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Objective of proposed research is to recognize total number of fingers from hand gesture image. This kind of system is very useful for mute people who can communicate through computer with others. This research paper defines proposed methodology for recognition of total fingers from hand gesture image. Images are processed by various image processing functions in MATLAB 2018. After extraction of features, finally recognition is done through Neural Network classification algorithm. For testing, 100 different images of hand gesture have taken from different people. Average result accuracy is 92%.

Neaural Network, Matlab, Feature Extraction

[1]. Abd albary sulyman, zeyad t. Sharef, kamaran hama ali faraj , zaid ahmed aljawaryy, and fahad layth malallah, “Real-time numerical 0-5 counting based on hand-finger gestures recognition”, Journal of Theoretical and Applied Information Technology, ISSN: 1992-8645, Vol.95. No 13. , 15th July 2017.
[2]. CHETHANA N S, DIVYA PRABHA, M Z KURIAN, “STATIC HAND GESTURE RECOGNITION SYSTEM FOR DEVICE CONTROL”, International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084 Volume-3, Issue-4, April-2015.
[3]. Jean-Francois Collmeau, Elyse Nespoulous, Helene Laurent and Benoit Magnanain “Simulation interface for gesture based remote control of a surgical lighting arm” IEEE International Conference on Systems, Man, and Cybernetics 2013.
[4]. Asanterabi Malima, Erol Ozgur, and Mujdat Cetin “A Fast Algorithm for vision-based Hand Gesture Recognition for Robot Control” IEEE International conference on Computer Vision, 2006.
[5]. Yikai Fang, Kongquiao wang, Jian Cheng and Hanquing Lu “A Real Time Hand Gesture Recognition Method” ICME, IEEE 2007.
[6]. Tasnuva Ahmed “A Neural Network based Real Time Hand Gesture Recognition System” International Journal of Computer Applications (0975 – 8887) Volume 59– No.4, December 2012.
[7]. Mithun G. Jacob, Yu-Ting Li, Juan P. Wachs “Surgical Instrument Handling and Retrieval in the Operating Room with a Multimodal Robotic Assistant” IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013.
[8]. Meenakshi Panwar and Pawan Singh Mehra , ―Hand Gesture Recognition for Human Computer Interaction‖, in Proceedings of IEEE International Conference on Image Information Processing(ICIIP 2011), Waknaghat, India, November 2011.
[9]. Zhong Yang, Yi Li, Weidong Chen, Yang Zheng, ―Dynamic Hand Gesture Recognition Using Hidden Markov Models‖, in Proceedings of 7th International Conference on Computer Science & Education (ICCSE 2012) July 14-17, Melbourne, Australia,2012.
[10]. Amiraj Dhawan, Vipul Honrao, “Implementation of hand detection based techniques for human computer interaction”, in International Journal of Computer Applications (0975 – 8887).
[11]. Ankit P. Parmar, Dr. Nehal G. Chitaliya,” Gesture Recognition System for Indian Sign language on Smart Phone”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 2, February 2016.

H. Bhavsar, J. Trivedi, "Recognize Number of fingers from Single hand gesture Image using Image processing and Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.349-352, 2018.

Data mining is the process of making patterns for large data sets. There are many data mining algorithms proposed in recent years. All algorithms work on different data type so one algorithm may not be used for all applications. On the basis of requirement and compatibility of dataset, an algorithm will be applied. Apriori is a classical algorithm which is use for all dataset. But there are some shortfalls of Apriori algorithm. Our, proposed Data segregation algorithm is introduced to address the effect of these short comings of apriori. The comparison of two algorithm studied on the basis of some criteria discussed in the paper.

Comparative analysis, data segregation model, cloud, data mining, methodology, Apriori algorithm

[1] Rajleen Kaur, Amanpreet Kaur, “A Review Paper on Evolution of Cloud Computing, its Approaches and Comparison with Grid Computing”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5) , 2014.
[2] Abdulaziz Aljabre ,” Cloud Computing for Increased Business Value”, International Journal of Business and Social Science Vol. 3; January 2012.
[3] T. Dillon, C. Wu, and E. Chang, “Cloud Computing: Issues and Challenges,” 2010 24th IEEE International Conference on Advanced Information Networking and Applications(AINA), pp. 27-33, DOI= 20-23 April 2010
[4] Santosh Kumar , R. H. Goudar, “Cloud Computing – Research Issues, Challenges, Architecture, Platforms and Applications: A Survey”, International Journal of Future Computer and Communication, Vol. 1, No. 4, December 2012.
[5] Heli P. Vyas, Sanjay M. Shah,” A New Perception in Cloud Computing: Hybrid Model”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 3 Issue: 9 5702 – 5704.
[6] Ahilandeeswari.G.,Dr. R. ManickaChezian,” A comparative study of Frequent pattern mining Algorithms: Apriori and FP Growth on Apache Hadoop”, International Journal of Innovations & Advancement in Computer Science, ISSN 2347 – 8616, March 2015.
[7] Heli P. Vyas, Sanjay M. Shah,” A New Approach: Data Segregation Model”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 4 Issue: 7, p 38 – 40.
[8] Heli P. Vyas, Sanjay M. Shah,” A Comparative Analysis of Frequent Pattern Mining Algorithms”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; Volume 5 Issue XI November 2017
[9] R. V. Dharmadhikari, S. S. Turambekar , S. C. Dolli , P K Akulwar, ” Cloud Computing: Data Storage Protocols and Security Techniques”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.2, pp.113-118, April (2018), E-ISSN: 2320-7639.
[10] Rajesh Piplode , Pradeep Sharma and Umesh Kumar Singh, “Study of Threats, Risk and Challenges in Cloud Computing”, International Journal of Scientific Research in Computer Science and Engineering, Volume-1, Issue-1, Jan- Feb-2013

Heli P. Vyas, Sanjay M. Shah, "Finding Most Efficient Algorithm for Segregating and Saving Data: A Comparative Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.353-356, 2018.

The aim of this research to work on new methods of image feature extraction, image retrieval and feature vector indexing. This research work presents a new weighted histogram based statistical features, which integrate the pixel spatial distribution information into a numerical value. As a fundamental retrieval method, it can easily be integrated with multidimensional R- Tree indexing method. The experiment results show that this method is still effective when the data scale is very large, and it has superior scalability than traditional indexing methods. We analyzed the performance of the system under four classes of different database environment.

Tamura , Texture , Content based, R-Tree, Indexing

[1]. P.S. Patwal, A.K. Srivastava "A content-based indexing system for image". Indian Journal of Science and Technology. 2016; 9(29):1–7
[2]. M.M. Shah and P.S. Patwal, “Multi-Dimensional Image Indexing with R+-Tree”, IJIACS ISSN 2347 – 8616 Volume 3, Issue 1 April 2014.
[3]. M.M. Shah and P.S. Patwal “Pattern Reorganization-Review of Texture Analysis Techniques”, National Conference “Emerging Trends In Intelligente Computing and Communications, 2012” Galgotia University, Greater Noida”
[4]. M.M. Shah and P.S. Patwal “ Image Retrieval Using Wavelet Based Techniques”, International Conference on Technical Innovation through Modern Engineering Sciences-TIMES-2013.
[5]. N. Jain, M.M. Shah and P.S. Patwal “Shape Based Image Indexing and Retrieval for Diagnostic Pathology", International Conference on Technical Innovation through Modern Engineering Sciences-TIMES-2013
[6]. P.S. Patwal, A.K. Srivastava, “Image Retrieval Based on Content with Color Feature using Quadratic Distance Metric Technique”,International Conference on Technical Innovation through Modern Engineering Sciences-TIMES-2013
[7]. P.S. Patwal, A.K. Srivastava, "An Analysis of Spatial domain techniques - Neighborhoods Processing” ,
[8]. Pratap Singh Patwal, A.K. Srivastava, “An analysis of Spatial domain techniques - Point Operations”,International Journal of Graphics & Image Processing |Vol 4|issue 3|Aug. 2014
[9]. E. Bruce, Goldstein ”The Ecology of j. j. gibson`s perception”,Printed in Great BritainLeonardo, Vol. 14, No. 3, pp. 191-195, 1981.
[10]. Y.Rui and S. Thomas. “Image Retrieval: Current Techniques, Promising Directions, and Open Issues”, Journal of Visual Communication and Image Representation, PP. 39–62, 1999.
[11]. M.R. Turner, RuzenaBajcsy,“Underestimation of Visual Texture Slant by Human”,Technical Reports (CIS), Department of Computer & Information Science January 1990
[12]. Qi. Xiaojun and HongxingZheng “A new integrated method for shape based image retrieval”, Computer Science Department, Utah State University, Logan, UT, 84322-4205.
[13]. S NajimunNisha, K.A MeharBan “Performance Enhancement of an Image Retrieval by Integrating Text and Visual Features”, International Journal of Advanced Research in Computer Science & Technology (IJARCST), Vol. 2 Issue Special 1 , PP.67-71, 2014.
[14]. K V Shriram, P.L.K. Priyadarsini, “CBIR – An analysis and suggestions for improvement”, International Journal of Computer Applications (0975 – 8887) Volume 42– No.14, March 2012.

Pratap Singh Patwal, Jitendra Kumar Tyagi, Garima Yadav, "Image Indexing System Using Texture Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.357-360, 2018.

We aim to project the impact of the Gross Domestic Product of India on the overall poverty rate of the country through the trailing years using data science. The correlation between GDP and Poverty rates has been modelled for the years 1981-2015. On getting a high correlation, we have used Linear Regression in order to train a model corresponding to the World development Indicators (a world-bank dataset) and found out their individual contributions towards the GDP of the country. The results found during the research are immensely helpful to define the major contributors of the current economic conditions of India. Also, these results can be further formulated to predict the poverty rates of the country.

GDP(Gross Domestic Product), Poverty rates, Data Science, Pearson’s correlation, Linear regression

[1] Deaton, Angus. 2010. "Price Indexes, Inequality, and the Measurement of World Poverty." American Economic Review, 100 (1): 5-34.DOI: 10.1257/aer.100.1.5
[2] Sanjay G. Reddy, “Counting the poor: the truth about world poverty statistics”
[3] Human Development Reports, United Nations development program.
[4] Martin Ravallion, “Poverty Lines in Theory and Practice”, Georgetown University
[5] Dong Nguyen Noah A. Smith Carolyn P. Rose,
“Author Age Prediction from Text using Linear Regression”, Language Technologies Institute Carnegie Mellon University, Pittsburgh, PA 15213, USA
[6] Christofides, S., Regression Models Based on Log-incremental Payments, Claims Reserving Manual,
1990.2, Institute of Actuaries, London.
[7] World Development Indicators | A World Bank data published by Kaggle.
[8] GDP World Bank Data | A world bank data published by Kaggle
[9] Statista – The statistics portal for market data, market research and market studies.
[10] Ieconomics | Search and visualization of economic indicators.

Saumya Gupta, Pradeep Rai, "Mapping Correlation between GDP and Poverty rate of India using Linear Regression," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.361-365, 2018.

Intrusion detection systems monitor computer system events to discover malicious activities in the network. There are two types of intrusion detection systems, namely, signature-based and anomaly-based. Anomaly detection can be either flow-based or packet-based. In the flow-based approach, the system looks at aggregated information of related packets in the form of flow. Packet-based detection system inspects the complete packet which consists of a header as well as payload data. In this paper, a packet-based improved anomaly detection technique is proposed. In the training module, the normal profiles of the network traffic are generated by modeling the payload of the network using n-gram approach by applying length-wise clustering of packets according to payload length. Length-wise clustering is done to reduce the number of models for normal profiles. Then the mean and standard deviation is calculated which are used in detection module. In detection module, the distance between normal profiles and newly arriving data in the network is computed using cosine similarity. The standard dataset DARPA’99 and the Panjab University collected data are used for testing the proposed technique. Anomaly detection of the proposed technique is done on port numbers 21, 23 and 80 and the results are compared with the various n-gram techniques and other techniques used in literature for payload anomaly detection. It is concluded that this improved technique can reduce space and provide better results on port 21 and port 23 than on port 80.

Payload, anomaly detection, cosine similarity, n-gram, length-wise clustering

[1] N. M. Jacob, and M. Y. Wanjala, “A Review of Intrusion Detection Systems”, International Journal of Computer Science and Information Technology Research, Vol. 5, Issue 4, pp. 1-5, 2017.
[2] H. Alaidaros, M. Mahmuddin, and A. Mazari, “An Overview of Flow-based and Packet-based Intrusion Detection Performance in High Speed Networks”, Naif Arab University for Security Sciences, pp. 1–9, 2011.
[3] K. Wang, J.S. Stolfo, “Anomalous Payload-based Network Intrusion Detection”, International Workshop on Recent Advances in Intrusion Detection, Springer, Berlin, Heidelberg, Vol. 3224, pp. 203-222, 2004.
[4] S.A. Thorat, A. K. Khandelwal, B. Bruhadeshwar, and K. Kishore, “Payload Content based Network Anomaly Detection”, In the Proceedings of the 2008 International conference on the Applications of Digital Information and Web Technologies, IEEE, pp. 127-132, 2008.
[5] S. Staniford, J.A. Hoagland, J.M. McAlerney, “PracticalAutomated Detection of Stealthy Portscans”, Journal of Computer Security, Vol.10, pp. 105-136, 2002.
[6] C. Krugel, T. Toth, and E. Kirda, “Service Specific Anomaly Detection for Network Intrusion Detection”, In the Proceedings of the 2002 ACM symposium on Applied computing, pp. 201-208, 2002.
[7] L. Zhang, and G.B. White, “Anomaly Detection forApplication Level Network Attacks Using Payload Keywords”, In the Proceedings of IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA, pp.178-185, 2007.
[8] R. Perdisci, D. Ariu, P. Fogla, G. Giacinto, and W. Lee, “McPAD : A Multiple Classifier System for Accurate Payload-based Anomaly Detection,” Elsevier Science Journal of Computer. Networks, Vol. 5, Issue. 6, pp. 864–881, 2009.
[9] Z. Tan, A. Jamdagni, X. He, and P. Nanda, “Network Intrusion Detection based on LDA for Payload Feature Selection”, in Proc. of IEEE Globecom Workshops, pp. 1545–1549, 2010.
[10] M. Kakavand, N. Mustapha, A. Mustapha, and M.T.Abdulla, “Effective Dimensionality Reduction ofPayload- Based Anomaly Detection in TMAD Model for HTTP Payload”, Transactions on Internet and Information Systems, Vol. 10, Issue. 8, pp. 3884-3910,2016.
[11] G. Kim, S. Lee, and S. Kim, “A Novel Hybrid Intrusion Detection Method Integrating Anomaly Detection with Misuse Detection”, Expert Systems with Applications,Elsevier, Vol. 41, Issue 2, pp. 1690-1700, 2014.
[12] E. Eskin, “Anomaly Detection over Noisy Data UsingLearned Probability Distributions”, in Proceedings ofThe International Conference on Machine Learning, pp.255-262, Czech Republic, Aug 2000.
[13] K. Scarfone, and P. Mell, “Guide to Intrusion Detectionand Prevention Systems (IDPS)”, Technical report NISTSpecial Publication Vol. 800, Issue 94, Feb. 2007.
[14] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal Science Research in Network Security and Communication, Vol. 5, Issue 6, pp.5-8, December 2017.
[15] M. Shivakumar, R. Subalakshmi , S. Shanthakumari and S.John Joseph, “Architecture for Network-Intrusion Detection and Response in open Networks using Analyzer Mobile Agents”, International Journal Science Research in Network Security and Communication, Vol. 1, Issue 4, pp. 1-7, Oct 2013.

Kajal Rai, M. Syamala Devi and Ajay Guleria, "Packet-based Anomaly Detection using n-gram Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.366-372, 2018.

-Now a days wireless sensor network is a rapid growing area, are increasing in demand and widely deployed. Sensor communicates with other nodes in wireless manner and has ability of collecting, storing, transferring data with each other. Sensors have low power and low cost. Replace and remove of power in each sensor is very difficult. Due to power constraint decrease in energy constraint is a major research issue like localizing, tracking etc. Our proposed approach is to track the moving object accurately with reduce energy consumption of each sensor. Our proposed approach is to locate the position of moving object within an area. First we describe proposed clustering approach and then within the cluster, a cluster head (CH) is positioned. Then active cluster head is localized to get the current moving position of object through sensors. Within that time span the remaining sensors is standby mode so that energy is minimized. Current location of object is localized by trilateration algorithm. Lastly our proposed system shows the network stability and with less consumed sensors power.

Wireless Sensor Network (WSN), Trilateration, Cluster Head (CH)

[1] Zhenga, J., M.Z.A. Bhuiyan, S. Liang, X. Xing and G. Wang, 2014. Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks.Future Generat.Comput. Syst., 39: 88-99. DOI: 10.1016/j.future.2013.12.014.
[2] M. Akter, M.O. Rahman, M. N. Islam, and M. A. Habib, ”Incremental clustering-based object tracking in wireless sensor networks,” in Proceeding of the International Conference on Networking Systems and Security (NSysS ‘15), pp. 1-5, January 2015.
[3] F. Deldar and M. H. Yaghmaee, "Designing a prediction-based clustering algorithm for object tracking in wireless sensor networks," Computer Networks and Distributed Systems (CNDS), 2011 International Symposium on, Tehran, 2011, pp. 199-203.
[4] KhinThandaSoe, “Increasing Lifetime of Target Tracking Wireless SensorNetworks” ,World Academy of Science, Engineering and Technology 42 2008.
[5] Dan Liu, Nihong Wang and Yi An, “Dynamic Cluster Based Object Tracking Algorithm in WSN”, 2010 Second WRI Global Congress on Intelligent Systems, vol : 1 ,Pg:397-399, Dec 2010.
[6] YingqiXu, Julian Winter and Wang-Chien Lee, “Dual Prediction-based Reporting for Object Tracking Sensor Networks”, 2004 First Annual International Conference on Networking and services, Pg:154-163,August2004.
[7] Mohammad-TaghiAbdizadeh, HadiJamali Rad and BahmanAbolhassani, “A New Adaptive Prediction-Based Tracking Scheme for Wireless Sensor Networks”, 2009 Seventh Annual Communication Networks and Services Research Conference, Pg:335-341,May2009.
[8] Guojun Wang, Md. ZakirulAlamBhuiyan and Li Zhang, “Two-level cooperative and energy-efficient tracking algorithm in wireless sensor networks”, Concurrency and Computation: Practice and Experience, September 2009.
[9] Neelam and Dishant khosla, “The energy efficient techniques for Wireless Sensor Network: A review”,. International Journal of Computer Science and engineering, Vol.4, Issue. 11, pp.38-41, 2016
[10] V.Prasad, VS. Sunsan, “Multi path dynamic routing for data integrity and delay Minimization differentiated services in wireless sensor network”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.20-13, 2016
[11] Manju Bhardwaj, “Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks “,International Journal of Scientific Research in Network Security and Communication Vol.5, Issue.3, pp.1-8, 2017

Manas Kumar Ray, Gitanjali Roy, "An Efficient Proposed Approach for Tracing of Moving Object in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.373-377, 2018.

The research done in this paper aimed to develop a model for the protection of copyright of software using Digital Watermarking by providing a proper way for designing the software. Digital watermarking has now become the research area for the protection of intellectual property right. In this research paper, the technique of cryptography with a assigned key is implemented for the development of the model. This paper gives the procedure of the implementation of the technique.

Copyright, Digital Watermarking, Algorithm, Encryption, Embedding, Detection

[1] S. E. Tsai, K. C. Liu, and S. M. Yang, “An Efficient Image Watermarking Method Based on Fast Discrete Cosine Transform Algorithm”, Hindwani-Mathematical Problems in Engineering, Volume 2017.
[2] Nisha, “Digital Watermarking Techniques: Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 1, 2017.
[3] Himanshu Rastogi, Dr. B.K.Sharma “A Study on Intellectual Property Right and Digital Watermarking”, International Journal of Advanced Research in Computer Science, Volume 8, No. 7, Page No 368-371, 2017.
[4] M. Jamal, S. Mudassar, F. S. Mehmood, M. R. Rehman, "Improved Consistency of Digital Image Watermarking Using RDWT and SVD", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.104-106, 2017
[5] S. Mudassar, M. Jamal, F. S. Mehmood, M. R. Rehman, "Resistance of Watermarked Image against Solidity Attacks", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.31-35, 2017
[6] Rajiv Vasudev,“A Review on Digital Image Watermarking and Its Techniques”, Journal of Image and Graphics, Vol. 4, No. 2, page no 150-153, 2016.[4]
[7] Maninder Kaur and Nirvair Neeru. “Digital Image Watermarking using New Combined Technique”, International Journal of Computer Applications, 145(2), page no 26-30, 2016.[5]
[8] Seizing Opportunity Through License Compliance: BSA Global Software Survey, MAY 2016[6]
[9] J. K. Ravan, T.C.Thanuja “Watermarking Technique for security”, International Conference on Recent Innovations in Engineering and Management, page no 721-725, 2016.[7]
[10] Sumedh P. Ingale et al, “Digital Watermarking Algorithm using DWT Technique”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, page no 01-09, 2016[8]
[11] Anuj Kumar Dwivedi, Dr. B. K. Sharma, Dr. A. K. Vyas,“Watermarking Techniques for Ownership Protection of Relational Databases”, International Journal of Emerging Technology and Advanced Engineering; ISSN 2250-2459 (Online), Volume 4, Special Issue 1, 2014 International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. [9]
[12] Yogesh Awasthi, R.P.Agarwal, B.K. Sharma, “Intellectual Property Right Protection of Browser based Software through Watermarking Technique”, International Journal of Computer Application(IJCA), Volume 97– No.12, page no 32-36, 2014. [10]
[13] V. Kapoor, "A New Cryptography Algorithm with an Integrated Scheme to Improve Data Security", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.2, pp.39-46, 2013.
[14] Dr. B. K. Sharma, Dr. R. P. Agarwal, Dr. Raghuraj Singh, “An IPR of software codes using watermarking For Modular Programming”, ISST Journal of Mathematics & Computing System, Vol. 1 No.1, page no 55-58, 2010.[11]

H. Rastogi, B.K. Sharma, "Development of an expected model for the protection of Copyright of software code using digital watermarking," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.378-382, 2018.

Deep Learning technique which is a new area of Machine Learning is showing huge promise in achieving the original goals of Machine learning: Artificial Intelligence. Deep Learning is being applied in every machine learning problem and has shown great results. In this paper, we evaluate the problem of spam classification using Deep Learning Technique and compare the result with other state-of-art machine learning techniques. The machine learning techniques used in the comparison are: Random Forest, Multinomial Naïve Bayesian and Support Vector Machine. The dataset used in the experiment is the CSDMC_2010 and Enron dataset and the platform used is the WEKA interface. Common features are extracted from the body of the spam and feature vector table is constructed, which is used on all the model. Our experiment shows that Deep Learning model outperform all the other machine learning techniques in terms of true positive & true negative and even in the overall accuracy.

Spam, Deep Learning, Machine Learning, Classify, WEKA

[1] Thiago S. Guzella and Walmir M. Caminhas, “A review of machine learning approaches to spam filtering”, Expert System with Applications”, Elsevier, Vol-36, pp 10206-10222, 2009.
[2] G. Cormack, “Email spam filtering: A systematic Review", Foundations and Trends in Information Retrieval , Vol-1, no. 4, pp. 335–455, 2008.
[3] M. Sahami, S. Dumais, D. Heckerman and E. Horvitz, “A Bayesian Approach to Filtering Junk Email,” AAAI Technical Report WS-98-05, AAAI Workshop on Learning for Text Categorization, 1998.
[4] Drucker H, Wu D, Vapnik VN. “Support Vector Machines for Spam Categorization”, IEEE Transactions on Neural Networks Vol-10, Issue-5, pp 1048-1054, 1999.
[5] Yudong Zhang, Shuihua Wang, Preetha Phillips, Genlin Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection”, knowledge-Based Systems, Elsevier, Vol-64, pp 22-31, 2014.
[6] Zhang L, Zhu J, Yao T, “An Evaluation of Statistical Spam Filtering Techniques Spam Filtering as Text Categorization”, ACM Transactions on Asian Language Information Processing (TALIP), Vol-3, Issue 4, pp 243-269, 2004.
[7] Almeida TA, Yamakami A, “Content-Based Spam Filtering”, The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, pp 1-7, 2010.
[8] Lin Li and Chi Li, ”Research and Improvement of a Spam Filter based on Naïve Bayes”, Proceedings of the 2015 Seventh International Conference on Intelligent Human-Machine Systems and Cybernetics, 2015
[9] Amayri O, Bouguila N, “A study of Spam Filtering using Support Vector Machines”, Artificial Intelligence Review, Vol-34, Isuue 1, pp 73-108, 2010.
[10] Koprinska I, Poon J, Clark J, Chan J, “Learning to Classify e-mail”, Information Sciences, Vol-177, issue 10, pp 2167-2187, 2007.

A.B.Singh, S.B.Singh, Kh.M.Singh, "Spam Classification Using Deep Learning Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.383-386, 2018.

Intelligent fault diagnosis is a promising instrument to deal with mechanical big data because of its capacity in quickly and proficiently handling gathered signals and giving exact diagnosis outcomes. Feature extraction is done manually in most of the traditional techniques which required previous knowledge along with diagnostic expertise. Such procedures take favourable position of human inventiveness is tedious and work escalated. The main possibility of unsupervised component discovering that utilization of intelligence systems to learn raw data, a two-stage learning technique is proposed for intelligent analysis of machines. In the first stage vibration signal is utilized to get a grasp on features from mechanical vibration signals. In the next stage, softmax regression is used to classify the health conditions depends on the studied features. The approach is verified by a motor bearing dataset and a locomotive bearing dataset. It can be seen that using this method high diagnosis accuracy can be obtained. Also, the proposed method reduces the need of human labour making it preferable than the existing methods.

Mechanical big data, unsupervised feature learning, sparse filtering, softmax regression, intelligent fault diagnosis

[1] X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97-107, Jan. 2014.
[2] S. Yin, and O. Kaynak, “Big data for modern industry: challenges and trends,” Proc. IEEE, vol. 103, no. 2, pp. 143-146, Feb. 2015.
[3] W. Qiao, and D. Lu, “A survey on wind turbine condition monitoring and fault diagnosis, ” IEEE Trans. Ind. Electron., vol. 62, no. 10, pp. 6536-6545, Oct. 2015.
[4] Y. Lei, J. Lin, M. J. Zuo, and Z. He, “Condition monitoring and fault diagnosis of planetary gearboxes: A review,” Measurement, vol. 48, pp. 292-305, Feb. 2014.
[5] D. You, X. Gao, and S. Katayama, “WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM,” IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 628-638, Jan. 2015.
[6] Y. Lei, Z. He, Y. Zi, and X. Chen, “New clustering algorithm-based fault diagnosis using compensation distance evaluation technique,” Mech. Syst. Signal Process., vol. 22, no. 2, pp. 419-435, Feb. 2008.
[7] S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
[8] Y. Lei, Z. He, Y. Zi, and Q. Hu, “Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs,” Mech. Syst. Signal Process., vol. 21, no. 5, pp. 2280-2294, Jul. 2007.
[9] M. Kang, J. Kim, J.-M. Kim, A. C. Tan, E. Y. Kim, and B.-K. Choi, “Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis,” IEEE Trans. Power Electron., vol. 30, no. 5, pp. 2786-2797, May 2015.
[10] Yaguo Lei, Member, IEEE, Feng Jia, Jing Lin, Saibo Xing, and Steven X. Ding,” An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data.”, IEEE Transctions on Industrial Electronics, 2016.

M.S. Jamadagni, K.A. Nagarkar, V.S. Badame, C.R. Chindwin, V.M. Lomte, "A Two-Stage Learning Method For Fault Detection of Machines Using Mechanical Big Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.387-391, 2018.

Healthcare systems have grown to be a crucial research area today. Chronic diseases and the cardiovascular diseases are the biggest challenge for India and these diseases are the main cause for the hospitalization of the elderly people. For this purpose, there is need to monitor some parameters of the patient continuously. This system is designed and developed for the remote patient monitoring in the healthcare field. It affords a useful tool where doctor can monitor the heart condition i.e. blood pressure and heart rate with posture and temperature of the patient continuously using web server. Once, the abnormality of the patient’s condition is observed by the doctor, he can give the appropriate decision in the form of prescription to the caretaker of the patient. The availability of this system can help the physician to cover the distance between himself and the patient which helps in early recovery of the patient.

IoT, Chronic Diseases, Remote Patient Monitoring, Blood Pressure, Heart Rate and Body Temperature

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