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In this paper, the performance metrics of routing protocols by varying node density and transmission range are analyzed using the statistical tool called Two-way ANOVA. The performance of routing protocols namely AODV, DSR, DSDV and OLSR are evaluated in two different traffic classes using NS2 simulation study. The sample data is collected from this simulation study. The Quality of Service metrics such as PDR, NRO, E2ED, TP and Jitter are treated as dependent variables which are evaluated by varying independent factors namely Node Density and Transmission Range. The ANOVA test is intended to ratify the correctness of the results and to investigate the important metrics while using individual factors and interaction effect between factors. The results of the ANOVA test found to be inline with that of NS2 simulation.

AODV, DSR, DSDV, OLSR, ANOVA, MANETs

[1] K.Gangadhara Rao, Ch.Suresh Babu, B.Basaveswara Rao, D.Venkatesulu, “Simulation Based Performance Evaluation of Various Routing Protocols in MANETs”, IOSR journal of Mobile Computing & Application, Vol 3, Issue 4, pp 23-39, 2016.
[2] Subhrananda Goswami, “Performance comparison of routing protocols of MANET using NS2”, LAP LAMBERT Academic Publishing, pp 1-140, 2014.
[3] Jean-Michel D, P.D.Doncker, E.Zimanyi, “Multivariate Analysis of the Cross-Layer Interaction in Wireless Networks Simulations”, In the Proceedings of International Workshop on Wireless Ad-hoc Networks, IWWAN, pp 121-125, 2005.
[4] B.A.S.Roopa Devi, J.V.R.Murty, G.Narasimha, S.Pallam Setty, “Investigating the Impact of Black Hole Attack on AODV with Statistical Tool-ANOVA”, International Journal of Current Engineering and Technology”, Vol 5, No. 1, pp 97-103, 2015.
[5] C.Chigan, L.Li, Y.Ye, “Resource-aware Self-Adaptive Security Provisioning in Mobile Ad Hoc Networks”, Journal of IET Information Security. May, 2014.
[6] C.F.Alvarez, L.E.Palafox, L.Aguilar, “Link Disconnection Entropy Disorder in Mobile Adhoc Networks”, International Journal of Recent Research in Electrical and Electronics Engineering, Vol 2, Issue 3, pp 119-125, 2015.
[7] Yongda Lin, Fengjie Li, “An Integrated Testbed for Routing Performance of MANET”. In the proceedings of 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), China, pp 33-38 2017.
[8] Fahad Taha AL-Dhief , Naseer Sabri , M.S. Salim , S. Fouad , S. A. Aljunid, “MANET Routing Protocols Evaluation: AODV, DSR and DSDV Perspective”, In the proceedings of MATEC Web of Conferences 150, No 06024, pp 1-6, 2018.
[9] Umar Draz, Tariq Ali, Sana Yasin, Ahmad Shaf, “Evaluation Based Analysis of Packet Delivery Ratio for AODV and DSR under UDP and TCP Environment”, In the proceedings of 2018 International Conference on Computing mathematics and Engineering Technologies, Pakistan, 2018.
[10] Lubdha M. Bendale, Roshani. L. Jain, Gayatri D. Patil, “Study of Various Routing Protocols in Mobile Ad-Hoc Networks”, International Journal of Scientific Research in Network Security and Communication, Vol 6, Issue 1, pp 1-5, 2018.
[11] R. Kumari, P. Nand, “Performance Analysis for MANETs using certain realistic mobility models: NS-2”, International Journal of Scientific Research in Computer Science and Engineering, Vol 6, Issue 1, pp 70-77, 2018.

Ch.Suresh Babu., K.Gangadhara Rao, B.Basaveswara Rao, K.Chandan, "Identification of Important Performance Metrics of Different Routing Protocols in the context of Different Scenarios using ANOVA Test," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.316-322, 2019.

Forecasting financial time series have been regarded as one of the most challenging applications of modern time series forecasting. Thus, numerous models have been depicted to provide the investors with more precise predictions. In recent years, financial market dynamics forecasting has been a focus of economic research. In this paper, we propose a hybrid non-stationary time series model with artificial neural network (ANN) for forecasting financial time series. The proposed model is non-stationary in trend component with regressor, lagged variable and non-linear component. The proposed model can capture both linear and non-linear structures in the time series. Non-linear structure is capture by Fed-Forward Neural Networks (FNN). The working of the proposed model is examined for SPY and VOO stock prices. Forecast based on the proposed model performs better than existing models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) criterion.

ARIMA-ANN, ARIMA-GARCH, Trend, Hybrid and Accuracy

[1] C.H.Aladag, E.Egrioglu, C.Kadil, “Forecasting nonlinear time series with a hybrid methodology”, Applied Mathematics Letters,Vol.22, Issue.9, pp.1467-1470, 2009.
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D.K.Shetty, B.Ismail, "Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.323-326, 2019.

Segmentation role is inevitable in image processing for the extraction of the desired region of interest. This work proposes decision tree for the segmentation of liver from abdomen CT images. Prior to feature extraction and segmentation, feature extraction was performed by the median filter. The hybrid feature extraction comprising of GLCM and LBP is used and training phase comprises of 20 DICOM CT abdomen images. The morphological operations are performed in the post processing phase for the refinement of output. The algorithms are developed in Matlab 2010a and tested on real time abdomen CT images.

Decision tree; Segmentation; Classification, regression tree

[1] Kumar SN, Fred AL, Varghese PS. “An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images”. Journal of Intelligent Systems.
[2] P. Umorya, R. Singh, “A Comparative Based Review on Image Segmentation of Medical Image and its Technique”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, April 2017.
[3] T.SenthilSelvi, R.Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, International Journal of Scientific Research Computer Science and Engineering, Vol.6, Issue.2, pp.15-19, April 2018.
[4] Kumar SN, Fred AL, Kumar HA, Varghese PS. “Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images”. InRecent Findings in Intelligent Computing Techniques pp. 457-469, 2018.
[5] Deshmukh J, Bhosle U. “Image mining using association rule for medical image dataset”. Procedia Computer Science. Vol. 85, 117-124. 2016
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[7] Rao RB, Fung G, Krishnapuram B, Bi J, Dundar M, Raykar V, Yu S, Krishnan S, Zhou X, Krishnan A, Salganicoff M. “Mining medical images”. InProceedings of the Third Workshop on Data Mining Case Studies and Practice Prize, Fifteenth Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), Vol. 15, p. 16, 2009.
[8] Khan A, Ansari Z. “Soft Computing based Medical Image Mining: A Survey”. International Journal of Computer Trends and Technology (IJCTT). Vol. 27, Issue 2, 2015.
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[10] D. Sherlin, D. Murugan, “A Case Study on Brain Tumor Segmentation Using Content based Imaging”, Int. J. Sci. Res. in Network Security and Communication, Vol-6, Issue-3, June 2018.
[11] A.C. Motagi, V.S. Malemath, “Detection of Brain Tumor using Expectation Maximization (EM) and Watershed”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.76-80 , June 2018.
[12] Elsayed A, Coenen F, García-Fiñana M, Sluming V. Segmentation for Medical Image Mining: A Technical Report. The University of Liverpool, Liverpool L69 3BX, UK. 2008.
[13] Shukla VS, Vala JA. “Survey on image mining, its techniques and application”. International Journal of Computer Applications. Vol. 133, Issue 9. 2016.
[14] Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q. “Comparison of three data mining models for predicting diabetes or prediabetes by risk factors”. The Kaohsiung journal of medical sciences. Vol. 29, Issue 2, pp. 93-99, 2013.
[15] Salcedo-Bernal A, Villamil-Giraldo MP, Moreno-Barbosa AD. “Clinical Data Analysis: An opportunity to compare machine learning methods”. Procedia Computer Science. Vol. 100, pp.731-738. 2016
[16] Sayad AT, Halkarnikar PP. “Diagnosis of heart disease using neural network approach”. InProceedings of IRF International Conference, 13th April-2014, Pune, India, pp. 978-993 2014.
[17] Nosaka R, Ohkawa Y, Fukui K. “Feature extraction based on co-occurrence of adjacent local binary patterns”. InPacific-Rim Symposium on Image and Video Technology, Springer, Berlin, Heidelberg. pp. 82-91, 2011.
[18] Mohanaiah P, Sathyanarayana P, GuruKumar L. “Image texture feature extraction using GLCM approach”. International Journal of Scientific and Research Publications. Vol. 3 Issue 5, 2013.

T.R. Nisha Dayana, A. Lenin Fred, "Segmentation of Liver from Abdomen CT Images Using Classification and Regression Tree," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.327-332, 2019.

Yield of chilly is very important aspect for farmer, it is depend on the supplied water to plant and use of pesticide. The chilly plant is mostly infected by white fly, bacterial leaf spot, pepper mosaic virus. In this paper we use peeper mosaic virus infected leaves of chilly plant. We also use four different vegetation Indices and Support Vector Machine classification to classify between diseased and non-diseased leaf. Among four vegetation indices, we found NPCI is better indices in this study work.

NPCI, MCARI, NDVI, TCARI, SVM

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[16] Swati B. Magare, Dr.Ratnadeep R. Deshmukh “To Study the Impact of Glyphosate on Chlorophyll Content of Crops” International journal of innovative research in science, Engineering and technology. Vol. 5, Issue 3, March 2016
[17] Sushma D Guthe, Dr.Ratnadeep R. Deshmukh “ Prediction of Phosphorus Content in Different Plants: Comparison of PLSR and SVMR Methods” International Journal of Computer Technology and Research, Volume 6–Issue 8, 410-416, 2017.
[18] Anatoly A. Gitelson et al “Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves” Journal of plant Physiology, 271-282,2003.
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Akshay V. Kshirsagar, Ratnadeep R. Deshmukh, Pooja V. Janse, Rohit Gupta, Jaypalsing N. Kayte, "Detection of disease from Chilly Plant Using Vegetation Indices," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.333-337, 2019.

The web services run in a highly dynamic environment so, the most fundamental challenges in web services based software solutions is to manage QoS changes of their component web services at runtime. In order to make the composite web service adapt to these changes, a self adaptive system is proposed for web service composition. The distributed approach is followed at the client and the server side along with the runtime monitoring and adaptation of the component web services at the provider side. For the self adaptive systems to recover as quickly as possible, a way of performance prediction is proposed in this paper along with the case study and the performance of the system. The prototype is developed using Java, and the JADE platform is used for implementing software agents using hospital lookup case study. The experimental results show that the proposed solution has better performance for supporting self adaptive web service composition.

Self-adaptive systems; Web services; Quality of Service (QoS) and Web Service Composition

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Navinderjit Kaur Kahlon, "A Self-adaptive System reconfiguring a Composite Web Service for Emergency Medical Aid," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.338-343, 2019.

Twitter is a social media platform, a place where people from all parts of the world can make their opinions heard. Twitter produces around 500 million of tweets daily which amounts to about 8TB of data. The data generated in twitter can be very useful if analyzed as we can extract important information via opinion mining. Opinions about any news or launch of a product or a certain kind of trend can be observed well in twitter data. The main aim of sentiment analysis (or opinion mining) is to discover emotion, opinion, subjectivity and attitude from a natural text. In twitter sentiment analysis, we categorize tweets into positive and negative sentiment. Clustering is a protean procedure in which identically resembled objects are grouped together and form a pack or cluster. We conducted a study and found out that the use of clustering can quickly and efficiently distinguish tweets on the basis of their sentiment scores and can find weekly and strongly positive or negative tweets when clustered with results of different dictionaries. This paper implements the approach of clustering with respect to sentiment analysis and presents a way to find relationships between the tweets on the basis of polarity and subjectivity.

Opinion Mining, sentiment analysis, clustering, Twitter

[1] Peng, Zhichao, Qinghua Hu, and Jianwu Dang. ”Multi-kernel SVM based depression recognition using social media data.” International Journal of Machine Learning and Cybernetics (2017): 1-15.
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[5] Tripathy, Abinash, Abhishek Anand, and Santanu Kumar Rath” Document-level sentiment classification using hybrid machine learning approach.” Knowledge and Information Systems (2017):1-27.
[6] Yousefpour, Alireza, Roliana Ibrahim, and Haza Nuzly Abdel amed. “Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis” Expert Systems with Applications 75 (2017): 80-93.
[7] Hussain, Jamil, Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Afzal, Hafiz Farooq Ahmad, Oresti Banos, and Sungyoung Lee. ”SNS based predictive model for depression.” In International Conference on Smart Homes and Health Telematics, pp. 349-354. Springer, Cham,2015.
[8]R. Joshi and R. Tekchandani, "Comparative analysis of Twitter data using supervised classifiers," 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1-6. doi:10.1109/INVENTIVE.2016.7830089
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Ashwini Yerlekar, Devika Deshmukh, "Investigating Sentiment analysis using Clustering and NLP tools," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.344-347, 2019.

It is very challenging for a contractor to procure skilled/ semi-skilled manpower for the various construction activates. On other side labourers are going through lots of hardships. Labourers who work on daily or hourly wages in any construction work find it very difficult to get a job. Generally labourers gathering at a place like railway station or bus stop etc. every morning in search of jobs. If they have to spend their whole day without work then it will become difficult for their family to survive. There are many middlemen working between worker and contractor, they take commission from both parties workers and contractors. They offer jobs only to those workers who give them good commission and ignore others. Contractors also have to give commission to the middlemen and because of this labourers get lesser than the expected wages. This system will eliminate the middlemen agents and hence commission from the labourers. This System will also provide full wages to labourers by contractors which were previously cut by middlemen/agents. Instead of agents there will be placement agencies which will help the labourers offering jobs and these placement agencies will be paid by contractors depending upon the amount of work and number of labourers. This will gradually increase the jobs seeking for labourers and eliminate unemployment for labourers. Hence, our system will be helpful for contractors for finding labourers and also be helpful for needy labourers who work on wages.

Contractor, Labourer, Middleman, Agents, Job, Construction

[1] Jason Stuart Gorham, Willoughby Cir., Lake Worth, “INTEGRATED ONLINE JOB RECRUITMENT SYSTEM”, International Patent Classification, Patent Number US 7,653,567 B2, Patent Date Jan. 26, 2010 FL (US) 33463.
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[3] Stephane Lajoie, Alma (CA), “ONLINE RECRUITMENT SYSTEM AND METHOD”, International Patent Classification, Patent No. US 2012/01854.02 A1, Patent Date Jul.19, 2012, Vol. 1.
[4] Avinash S. Kapse, Vishal S. Patil, Nikhil V. Patil, “E- RECRUITMENT”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 1, Issue. 4, April 2012, ISSN: 2249 – 8958.
[5] Arik Filstein, HerZila (IL), “INTELLIGENT JOB RECRUITMENT SYSTEM AND METHOD”, United States Patent Application Publication Pub. No. US 2014/0214711 A1, Pub. Date: Jul 31, 2014, Vol. 1.
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[7] Rina Agarwala, “USING LEGAL EMPOWERMENT FOR LABOUR RIGHTS IN INDIA”, The Journal of Development Studies, Published online: 01 Apr 2018, Vol. 1.
[8] Vaishnavi C. Mankar; Jai R. Sharma; Kanchan K. Masal; Siddhved R. Phadke, “ONLINE JOB PORTAL”, International Journal of Research In Science & Engineering, Special Issue: Techno-Xtreme 16, Dept. of CSE, J.D.I.E.T, Yavatmal, India.
[9] Robert J. McGovern, Potomac, James A. Winchester JR., Andrew B. Evans, Alderson, Brian E. Farmer, Jennie A. Kofman, Aaron P. Walker, “COMPUTERIZED JOB SEARCH SYSTEM”, United States Patent Application Publication, Pub. No.: US 2002/0120532, Pub. Date: Aug. 29, 2002.

Mohammed Ahmed, Shamshad Iraqui, Masood Ansari, Abdul Qadir Ansari, "Smart Labourer – A Proposed System for locating Labourers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.348-352, 2019.

Wireless Sensor Networks (WSNs) are rapidly becoming popular due to they are low cost solutions for various real-world challenges. It is widely used in military surveillance, transportation management, health care, etc. Prolonging a wireless sensor network’s lifetime is closely related to energy consumption. Furthermore, secure data transfer is a great challenge for WSNs, especially for applications that use important data such as military applications. The main objective is not only to design a new routing protocol that ensures network lifetime efficiency, but the balance between these security threats and energy efficiency. For that reason, we have designed the standard framework and simplify the process of building a novel geographical routing sensor networks that introduce location privacy, data privacy and energy awareness routing. Firstly, the whole network is divided into multiple grids with randomly distributed sensor nodes and all sensor nodes only communicates with their neighbor grids. Each grid selects the grid head based on the maximum energy level and minimum distance of the destination node in each grid .The data transmitter node identifies the neighbor grid with the grid head having maximum energy level and minimum distance of the destination node among its neighbor grids for data transmission. Further Hybrid AES-DES algorithm is applied to provide data privacy and location privacy. The proposed protocol provides a good trade-off between balancing energy, security and routing efficiency, and in all cases the lifespan of sensory networks can be significantly expanded.

Wireless Sensor Networks, Geographic Routing, Location privacy preserving, Data privacy preserving, Energy aware, blind path approach

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Manjunath D R, Thimmaraju S N, "A Secure Blind Path Energy Aware Geographical Routing Protocol for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.353-360, 2019.

The Mining task produce the various examples of values from the bunch of information Visit value sets mining is a critical information mining assignment to find the covered up, fascinating example of things in the set of Data. At times uncommon values are more vital because it convey valuable data. Uncommon values seem as it were at the point when edge is set to low. Uncommon value sets are moreover critical in discovering relationship between inconsistently bought trade things, examination of various medical reports which help in decision making. Uncommon values extraction from the transactional data is the difficult task in nature. For the extraction of uncommon values from the large transactional data some critical issues happen like (i) Extraction of recognize intriguing uncommon examples. (ii) The most effective method to productively find them in large transactional data. This manuscript represents the effective technique for extraction uncommon values from the large changing in nature Transactional data.

Threshold, Profit, uncomman value sets, visit value set, candidate value set

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Surbhi Singh, Renu Jain, "Mining of Uncommon Value Sets From the Transactional Data Using Proposed Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.361-364, 2019.

Sentiment analysis is one of the main tasks of Natural Language Processing (NLP). This analysis had gained more attention in recent years. In this paper, we tackled the problem of sentiment polarity categorization as one of the fundamental problems of sentiment analysis. A general process is proposed with detailed descriptions. Data used are online product reviews collected from Amazon.com. Experiment for sentence-level categorization and review-level categorization are performed with best outcomes. Finally, we give insight into our future work on sentiment analysis.

Natural Language Processing(NLP), Sentiment Analysis, Sentence Level Categorization, Review Level Categorization

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S. Sabeena, "Detecting Fraud Reviews of Apps Using Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.365-368, 2019.