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Sequence classification is an efficient task in data mining. Sequence classification problem can be solved by rules that consist of interesting patterns. Another major problem in data mining is pattern mining. In pattern mining, patterns can be used as rules. These rules may be more accurate or simpler to understand while classifying the data object. The cohesion and support of the pattern are used to define interestingness of a pattern. The degree of interest in patterns in a given class of sequences can be measured by combining these two factors. The patterns found can be used to generate reliable classification rules. There are two different ways to build a classifier. The first classifier consists of advanced classification methods that rely on association rules. In the second classifier, the value belonging to the new data object is first measured then the rules are ranked. A well-known methods of association classification are CBA (Classification based on Association rules), CMAR (Classification based on Multiple class-Association Rules), and CPAR (Classification based on Predictive Association Rules) etc. mine the frequent and confident patterns for building a classifier. All these approaches do not consider the cohesion of a pattern and applicable to only one type of pattern. These limitations can be overcome by taking into account a cohesion factor to define interestingness of pattern and can consider another type of pattern.

Sequence classification, interesting patterns, classification rules

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[4] X. Yin and J. Han, “Cpar: Classification based on predictive association rules,” in Proc. SIAMInt. Conf. Data Mining, 2003, pp. 331–335.
[5] J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu, Mining sequential patterns by patterngrowth: The prefixspan approach,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 11, pp. 1424–1440, Nov. 2004
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variable order hidden Markov model with duration based on frequent sequence mining,” ACM Trans. Knowl. Discovery Data, vol. 4, no. 1,p. 5, 2010.
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[11] Cheng Zhou, Boris Cule, and Bart Goethals, ”Pattern Based Sequence Classification” vol.28, NO. 5, May 2016

G. R. Mane, S. B. Bhagate, "Sequence Classification by Using Auto Calculation of Support," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.393-397, 2018.

Smartphones bring people into different level of computing and life style but all the features and computing abilities of smartphone is entirely rely upon battery backup. Short of power backup directly distresses user experience and it leverages new energy efficient findings. Over the past ten years, plethora of finding is carried out related to energy optimization and conservation developments of smartphone and mobile communication and this changes the entire perspective of the platform. This paper tries to fetch such a trend setting outcomes from the legendary researchers, compares them and provides evidence of the fact. This review of literature covers a wide range of the study regarding 3G/4G network communication, Smartphone Apps performance and usage patterns and highlights their research proposals, solutions, architectures and results regarding to the energy efficiency in smartphone and cellular network. This literature study may offer many research directions to the upcoming researchers.

Traffic Aware Optimization, Radio Signal Strength, Screen Off, Smartphone, Energy Efficiency, Cellular Network, Mobile Computing

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[14] Feng Qian et al. “Characterizing Radio Resource Allocation for 3G Networks,” in Proc. 10th ACM SIGCOMM Conference on Internet Measurement, Australia, pp. 137-150, 2010.
[15] Sanae Rosen et al. “Revisiting Network Energy Efficiency of Mobile Apps: Performance in the Wild,” in Proc. Internet Measurement Conference, pp. 339-345, 2015.
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[25] Xue, Guangtao et al. “SmartCut: Mitigating 3G Radio Tail Effect on Smartphones.” IEEE Transactions on Mobile Computing, pp.169-179, 2015.
[26] Feng Qian et al. “Periodic Transfers in Mobile Applications: Network-wide Origin, Impact, and Optimization,” in Proc. 21st international conference on World Wide Web, pp. 51-60, 2012.
[27] Shuo Deng et al. “Traffic-Aware Techniques to Reduce 3G/LTE Wireless Energy Consumption,” in Proceedings of the 8th international conference on Emerging networking experiments and technologies (CoNEXT `12). ACM, New York, pp.181-192, 2012.
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[36] Zhao, Bo et al. “Energy-Aware Web Browsing in 3G Based Smartphones.” in Proc. IEEE 33rd International Conference on Distributed Computing Systems, pp. 165-175, 2013.
[37] Feng Qian et al. “How to Reduce Smartphone Traffic Volume by 30%?,” in Proc. International Conference on Passive and Active Network Measurement, pp. 42-52, 2013.
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[41] Qian Zhang et al. "QoS-adaptive multimedia streaming over 3g wireless channels." MMSA2000, pp. 9-10, Nov 2000.
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[46] Y. Kim and J. Kim, “Personalized diapause: Reducing radio energy consumption of smartphones by network-context aware dormancy predictions,” in Proc. Workshop Power Aware ComputSyst., 2012.
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[50] Martins, Marcelo et al. “Selectively Taming Background Android Apps to Improve Battery Lifetime.” In Proc.USENIX Annual Technical Conference, 2015.
[51] Chen, Xiaomeng et al. “Smartphone Energy Drain in the Wild: Analysis and Implications.” In Proc. SIGMETRICS, 2015.
[52] Ding li et al, “An Empirical study of the energy consumption of android applications,” in Proc. IEEE international conference of software maintenance and evolution, pp.121-130, 2014.
[53] Ville Kononen et al, “Optimizing power consumption of always-on applications based on timer alignment,” in Proc. Third International Conference on Communication Systems and Networks (COMSNETS), 2011.
[54] L. Ravindranath, et al. “Appinsight: Mobile app performance monitoring in the wild,” in Proc. OSDI, 2012.
[55] H. Liu, Y. Zhang, and Y. Zhou, “Tailtheft: Leveraging the wasted time for saving energy in cellular communications,” in Proc. 6th International Workshop on MobiArch, pp. 31–36, 2011.
[56] Sai Suren Kumar Kasireddet al. “Measurements of Energy Consumption in Mobile Applications with respect to Quality of Experience,” Master thesis, Blekinge Institute of Technology, 2012.
[57] Abdelmotalib, Ahmed et al. “Background Traffic Analysis for Social Media Applications on Smartphones,” in Proc. Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 817-820, 2012.
[58] Qualcomm, “System Parameter Recommendations to Optimize PS Data User Experience and UE Battery Life”, Engineering Services Group, Technical Document, 2007.
[59] Hossein Falaki et al. “Diversity in Smartphone Usage,” in Proc. MobiSys’10, San Francisco, USA, 2010.
[60] Y. Diao et al. “Fast and memory-efficient regular expression matching for deep packet inspection,” in Proc. ACM/IEEE Symposium Architecture of Network Communication System, pp. 93–102, 2006.
[61] Nan, Eleonora et al. “User data traffic analysis for 3G cellular networks,” in Proc. 8th International Conference on Communications and Networking in China (CHINACOM) pp.468-472, 2013
[62] Ahmad Rahmati, Angela Qian, and Lin Zhong, “Understanding Human-Battery Interaction on Mobile Phones,” Proceedings of the 9th Conference on Human-Computer Interaction with Mobile Devices and Services, Mobile HCI 2007, pp. 265-272, Singapore, September, 2007.
[63] Nishkam Ravi et al, “Context-Aware Battery Management for Mobile Phones,” A feasibility study, 2006
[64] “The New Multi-screen World: Understanding Cross-platform Consumer Behavior”, Google, 2012.
[65] Chen, Xiaomeng et al. “Smartphone Background Activities in the Wild: Origin, Energy Drain, and Optimization.” in Proc. MobiCom, 2015.
[66] Feng Qian and junxian Huang et al. “Screen-Off Traffic Characterization and Optimization,” in Proceedings of the ACM Internet Measurement Conference, IMC `12, pages 357–364. ACM, 2012.
[67] Bijan Golkar et al. “Resource Allocation in Autonomous Cellular Networks,” IEEE Transactions on Wireless Communication, 2013.
[68] Qualcomm, Technical Document, “Managing Background Data Traffic in Mobile Devices,” Jan 2012.
[69] Selim Ickin et al, “QoE-Based Energy Reduction by Controlling the 3G Cellular Data Traffic on the Smartphone”
[70] Ella Peltonen et al. “Energy modeling of system settings: A crowdsourced approach,” in IEEE International Conference on Pervasive Computing and Communications, PerCom USA, pp. 37-45, 2015.
[71] Narseo Vallina et al. “Energy management techniques in modern mobile handsets,” IEEE Communication Surveys Tutorials, PP(99):1 –20, 2012.
[72] Aaron Schulman et al, “Bartendr: A Practical Approach to Energy-aware Cellular Data Scheduling,” in Proc. MobiCom’10, September 2010.
[73] Abhijnan Chakraborty et al, “Coordinating Cellular Background Transfers using LoadSense,” in Proc. MobiCom’13, 2013.
[74] Ning Ding et al, “Characterizing and Modeling the Impact of Wireless Signal Strength on Smartphone Battery Drain,” in Proc SIGMETRICS’13, June 17–21, 2013.

S.Pandikumar, G.Sujatha, M.Sumathi, "Energy Efficient Developments of Smartphone Environment and Cellular Network – Opportunities and Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.398-406, 2018.

One of the biggest challenges that many of the 21st-century teachers face in traditional classroom teaching is the difficulty to deal with students from diverse backgrounds. Especially in the case of slow learners they find it so hard to deal with and make them learn their academic subjects. Teachers are giving instructions or delivering learning contents to learners, without understanding the learner profile parameters such as learning style, motivation, attitude, aptitude etc. They should understand the art and science of teaching. In the current education paradigm students are compelled to learn the same learning material at the same time and rate. Hence teachers are expected to adopt new methods and technologies to eliminate this problem. E-learning is an activity where learners are able to achieve their educational goals based on their skills, interest, motivation, learning style etc. Through adaptive learning learners’ exact needs, their goals, preferences, etc. can be achieved during the learning process. Hence, the objective of the study is to develop a frame work of an adaptive learning system which helps the slow learners to be more active and engaged in their learning process.

Adaptive learning, E-contents, Instructional Design, Learning Management System, Slow Learners

[1] P.Tripathi, S.Mukerji, “Handbook of Research on Technology-Centric Strategies for Higher Education Administration”,IGI Global. 2017.
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[4] B. Holmes, J. Gardner,” E-learning: Concepts and practice”, Sage,2006.
[5] S. M. Shweta Pawar, “A Proposed System for Adaptive E-learning Using Ant Colony Optimization” IJSART ,pp. 72-76, 2015.
[6] N. Radwan “An adaptive learning management system based on learner’s learning style”, International Arab Journal of e-Technology, Vol. 3, Issue.4, pp.7, 2014.
[7] S. Graf, T. C. Liu, “Supporting teachers in identifying students` learning styles in learning management systems: An automatic student modelling approach”, Journal of Educational Technology & Society, Vol. 12, Issue.4, pp.3-14,2009.
[8] J. Yang, Z. X. Huang, Y. X. Gao, & H. T. Liu, ”Dynamic learning style prediction method based on a pattern recognition technique”, IEEE Transactions on Learning Technologies, , Vol. 7, Issue.2, pp.165-177, 2014.
[9] Y.H. Hung, R.I. Chang, C.F Lin, “Hybrid learning style identification and developing adaptive problem-solving learning activities”, Computers in Human Behavior, Vol.55, pp.552-561, 2016.
[10] A. Klašnja-Milićević, B. Vesin, M. Ivanović, Z. Budimac,& L. C. Jain, ”E-Learning Systems: Intelligent Techniques for Personalization” Springer, Vol. 112, 2016.
[11] P. Sarkar, C. Kar, “Adaptive E-learning Using Deterministic Finite Automata”, International Journal of Computer Applications, Vol. 97, Issue.21, pp.14-17, 2014.
[12] C. Makkena, K. Anuradha, “A Study on Efficiency of Data Mining Approaches to Online -Learning Methods” , International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 2, Issue. 4, pp. 808-816, 2017.
[13] A. Roy, K.Basu,”A Comparative Study of Statistical Learning and Adaptive Learning”, arXiv preprint arXiv: 1511.07538,2015.
[14] L.K. Poon, S.C. Kong, M.Y. Wong, T.S.Yau, ” Mining Sequential Patterns of Students’ Access on Learning Management System”, In the proceedings of the 2001 International Conference on Data Mining and Big Data, Springer, Cham, pp.191-198, 2017.
[15] S. J. Raut, “Smart Class Learning Management System for School Education”, International Journal of Scientificic Research in Computer Science and Engineering, Vol.06 , Special Issue.01 , pp.9-13, Jan-2018
[16] J. F. Hughes, Computer graphics: principles and practice. Pearson Education,2014.
[17] J. G. Ruiz, M. J. Mintzer, R. M. Leipzig, “The impact of e-learning in medical education” Academic medicine, Vol.81, Issue.3, pp.207-212, 2006.
[18] L. Joseph,, & S. Abraham, “Instructional design for learning path identification in an e-learning environment using felder-silverman learning styles model” In the proceedings of the IEEE International Conference on Networks & Advances in Computational Technologies (NetACT), pp. 215-220, 2017.
[19] A.Deepa E. C. Blessie,”Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.23-27, 2018.

Lumy Joseph, Sajimon Abraham, "An adaptive learning frame work for slow learners in an e-learning environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.407-412, 2018.

A mobile ad hoc network (MANET) is a collection of wireless mobile nodes dynamically forming a network topology without the use of any existing network infrastructure or centralized administration. Routing is the process which transmitting the data packets from a source node to a given destination . The main classes of routing protocols are Proactive, Reactive and Hybrid. A Reactive (on demand) routing strategy is a popular routing category for wireless ad hoc routing. In this chapter an attempt has been made to compare two Reactive (on demand) routing protocols in MANETs: EMMDV and AOMDV protocol.

MANET,AODV, AOMDV,EMMDV and MPR

B. Shen, H. Y. Ke, and Z. G. Hu, “Adaptive mechanisms to enhance Internet connectivity for mobile ad hoc networks,” Wireless Communications, Networking and Mobile Computing, 2006. WiCOM 2006 International Conference on 22-24 September 2006.
[2]. ZHU. W. “Multipoint Relay flooding” Master of Science Thesis Stockholm, Sweden TRITA-ICT-EX- 2009.
[3]. Perkins CE, Belding-Royer E, Das SR. Ad hoc on-demand distance vector (AODV) routing,July 2003. RFC 3561.
[4]. T.D. Le and H. Choo, ―Efficient Flooding Scheme Based on 2-Hop Backward Information in Ad Hoc Networks, In Proceeding of the IEEE International Conference on Communications, pages 2443-2447, 2008
[5]. Amirhossein Moravejosharieh , Hero Modares, Rosli Salleh and Ehsan Mostajeran, Performance Analysis of AODV, AOMDV, DSR, DSDV Routing Protocols in Vehicular Ad Hoc Network,Research Journal of Recent Sciences ISSN 2277-2502Vol. 2(7), 66-73, July (2013)
[6]. P.P.Pham, S.Perreau, “Increasing the network performance using multi-path routing mechanism with load balance”, Institute of Telecommunications Research, University of South Australia, Australia, September 2003
[7]. M.Geetha ,Dr.R.Umarani“An Overview of Multi Point Relay AODV in Mobile Ad hoc Network “ ©gopalax -International Journal of Technology And Engineering System(IJTES): Jan –March 2011 ,
[8]. M.Geetha ,Dr.R.Umarani” Performance Comparison And Implementation of MPRAODV and AODV In Mobile Adhoc Network”,International Journal of Advanced Computer Applications( IJACA), Vol.II, Issue-I,pp.32-39,March,2012
[9]. M.Geetha ,Dr.R.Umarani “Trigger Agent in Core Router Based on EMMDV Protocol for MANETs” , European Journal of Scientific Research, Vol.100 No.3 May 2013,,pp.408-415.
[10]. Dr.M.Geetha “A Novel Approach for Internet Connectivity in Mobile Ad Hoc Network Using Enhanced Multipath MPR AODV Routing Protocol” Singaporean Journal of Scientific Research(SJSR), Vol.9.No.1 2017,Pp.1-8
[11]. Ducksoo Shin*, Jonghyup Lee, Jaesung Kim, And Jooseok” An Adaptive Ad Hoc On-Demand Multipath Distance Vector Routing Protocol Using Dynamic Route Switching “Journal Of Engineering Science And Technology vol. 4, no. 2 (2009) 171 - 183

M. Geetha, "Performance and analysis of Adhoc On Demand Multipath Distance Vector Routing Protocol ( AOMDV) and Enhanced Multipath MPR AODV Routing Protocol(EMMDV) in MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.413-418, 2018.

Road accidents are one of the biggest concerns leading to loss of life. Road accident injuries are predictable and preventable, but it is important to understand the ways in which road safety interventions and technology can be used to prevent or may be completely wash out this curse. One of the major factors for this is over speeding. This paper talks about how introducing IoT devices in automobiles and accident prone areas helps reduce the risk of accidents. Our proposal works by sensing the automobiles in accident prone areas based on its current location, checking for its speed and notifying the driver about what needs to be done. In cases of accidents, notifications are sent to the automobiles nearby informing them about it, while distress signals are sent to the nearest police station and hospital for immediate help. The idea would also be useful in many other applications such as alerting the concerned in the event of an accident, thus helping in immediate response from emergency services.

Safety, Accident, IoT, Cloud, Location, Speed

[1] Jorge Z., Carlos T. , Juan C. and Pietro M., “Providing Accident Detection in Vehicular Networks through OBD-II Devices and Android-based Smartphones”, Proceedings of the IEEE 36th Conference on Local Computer Networks, Washington, DC, USA, pp.813-819, October 2011.
[2] Patel K.H., "Utilizing the Emergence of Android Smartphones for Public Welfare by Providing Advance Accident Detection and Remedy by 108 Ambulances", International Journal of Engineering Research & Technology (IJERT), Vol.2, Issue.9, pp.1340-1342, September 2013.
[3] Sneha R.S. and Gawande A. D., "Crash Notification System for Portable Devices", International Journal of Advanced Computer Technology (IJACT), Vol.2, Issue.3, pp.33-38, June 2013.
[4] Sadiki Lameck Kusyama1 , Dr. Michael Kisangiri1 , Dina Machuve, “Automatic vehicle over speed, accident alert and locator system for public transport (Buses)”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.2327-2331, August 2013.
[5] C.Vidya Lakshmi, J.R.Balakrishnan, “Automatic accident Detection via Embedded GSM message interface with Sensor Technology”, International Journal of Scientific and Research Publications, Vol.2, Issue.4, April 2012.
[6] Sonali N. and Maheshwari R., “An Intelligent Obstacle and Overspeeding Sensor to prevent dashing of Motorcycles”, International Journal of Soft Computing and Artificial Intelligence, Vol.1, Issue.1, pp.14-17, Nov 2012.
[7] Hamid M. Ali, Zainab S. Alwan, “Car Accident Detection and Notification System using Smartphone”, International Journal of Computer Science and Mobile Computing, Vol.4, Issue.5, pp.620-635, April 2015.
[8] Chris T., White J., Dougherty B. , Albright A. and Schmidt DC.," WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones", International Journal of mobile network and application, Springer, Hingham, MA, USA., Vol.16, Issue.3, pp.285-303, March 2011.
[9] Aditi Zear, Pradeep Kumar Singh and Yashwant Singh, “Intelligent Transport System: A Progressive Review”, Indian Journal of Science and Technology, Vol 9(32), pp.1-8, August 2016.
[10] Pradeep Kumar Singh, Vibhu Kapoor, Kopal Tripathi, Yugal Kumar and Shaweta Khanna, “IoT Enabled Immediate Response System For People In Case Road Accidents”, International Journal of Latest Trends in Engineering and Technology, Vol.9, Issue.2, pp.132-138, August 2017.

R. Sai Prasanth, Soorya Sahar P.N., Spoorthi S., Sweta Kumari, "Preventing Road Accidents using IoT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.419-423, 2018.

Information retrieval as an executive Demas extensible as a technique near procedure as takeout applicable information use for Big Information. Information mining as advanced study big extent information near concludes original information using sketch model, leaning, as a associations. Among the extend World Wide Web, this digit information lay up as a completed obtainable by machine amplified enormously, as a technique near retrieve information as about big information grow enormous consequence used for business, scientific as a engineering do research community. Frequent Itemset Mining individual the majority widely functional measures near retrieve about use information from information. Nonetheless, as its technique be useful near Big Information, combinatorial eruption cuspidate itemsets has grown to be challenge. A current growth use neighborhood about parallel programming obtainable outstays apparatus near conquer difficulty. However, apparatus include possess scientific disadvantage, for example impartial information allocation as an inter-communication expenses. During advance study, we scrutinize request about Frequent Itemset Mining using MapReduce framework. We bring in original technique used for takeout big informationsets: Big-Frequent-Itemset Mining. Its technique optimized near sprint lying on extremely big informationsets. Come near comparable consequently, we apply a dispersed association rule mining algorithm lying on big information set forename as a Genetic Algorithm as a Adaptive-Miner which utilize adaptive approach used for judgment frequent patterns among superior accurateness as a competence. Adaptive-Miner utilizes adaptive approach based lying on the fractional processing informationsets. Adaptive-Miner constructs implementation strategy previous to all iteration as a go away among top appropriate strategy reduce time as a space complexity. Adpative-Miner is dynamic association rule mining algorithms adjust this come near based lying on scenery about informationset. Consequently, this dissimilar as enhanced modern static association rule mining algorithms. We behavior techniqueically research near increase approaching keen on efficiency, as a scalability about Adaptive-Miner algorithm lying on big informationset. use its research’s, we exhibit scalability about techniques.

Genetic Algorithm, association rule mining algorithm, association rules; big data sets; frequent pattern mining; map reduce.

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Sachin Kumar Pandey , "Analytical Study of Association Rule Mining Algorithm for Retrieving Frequent Itemsets in Big Datasets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.424-436, 2018.

Multi User Detection or MUD is a major challenge faced in the field of wireless and cellular communications. Wireless Networks are becoming increasingly everywhere in computer networks due to less cost and maintenance overhead. While some wireless networks may operate in regulated spectrum and the majority operate in the unregulated (ISM) band. It is highly challenging for a base or control station to successfully detect signals from multiple users in the same frequency range. This may occur due to comparatively small frequency re-use distance. This paper proposes a technique based on decision feedback equalization (DFE) and strongest signal cancellation for multi-user detection (MUD) in wireless networks. It has can be seen that by employing the proposed system, the Bit Error Rate (BER) for strong, average and weak users assemble. Thus it indicates the fact that all the signals are detected with equal accuracy.

Multi User Detection (MUD), User Equipment (UE), Inter Symbol Interference (ISI), Frequency Selective Channel, Bit Error Rate (BER).

[1] X Wang, H Lou, Yonghang Jiang , Xiang-Yang Li, Yunhao Liu , Panlong Yang, “Montage: Combine Frames with Movement Continuity for Real-time Multi-User Tracking”, IEEE Transactions on Mobile Computing, IEEE 2017.
[2] B Wang, L Dai, Y Zhang, T Mir, “Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA”, Vol-20, Issue-11, IEEE 2016.
[3] J Choi, J Mo, RW Heath, “Near maximum-likelihood detector and channel estimator for uplink multiuser massive MIMO systems with one-bit ADCs”, Vol-64, Issue-5, IEEE Xplore- 2016.
[4] Maha Alodeh ; Symeon Chatzinotas ; Björn Ottersten, “Energy-Efficient Symbol-Level Precoding in Multiuser MISO Based on Relaxed Detection Region”, Vol-15, Issue-5, IEEE Xplore- 2016.
[5] Fabian Monsees ; Matthias Woltering ; Carsten Bockelmann ; Armin Dekorsy, “Compressive Sensing Multi-User Detection for Multicarrier Systems in Sporadic Machine Type Communication”, IEEE 2015.
[6] Bichai Wang ; Linglong Dai ; Yifei Yuan ; Zhaocheng Wang, “Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access”, IEEE-Xplore 2015.
[7] S Wang, Y Li, J Wang, “Multiuser detection in massive spatial modulation MIMO with low-resolution ADCs”, IEEE Transactions on Wireless Communication, Vol-14, Issue-4, IEEE Xplore 2015.
[8] S Narayanan, MJ Chaudhry, A Stavridis, “Multi-user spatial modulation MIMO”, Proceedings of Wireless Communications and Networking Conference (WCNC), IEEE 2014.
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[12] Henning F. Schepker ; Carsten Bockelmann ; Armin Dekorsy, “Exploiting Sparsity in Channel and Data Estimation for Sporadic Multi-User Communication”, Proceedings in Wireless Communication Systems (ISWCS 2013), IEEE 2013.
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Aditya Rege, Santosh Pawar, Sheetesh Sad, "Multi-User Detection in Wireless Networks using Successive Signal Detection and Decision Feedback Equalization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.437-443, 2018.

We look for figuring out non-trivial integral solutions of the negative Pell’s equation involving two digit sphenic numbers 30 and 66 for the choice of odd integers including zero. We find some interesting recurrence relations using the solutions.

Pell equation, Integer Solutions, 2 digit sphenic numbers

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S. Vidhya, G. Janaki, "An Integral Solution of Negative Pell’s equation involving two digit sphenic numbers," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.444-445, 2018.

Rapid growth in technology has enabled cyber attackers hack the data and misuse the content transferring using the authentication method. Due to this information transferring requires more security. Attackers can thoroughly figure out sensitive data from the traditional methods, using the hash value. Traditional methods of visual cryptography approach were used to transfer the sensitive data. Enhanced image scheme is proposed to hide the sensitive data. Transferring sensitive data in the form of image with the printed text requires more security without allowing the attackers to change the sensitive data. In this project OCR engine, tesseract approaches help in recognizing and conversions of the printed text to the machine typed characters. AES algorithm is then applied to encrypt these machine typed characters. Steganography technique is used for binding the secure data with the cover image without changing the input data format. Digital signature approach is used for verifying the extracted and decrypted character from the output image with the input data. Using multiples algorithms in the proposed approach enables more security for transferring of sensitive data. PNG image format is used for the implementation of the proposed system for better accuracy.

OCR engine, Tesseract, Cryptography, Steganography and Digital Signature

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[4] Arnab Mallik, Asst. Professor, CSE Dept, Calcutta Institute of Engineering and Management “An Open Source Tesseract based Tool for Extracting Text from Images with Application in Braille Translation for the Visually Impaired”, International Journal of Computer Applications (09758887) Volume 68–No.16, April 2013.
[5] Chirag Patel, Atul Patel, Dharmendra Patel, Charotar University of Science and Technology “Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study” international Journal of Computer Applications (0975 –8887) Volume 55–No.10, October 2012.
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[8] José Manuel Ortega, “Python Cryptography & Security” europython 2016, bilbao, 20-26 July.
[9] Pri a Bharti, Roopali Soni, “A New Approach of Data Hiding in Images using Cryptography and Steganography”, International Journal of Computer Applications (0975 – 8887) Volume 58–No.18, November 2012.
[10] Chirag Patel, Atul Patel, Dharmendra Patel “Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study” International Journal of Computer Applications (0975 – 8887) Volume 55–No.10, October 2012.
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[12] Abdel-Karim Al Tamimi, “Performance Analysis of Data Encryption Algorithms” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 3, March 2016.
[13] Avinash Kak, “AES: The Advanced Encryption Standard Lecture Notes on “Computer and Network Security”, Purdue University, February 2018.
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Madhura.M, Mohana Kumar.S, "Enhanced Image Transferring Scheme using Security Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.446-451, 2018.

Currently every person’s use short text in real life for communication and chatting purpose. Short texts are also uses in social posts, news titles, events, search queries, tweets, conversations, keywords, Short text understanding is a confusing process in ideas deals with secret messages. The short text is produce that contain social posts, discussions, keywords and news titles which are restricted context and represent the significance of the text or insufficient information. As short text has more than one meaning, they are challenging to understand as they are noisy and ambiguous. The term can be any single or multi-word. Short texts do not contain satisfactory information. Some short texts have unique features. So these short texts are difficult to handle. It essential well understand the short text. Semantic analysis is necessary to understand the short text properly. Tasks such as part-of-speech tagging, concept labelling and segmentation are used for semantic analysis. Conduct short text uses in real life information. The prototype system is constructed and used to recognize the short text. These systems deliver the semantic knowledge from knowledge base and collection of written words that are automatically harvest. Creating construction of co-occurrence network and term extraction showing to better understand for short text.

Short Text, Semantics, Text segmentation, co-occurrence,Term Extraction

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P. G. Kamble, S. B. Bhagate, "Semantics discovery of Short Text," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.452-456, 2018.