. Roopa, Goje, and M. Sampath Reddy. “A study on pattern matching intrusion detection system for providing network security to improve the overall performance of security system.” Indian Journal of Public Health Research & Development 9, no. 11 (2018): 683-687.
X. Sallauddin, M. Ramesh, D. Harshavardhan, A. Pasha, S.N. Shabana, “A comprehensive study on traditional AI and ANN architecture”, International Journal of Advanced Science and Technology 28 (17) ,pp.479, 2019
XI. Shaik, Mohammed Ali, P. Praveen, and R. VijayaPrakash. “Novel Classification Scheme for Multi Agents.”, Asian Journal of Computer Science and Technology 8, no. S3 (2019): 54-58.
XII. Traore, B.B. Kamsu-Foguem, B. and Tangara, F., 2018. Deep convolution neural network for image recognition. Ecological informatics, 48, pp.257-268.
XIII. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975, https://doi.org/10.26782/jmcms.2020.01.00027

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Abstract:

The engine performance, combustion characteristics and exhaust gas emissions of a four-cylinder, four-stroke indirect injection spark ignition engine has been numerically investigated at constant engine speed and different ignition timings when using gasoline, ethanol and LPG fuels. For this purpose, a model has been suggested by using a two-zone burnt and unburnt gas for in-cylinder combustion. The experimental data related to the cylinder pressures have been carried out to validate the engine model. The optimal effective power and effective torque were shown at advanced crank angle degrees before the top dead center. It is observed that the brake specific fuel consumption decreases if the ignition timings increase. The ethanol fuel exhausted a minimum level of carbon monoxide, unburnt hydrocarbon and oxide nitrogen emissions when compared with the gasoline fuel at all operating conditions. LPG fuel produced promising good emission results than that obtains from gasoline fuel.

Keywords:

LPG and Ethanol fuels,SI engine,Engine performance,Emissions,

Refference:

I. B. Erkuş, A. Sürmen, M. İ. Karamangil, “A comparative study of carburation and injection fuel supply methods in an LPG-fuelled SI engine,” Fuel, vol. 107, pp. 511–517, May 2013.

II. C. D. Rakopoulos, C. N. Michos, E. G. Giakoumis. “Availability analysis of a syngas fueled spark ignition engine using a multi-zone combustion model,” Energy, vol. 33, no. 9, pp. 1378-1398, September 2008.

III. C. Ji, C. Liang, S. Wang, “Investigation on combustion and emissions of DME/gasoline mixtures in bja spark-ignition engine,” Fuel, vol. 90, no. 3, pp. 1133-1138, Mar. 2011.

IV. C. Park, S. Oh, T. Kim, H. Oh, C. Bae, “Combustion Characteristics of Stratified Mixture in Lean-Burn Liquefied Petroleum Gas Direct-Injection Engine with Spray-Guided Combustion System,” Journal of Engineering for Gas Turbines and Power, vol. 138, no. 7, PP. 071501, Jul 2016.

V. C. P. Cooney, J. J. Worm, J. D. Naber, “Combustion characterization in an internal combustion engine with ethanol-gasoline blended fuels varying compression ratios and ignition timing,” Energy & Fuels, vol. 23, no. 5, pp. 2319-2324, April 2009.

VI. E. Hu, Z. Huang, B. Liu, J. Zheng, X. Gu, “Experimental study on combustion characteristics of a spark-ignition engine fueled with natural gas–hydrogen blends combining with EGR,” International journal of hydrogen energy, vol. 34, no. 2, pp. 103 5-1044, January 2009.

VII. E. Singh, K. Morganti, R. Dibble, “Dual-fuel operation of gasoline and natural gas in a turbocharged engine,” Fuel, vol. 237, pp. 694-706, February 2019.

VIII. H. Bayraktar O. Durgun, “Investigating the effects of LPG on spark ignition engine combustion and performance,” Energy Conversion and Management, vol. 46, no. 14, pp. 2317-2333, August 2005.

IX. H. Bayraktar, “An experimental study on the performance parameters of an experimental CI engine fueled with diesel–methanol–dodecanol blends,” Fuel, vol. 87, no. 2, pp. 158–164, February 2008.

X. H. Hedfi, A. Jbara, H. Jedli, K. Slime, A. Stoppato, “Performance enhancement of a spark ignition engine fed by different fuel types Performance enhancement of a spark ignition engine fed by different fuel types,” Energy Conversion and Management, vol. 112, pp. 166–175, Mar. 2016.

XI. K. Dheeraj, B. Veeresh, K. Vijay, “Effects of LPG on the performance and emission characteristics of SI engine – An Overview,” IJEDR, vol. 2, no. 3, pp. 2997-3003, 2014.

XII. K. Kim, J. Kim, S. Oh, C. Kim, Y. Lee, “Lower particulate matter emissions with a stoichiometric LPG direct injection engine,” Fuel, vol. 187, no. 1, pp. 197–210, January 2017.

XIII. K. Kim, J. Kim, S. Oh, C. Kim, Y. Lee, “Evaluation of injection and ignition schemes for the ultra-lean combustion direct-injection LPG engine to control particulate emissions,” Applied Energy, vol. 194, pp. 123-135, May 2017.

XIV. L. Tunka, A. Polcar, “Effect of various ignition timings on combustion process and performance of gasoline engine,” Acta Univ. Agric. Silvic. MendelianaeBrun., vol. 65, no. 2, pp. 545–554, April 2017.

XV. M. Aldhaidhawi, M. Naji, K. A. Subhi, “Numerical study of combustion characteristic, performance and emissions of a SI engine running on gasoline, ethanol and LPG” Teat Engineering and Management, vol. 82, pp. 3559-3565, January-February 2020

XVI. M. Gumus, “Effects of volumetric efficiency on the performance and emissions characteristics of a dual fueled (gasoline and LPG) spark ignition engine,” Fuel Processing Technology, vol. 92, no. 10, 1862-1867, October 2011.

XVII. M. Najee, M. Aldhaidhawi, O. Khudhair, “Study on performance and emissions of SI engine fueled by different fuels” ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 8, pp. 1490-1494, April 2019

XVIII. M. Pecqueur, K. Ceustermans, P. Huyskens, D. Savvidis, “Emissions Generated from a Suzuki Liane Running on Unleaded Gasoline and LPG under the Same Load Conditions,” SAE Technical Paper, 2637, Oct. 2008.

XIX. O. I.Awad, R. Mamat, O. M. Ali, N. A. C. Sidik, T. Yusaf, K. Kadirgama, M. Kettner, “Alcohol and ether as alternative fuels in spark ignition engine: A review,” Renewable and Sustainable Energy Reviews, vol. 82, no. 3, PP. 2586-2605, February 2018.

XX. S. Yousufuddina, M. Masoodb, “Effect of ignition timing and compression ratio on the performance of a hydrogen–ethanol fuelled engine,” International journal of hydrogen energy,” vol. 34, no. 16, pp. 6945- 6950, August 2009.

XXI. T. Hu, Y. Wei, S. Liu, L. Zhou, “Improvement of sparkignition (SI) engine combustion and emission during cold start, fueled with methanol/gasoline blends,” Energ& Fuels, vol. 21, pp. 171-175, November 2007.

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