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In the era of globalization with increased mobility, the learning process is becoming more and more ubiquitous. To satisfy the growing industry requirements and also to enhance the educational growth, it becomes necessary to incorporate new and innovative technologies in to the educational system. Hence to improve the learning system, Mobile learning has been implemented by many colleges and universities. In addition Cloud computing technology has become popular that can reduce the storage cost incurred in Mobile learning. This model of Mobile learning in the Cloud has a number of advantages and has now become a new stage for Knowledge Management in the educational institutions. This paper presents a discussion on how a Knowledge Management system is implemented using Mobile learning in the Cloud with the associated merits and demerits.

Cloud Computing, Mobile Cloud Computing, Mobile Learning, Knowledge Management

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[24] Sharmila, Nisha Jebaseeli, "Enhancing M-Learning System Using Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.51-55, 2016.
[25] Sangeeta Namdev Dhamdhere, “Importance of Knowlegde Management in Higher Education Institutes,” Turkhish Online Journal of Diastance Education, vol. 16, no. 1, pp. 162-183, 2015.

Ramananda Mallya K, B. Srinivasan, "Mobile Learning in the Cloud: New Stage for Knowledge Management," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1454-1458, 2018.

Differential evolution is potentially one of the most promising real parameter optimization algorithms. In last few years, time has witnessed the growth and applications of Differential Evolution in the field of science and engineering. This paper focuses on the application of Differential Evolution (DE) in mobile ad-hoc network to solve optimization problems. According to the conducted studies the applications are classified on the basis of four categories of optimization problems: clustering, routing, security and topology control mechanism. In this article, we review the main work and discuss the future scope of differential evolution with mobile ad-hoc networks. The results of conducted literature survey shows that maximum amount of articles were published in the years 2012 and 2015. About 62.5% of articles were related to topology control mechanism whereas rest of categories shared the similar contribution of about 12.5% each. From the review, it was found that research has been conducted on the application of DE in solving issues such as handling networks (up to 500 nodes) with minimum overhead and strengthening of cryptographic algorithms (up to 9 key size). In future these works can be extended to solve similar problems in large networks. Moreover, it was found that no work has been carried out to find secure route for dissemination using DE.

Differential Evolution, Mutation, Crossover, Fitness Function, Topology control mechanism, Routing, Clustering

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S. Prabha, R. Yadav, "Differential Evolution for Mobile Ad-hoc Networks: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1459-1467, 2018.

Wireless Sensor Networks (WSNs) consists of sensor nodes (SNs) which are deployed in hostile areas to monitor the environmental* or physical conditions such as temperature, sound, pressure etc. WSNs are used in military operations, civilian operations, forest fire detection and healthcare monitoring etc. These SNs forwards their sensor data via multi-hop wireless paths to the Base Station (BS) and are assembled with limited energy, resource, and memory and communication range. We consider base station to be highly secure, unlike sensor nodes which are threat prone. We assume that the attacker can extract security credentials from the compromised nodes. As these sensor nodes are resource constraint, so the number of hops required sending data from event region to base station needs to be reduced. Moreover, as the sensor nodes have limited memory so the number of keys to be stored on these sensor nodes needs to be reduced. This paper basically focuses on reducing the path length of an event region to the base station as well as reducing the number of keys. We also calculate the number of suspicious nodes and cells in the network.

Sensor nodes, security, key management, data gathering, wireless sensor networks

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Nidhi Tawra, Prerna, Bhawna Gupta, "A Location Dependent Key Management Method for WSN With Reduced Path Length And Considering Cell Head," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1468-1474, 2018.

In this paper, The Juctionless Double Gate Vertical MOSFET with metal gate electrode and high-k gate dielectric material (HfO2) has been analyzed using simulation tool. The simulated results show significant improvement in its performance. In this device structure, the metal gate electrode and high-k gate dielectric material (HfO2) are used at the place of polysilicon gate electrode and SiO2 layer. We observed that use of metal gate electrode with SiO2 in JLVMOS exhibits drain current (Idmax) of 0.98 mA, average sub-threshold swing 67mV/dec and DIBL 61mV/V at gate voltage of 1V. When high-k gate dielectric material (HfO2) is used with metal gate electrode in JLVMOS shows drain current (Idmax) of 1.7mA, average subthreshold swing 61mV/dec and DIBL 40mV/V at gate voltage of 1V. This improvements in the performance of JLVMOS using high-k material can be utilized for high performance circuit applications.

Junctionless Double Gate Vertical MOSFET (JLVMOS), Subthreshold swing (S.Swing), Drain Induced Barrier Lowering (DIBL), HfO2, Workfuntion (WF)

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Jagdeep Rahul, "The impact of high-k gate dielectric on Junctionless Vertical Double Gate MOSFET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1475-1478, 2018.

Data mining is one of the main research areas to find particular data from the large set of data. The main aim of this paper is to give more knowledge about the agriculture sector. Agriculture is one of the main economic parts of growing country. Agricultural statistical data from India as been taken here the state of Kerala. To cover Kerala state 14 districts cropped data analysis, in the 12 years pattern of statistical dataset, start from 2005 to 2017. Utilization of Query Optimization Techniques (QOT), K-Means clustering and Filter Techniques (FT). The QOT analysation is to provide variety of query generation and reports. The K-Means clustering, is usage of spatio-temporal cluster data mining techniques. It also provides changes report of the dataset. Using clustering analysis is the process of discovering groups. The FT is used to filter the data season wise and found maximum production of rice occurrence district report.

Cropped area-Grouping-Query optimization-Maximum Production

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[8]Faisal Aburub, Wael Hadi, “Predicting Groundwater Areas Using Data Mining Techniques: Groundwater in Jordan as Case Study”, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:10, No:9, 2016.
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[10]Himanshu Trivedi, Dr. Amit Dutta, “Parametric Analysis of Water Resource Data (E-Governance Projects) Using Data Mining Techniques”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 3 , No.2, Pages : 04 - 07 (2014) Special Issue of ICACE 2014 - Held on March 10, 2014 in Hotel Sandesh The Prince, Mysore, India.
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[12]Kamakshaiah.Kolli and R. Seshadri, “ Ground Water Quality Assessment using Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 76– No.15, August 2013.
[13]K.Yoo et al., “Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity”, Journal of Cleaner Production 122 (2016) 277-286.
[14]L.Yeshodha et al., “ Comparative Analysis for Estimation of Groundwater Potential A-Case Study for Hosur Block, Krishnagiri District , Tamil Nadu”, international journal of innovative research in technology&science | volume 1, number5.
[15]Nallan, S. , & Armstrong, L. “ Assessment of Climate Change Effect on Water Harvesting Structures in Rainfed Regions: Geospatial Data Mining Approach” Proceedings of The Third National Conference on Agro-Informatics and Precision Agriculture 2012. (pp. 201-204). Hyderabad, India. Allied Publishers PVT LTD.
[16]M Waseem Ashfaque et al., “ Robust Strategies of GIS and Geospatial Data mining techniques for drinking ground water quality management, challenges and issues of Drought Case study
[17]Oliver López-Corona et al., “Data Mining of Historic Hydrogeological and Socioeconomic Data Bases of the Toluca Valley, Mexico”, Journal of Water Resource and Protection, 2016, 8, 522-533 Published Online April 2016 in SciRes. http://www.scirp.org/journal/jwarp,http://dx.doi.org/10.4236/jwarp.2016.84044.
[18]Pratap Singh Solanki and R. S. Thakur , “ A Review of Literature on Water Resource Management Using Data Mining Techniques”, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.391.
[19] Rosaida Rosly et al., “ The Study on the Accuracy of Classifiers for Water Quality Application”, International Journal of u- and e- Service, Science and Technology Vol.8, No.3 (2015), pp.145-154 http://dx.doi.org/10.14257/ijunesst.2015.8.3.13
[21]http://www.ecostat.kerala.gov.in/index.php/agricultures.
[22] Ramprasad Kundu et al., “Spatial Growth Pattern of Potato in West Bengal using Multi-temporal MODIS NDVI Data”,IJCSE., Vol.6 , Issue.6 , pp.55-61, Jun-2018.
[23] Kone Chaka et al., “performance comparison of the knn and svm classification algorithms in the emotion detection system emotica”, Int J Sens Netw Data Commun, an open access journal Volume 7 • Issue 1 • 1000153 ISSN: 2090-4886.

I. Shahina Begam, K. Tajudin, "Different Query Optimization Techniques (QOT) using Data Mining Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1479-1487, 2018.

Wireless sensor networks (WSN) is a wireless connection of the sensor nodes and they spontaneously build the networks so that the sensor data can be communicate wirelessly. Data collected from sensor network are used in decision making. A malicious adversary can introduce additional nodes in the network easily and tamper the information. Provenance is used for assessing the trustworthiness of the data, diagnosing network failures, detecting early signs of attacks, etc. But management of the provenance for sensor networks may lead to several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. We propose an efficient mechanism to securely transmit the data through the nodes using AODV Routing algorithm and secure provenance scheme to detect packet drop attacks in the network using Bloom Filter.

Provenance, Ad-hoc On Demand Distance Vector(AODV)

[1] Y. Simmhan, B. Plale, and D. Gannon, “A Survey of Data Provenance in E-Science,” ACMSIGMODRecord, vol. 34, pp. 31-36, 2005.
[2] R. Hasan, R. Sion, and M. Winslett, “The Case of the Fake Picasso: Preventing History Forgery with Secure Provenance,” Proc. Seventh Conf. File and Storage Technologies (FAST), pp. 1-14, 2009.
[3] C. Rothenberg, C.Macapuna,M.Magalhaes, F. Verdi, and A. Wiesmaier,“In-Packet Bloom Filters: Design and Networking Applications,” Computer Networks, vol. 55, no. 6, pp. 1364-1378, 2011.
[4] H. Lim, Y. Moon, and E. Bertino, “Provenance-Based Trustworthiness Assessment in Sensor Networks,” Proc. Seventh Int’l WorkshopData Management for Sensor Networks, pp. 2-7, 2010.
[5] Salmin Sultana, Gabriel Ghinita, Member, IEEE ,Elisa Bertino, Fellow, IEEE , and Mohamed Shehab,Member, IEEE Computer Society, “A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet Drop Attacks in Wireless Sensor Networks”, ieee transactions on dependable and secure computing, vol. 12, no. 3,may/june 2015.
[6] Salmin Sultana, "A Provenance based Mechanism to Identify Malicious Packet Dropping Adversaries in Sensor," in 31st International Conference on Distributed Computing Systems Workshops, 2011.
[7] A. Kirsch and M. Mitzenmacher, “Distance-Sensitive Bloom Filters,” Proc. Workshop Algorithm Eng. and Experiments, pp. 41-50, 2006.
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[9] W. Zhou, Q. Fei, A. Narayan, A. Haeberlen, B. Loo, and M. Sherr, Secure Network Provenance, Proc. ACMSOSP, pp. 295-310, 2011.
[10] S. Chong, C. Skalka, and J.A. Vaughan, Self-Identifying Sensor Data, Proc. Ninth ACM/IEEE Intl Conf. Information Processing in Sensor Networks (IPSN), pp. 82-93, 2010.
[11] S. Sultana, M. Shehab, and E. Bertino, Secure Provenance Transmission for Streaming Data, IEEE Trans. Knowledge and Data Eng., vol. 25, no. 8, pp. 1890-1903, Aug. 2013.
[12] R. Laufer, P. Velloso, D. Cunha, I. Moraes, M. Bicudo, M. Moreira, and O.Duarte, Towards Stateless Single-Packet IP Traceback, Proc.32nd IEEE Conf. LocalComputer Networks (LCN), pp. 548-555,2007.

Anudeepa, Amutha S, "Provenance Based Mechanism to identify Packet Drop attack in WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1488-1492, 2018.

Agriculture is the backbone of Indian economy. Plant disease which mainly affects the leaves is the major constraining factor, which decreases the productivity of cucumber. Farmers are experiencing heavy loss in the yield due to disease attack on leaves. Hence detection and diagnosis of cumber leaf disease at the right time are very essential. Diagnosis of cucumber leaf disease at the early stage helps in preventing heavy loss in the yield. Automatic detection of cucumber disease using image processing techniques helps in monitoring large fields by identifying the diseases as soon as they appear on the leaf. The main purpose of this work is disease identification and classification using image processing techniques. The proposed method mainly comprises of image pre-processing, segmentation using K means clustering to segment the diseased leaf then feature extraction and followed by classification of disease using SRC. The experimental results show that the cumber leaf diseases can be identified more accurately for the proposed work.

Cucumber leaf disease, K-means Clustering, Sparse representation Classification (SRC)

[1] X. W. Z. Y. L. Z. Shanwen Zhang, “Leaf image based cucumber disease recognition using sparse representation classification,” computers and Electronics in Agriculture 134 (2017) 135–141, p. 7, 2017.
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[4] W. X. Dong Pixia, “Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology,” Open Journal of Applied Sciences, 2013, 3, 27-31, p. 5, 2013.
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[7] M. S. Al-Tarawneh, “An Empirical Investigation of Olive Leave Spot Disease Using Auto-Cropping Segmentation and Fuzzy C-Means Classification,” World Applied Sciences Journal 23 (9): 1207-1211, 2013, p. 5, 2013.
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[10] S.C.Ng, “Principal component analysis to reduce dimension on digital image,” in 8th International Conference on Advances in Information Technology, IAIT2016, 19-22, 2017.
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[13] Y. H. a. H. H. Hyunsup Yoon, “Image Contrast Enhancement based Sub-histogram Equalization Technique without Over-equalization Noise,” International Journal of Electrical and Computer Engineering, p. 7, 2009.
[14] G. Z. Libo Liu, “Extraction of the Rice Leaf Disease Image Based on BP Neural Network,” in Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, 2009.
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Shrutika.C.Rampure, Dr. Vindhya .P. Malagi, Dr. Ramesh Babu D.R, "Identification of Cucumber Leaf Disease using Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1493-1499, 2018.

Myocardial infarction is one of the most serious and prevailing heart disease faced in today’s world, occurs when blood supply stops to a certain artery. Early and accurate detection of myocardial infarction reduces the mortality rate of heart attack. In this paper, we proposed an algorithm for early detection of myocardial infarction based on analysis of ST segment in electrocardiogram (ECG). This algorithm consists of following steps: loading of a database from physionet, preprocessing of a signal, detection of QRS complex, P, T wave, ST segment and other related parameters. European ST-T database was used for evaluation of an algorithm for detection of ST segment.

Electrocardiogram (ECG), myocardial infarction (MI), ST segment, QRS complex, European ST-T database

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[4] Duck Hee Lee, Jun Woo Park, Jeasoon Choi, Ahmed Rabbi and Reza Fazel-Rezai, “Automatic Detection of Electrocardiogram ST Segment: application in Ischemic Disease Diagnosis”, International Journal of Advanced Computer Science and Applications, Vol.4, No.2, 2014.
[5] Jayachandran, E.S., et al.: Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34(6), pp. 985–992, 2010
[6] Banerjee, S., Mitra, M.: Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans. Instrum. Meas. 63(2), pp. 326–333,2014
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[12] Zheng, H., Wang, H., Nugent, C., Finlay, D.: Supervised classification models to detect the presence of old myocardial infarction in body surface potential maps. In: Computers in Cardiology, pp. 265–268. IEEE, Valencia, Spain 2006
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[16] Rachid Haddadi, Elhassane Abdelmounim, Mustapha Elhanine, Abdelaziz Belaguid, “ST Segment Analysis Using Wavelet Transform”, International Journal of Computer Science and Network Security, Vol.17 No.9,2017

Nang Anija Manlong, Jagdeep Rahul, Marpe Sora, "ST Segment Analysis for Early Detection of Myocardial Infarction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1500-1504, 2018.

The definition of software engineering might blast something like, “An organized, analytical approach to the analysis, design, development, use, reliability and maintenance of software.”Software reliability is the probability that a software system will function without failure under a given environment and during a specified period of time. To be cost and time effective, reliability engineering has to be coordinated with quality assurance activities, in agreement with Total Quality Management (TQM) and concurrent engineering efforts. To build in reliability and maintainability into complex equipment or systems, failure rate and failure mode analyses have to be performed early in the software development life cycle (SDLC) and be supported by design guidelines for reliability, maintainability and software quality as well as extensive design reviews. There are different types of software reliability models (SRMs) used for different phases of the software development life-cycle. With the growing demand to deliver quality software, software development organizations need to manage quality achievement and assessment. In this paper, we present the utility of a software reliability growth model is related to its stability and predictive ability. Stability means that the model parameters should not significantly change as new data is added. Predictive ability means that the number of remaining defects predicted by the model should be close to the number found in field use.

Software reliability models, model classification, software reliability growth model, Time Between Failure, Fault Count Model

[1] Yamada, S. and Osaki, S., “Software Reliability Growth Modeling: Models and Applications, IEEE Trans. On Software Engineering, vol. SE-11, no. 12, pp. 1431-1437, December1985.
[2] Hoang Pham, “Software Reliability", Springer-Verlag Singapore,Pte. Ltd 2000.
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[5] Linda M. Laird and M. Carol Brennan, "Software Measurement and Estimation", Copyright John Wiley & Sons, Inc., 2006.
[6] A. Loganathan, R. Jeromia Muthuraj, "Importance Of Environmental Factors Affecting Software Reliability”, Global And Stochastic Analysis, Vol. 4 No. 1, 119-125, January 2017.
[7] Alan Wood,"Software Reliability Growth Models",© Tandem Computers, 1996.
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[9] Ch. Ali Asad, Muhammad Irfan Ullah, Muhammad Jaffar-Ur Rehman, "An Approach for Software Reliability Model Selection”, Proceedings of the 28th Annual International Computer Software and Applications Conference (COMPSAC’04), IEEE, 2004.

J.K. Mantri, R.S Bala, "The Selection of Software Reliability Growth Models in Software Development Life Cycle," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1505-1512, 2018.

In Computer vision, object detection has become one of the most popular research fields. Human eyes can distinguish a various number of objects in images with less exertion, despite the fact that the objects in the images differ at various perspectives. This task is as yet a challenge for Computer vision frameworks. The goal is to present an efficient approach for fruit detection which can be used for yield estimation. In this paper, an efficient survey presented for Fruit detection in order to estimate the yield using Convolutional Neural Networks. This approach mainly benefits farmers and also applying automation in the field of agriculture, this helped to create several advancements to the industry. Various methods are surveyed in this paper in order to solve the existing problems of Fruit detection.

Convolutional Neural networks, fruit detection, learning methods

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[4] K. Yamamoto, W. Guo, Y. Yoshioka and S. Ninomiya, "On plant detection of intact tomato fruits using image," vol. 2191–12206., 2014.
[5] Q. Wang, S. Nuske, M. Bergerman and S. Singh, "Automated Crop Yield Estimation for Apple Orchards.," 17–22 June 2012..
[6] C. Bac, J. Hemming and E. van Henten, "Robust pixel-based classification of obstacles for robotic harvesting," Vols. 96, 148–162., 2013.
[7] C. Hung, J. Nieto, Z. Taylor and J. S. Underwood, "Orchard fruit segmentation using multi-spectral," 3–7 November 2013.
[8] K. Kapach, E. Barnea, R. Mairon, Y. Edan and O. Ben-Shahar, "Computer vision for fruit harvesting robots-state," 2012.
[9] Y. Song, C. Glasbey, G. Horgan, G. Polder, J. Dieleman and G. van der Heijden, "Automatic fruit recognition," 2014.
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Moksha Lakshmi B N, Vindhya P Malagi, "A Survey on Identification and Detection of Fruits based on Deep Neural Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1513-1517, 2018.