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Secured Communication is essential due to scalability due to increase number of devices and drastically growing number of people involved in communication. In this paper a voice comparison based communication authentication mechanism is used for providing secured communication. This voice based authentication is used in two different applications like people communication and data retrieval. Before going to speak with people in online their information and their voice is compared and verified from the database and permission will be granted. Similarly according to the voice they can retrieve the data from the data base, where it provides data integrity. Both applications comprise a number of stages such as: (i) Voice, Voice to Text input, (II). Voice Comparison and Pattern Matching. Finally (III). Permission Granted and Data Retrieval (DR) as the output. In order to improve the accuracy and relevancy the proposed data retrieval system, it uses an indexing method called Bag of Words (BOW). BOW is like an index-table which can be referred to store, compare and retrieve the information speedily and accurately. Index-table utilization in DRS improves the accuracy with minimized computational complexity. The proposed DRS is simulated in DOTNET software and the results are compared with the existing system results in order to evaluate the performance.

Information Retrieval System, Data Mining, Bag of Words, Data Base Maintenance.

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Kadher Farook, Manikandan.S, Shakila Basheer, Albert Irudaya Raj, "An Efficient Voice Based Person Identification System for Secured Communication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.58-60, 2018.

Network on Chip (NoC) is an interconnection network, which provides a network architecture to overcome limitations of System on Chip(SoC). The Interconnection among multiple cores on a chip has a major effect on communication and performance of the chip in terms of latency and throughput. Many routing architectures and routing algorithm have been developed to alleviate traffic congestion, performance enhancement and low power consumption for Network on Chip(N0C). In this paper, Diagonal Mesh NoC architecture is described using Network Simulator (NS2), which reduces the load distribution across the network by reducing the diameter of the network, as a result, routing cost also reduces. In Diagonal Mesh topology, routing estimates the alternative routes with priority given to a diagonal path. In this paper, it is seen that latency reduces for a pair of nodes that uses the diagonal path as compared to another pair of nodes. Throughput is also enhanced by this approach.

Network on Chip (NoC), Topology, Latency, Throughput, NS2

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[3]. Ms. Neha N. Patil and Prof. S. P. Patil” Performance Evaluation of Topologies Using NS-2” International Journal of Advanced Research in science and Management and Technology Volume 2, Issue 4, April 2016.
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[11]. Sudhanshu Choudhary, Shafi Qureshi “Performance Evaluation of Mesh –based NoCs: Implementation of a New Architecture and Routing Algorithm” International Journal of Automation and Computing.9(4), August 2012, pp.403-413, 2012.

P.P. Papalkar, M.A. Gaikwad, "Performance of Diagonal Mesh Network on Chip using NS2," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.67-71, 2018.

This paper is a comparative analysis of different machine learning algorithms used to detect fraud claims of Automobile/vehicle Insurance claims dataset. In this paper large dataset of automobile insurance claims is used and three feature selection algorithms are applied to the dataset which will be used by the classification algorithms to detect the fraud claims. The Feature Selection algorithms used in this paper are Tree-Based Feature Selection Algorithm, L1-Based Feature Selection Algorithm and Univariate Feature Selection Algorithm and the classification algorithms are Random Forest (RF), Naive Bayes(NB), K-Nearest Neighbor(KNN) and Decision Tree(DT). These algorithms are compared on the basis of performance measures such as accuracy, precision, recall. The proposed model shows that Random Forest works well with respect to accuracy and precision and Decision Tree is the best with respect to recall.

Automobile Insurance,Machinelearning, Feature Selection Algorithms, Classification Algorithms

[1] Yaqi Li, Chun Yan, Wei Liu, Maozhen Li, “A Principle Component Analysis-based Random Forest with the Potential Nearest Neighbor Method for Automobile Insurance Fraud Identification”. Applied Soft Computing Journalhttp://dx.doi.org/10.1016/j.asoc.2017.07.027
[2] G G Sundarkumar, Ravi V, “A novel hybrid under sampling method for mining unbalanced data sets in banking and insurance” [J]. Engineering Applications of Artificial Intelligence, 2015: 368-377.
[3] Maozhen Li, Yaqi Li, Chun Yan, Wei Liu,. “Research and Application of Random Forest Model in Mining Automobile Insurance Fraud”. 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) 2016.
[4] Rekha Bhowmik, “Detecting Auto Insurance Fraud by Data Mining Techniques”, Journal of Emerging Trends in Computing and Information Sciences, Volume 2 No.4, APRIL 2011
[5] H.Lookman Sithic, T.Balasubramanian, " Survey of Insurance Fraud Detection Using Data Mining Techniques" International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-3, February 2013
[6] Adrian Gepp, J. Holton Wilson, Kuldeep Kumar and Sukanto Bhattacharya, "A Comparative analysis of Decision Trees and other computational datamining techniques in automotive insurance fraud detection", Journal of Data Science 10(2012)
[7] Clifton Phua, Damminda Alahakoon, and Vincent Lee, " Minority Report in Fraud Detection: Classification of Skewed Data", ACM SIGKDD Explorations 2014.
[8] Tina R. Patil, Mrs. S. S. Sherekar, " Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification ", International Journal Of Computer Science And Applications Vol. 6, No.2, Apr 2013.
[9] Manoel Fernando Alonso Gadi, Xidi Wang and Alair Pereira do Lago, "Credit Card Fraud Detection with Artificial Immune System "
[10] LijiaGuo,"Applying Data Mining Techniques in Property~Casualty Insurance "
[11] M. Shukla, A. K. Malviya, "Analysis and Comparison of Classification Algorithms for Student Placement Prediction", International Journal of Computer Sciences and Engineering Vol.-6, Issue-6, June 2018.

Sapna Panigrahi, Bhakti Palkar, "Comparative Analysis on Classification Algorithms of Auto-Insurance Fraud Detection based on Feature Selection Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.72-77, 2018.

Software metrics plays a major role in assessing the quality of software in testing process. Software metrics elucidates the complexity, reusability, maintainability and understandability of the software code. Software complexity metrics are one of the emerging types of software metrics that focuses on the cognitive analysis of software in terms of understandability and maintainability. In other words, it can be rephrased as the effort taken to comprehend the program code for future enhancements. Complexity metrics has a direct impact with the analysis of complexity in software through an intrinsic study on the object oriented features. This paper proposes a novel Coupling Complexity Metric (IMFC), to highlight the complexity that incurs with coupling and weighs the complexity of a class.

Coupling, CBO, reusability, maintainability, modifiability

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N. Vijayaraj, T.N. Ravi, "An Advanced Coupling Complexity Metric for Evaluating the Quality of OO Software Modules," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.78-82, 2018.

Several precautions should be taken in using pharmaceutical drugs, for both healthcare professionals, who prescribe and administer drugs, and for drug consumers. Factors such as interactions among the prescribed drugs, interactions with the patient’s current medication, side effects to be avoided, and contraindications, need to be carefully considered. Additionally, the presence of some drug properties, such as side effects and effectiveness, depends on characteristics of patients, such as age, gender, lifestyles, and genetic profiles. The goal is to provide a system to assist medical professionals and drug consumers in choosing and finding drugs that suit their needs. And develop an approach that allows querying for drugs that satisfy a set of conditions. The approach allows users to specify side effects and tailors the answers based on user specification. Finally utilize drug data from multiple data sources. However, drug data are usually noisy and incomplete as they are either manually curated or automatically extracted from text resources such as drug labels. To cope with incomplete and noisy data, data mining techniques were designed and implemented which include clustering and classification algorithms. Then the developed system was used for comparative analysis of supervised and semi-supervised learning using performance metrics. The result shows that Semi-supervised method provided 40% improved response time in comparison with Supervised method in Drug Retrieval System.

Drug query system, Data mining, Clustering, Classification, Semi-supervised learning

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V. Jagadeesan, K. Palanivel, "Comparative Study of Supervised and Semi-Supervised Learning for Enhanced Drug Prediction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.83-89, 2018.

An algorithm have been undergone growth which make able most good selection into line looking for between 2 order, one is question sequence and another is man giving food, room and so on order looking for homologues has become a regularly order operation of biological orders on a 32 bit person computer. The high degree of matching position on man giving food, room and so on order and question subsequence is to make out quickly and clearly a group of order that is given question order be part of to An Algorithm is presented that takes more chances of the high degree of homology between such orders to make an into line of the matching regions. It does not have need of knowledge of a starting homology band, neither complex memory square measure, even for orders of several kilo bases, and it can overcome complex opening, nothing in between or different bands. This is a small, able to be taken about, effecting on one another, front end road map person one is going be married to be used to get out the fields, ranges of matching between man giving food, room and so on order and question subsequence. Since this road map have been written originally in the C language, it is possible to run in other PCs with small changes. The road maps run on the IBM personal knowledge processing machine, has need of 512k, without addition of hardware expect for thin, flat, round plate drives and printer. The execution is quite tightly, all the operations are does in less than one of seconds, depending on the needed work and on the order measure end to end. The going round in circles RNA smallest units of potato spindle under earth stem viroid was used as a question order & normal plant such as plant with soft red yellow fruit plant used as a man giving food, room and so on order input facts for making clear by reasoning the operation of the road map. The facts in these records may be used in many applications in future. The knowledge base is ready either on magnetic tape, on hanging down, not stiff diskettes, or online form. The main try to discover the highly diseased special field, range of disease and keep from opening this field, range to keep safe from viroid interaction keyword homology.

Homology

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A.K.Samanta, Syed Mahamud Hossein, Susanta Mandal, Shankar Kumar Roy, Apurba Mondal, "An algorithm for Construction of Infectious Viroid," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.90-96, 2018.

The huge amount of data which can be stored and processed with the use of the internet has resulted in the capacity of data centers get bigger. This has resulted in the increase of the consumption of the power in data centers significantly and the management of power consumption in data centers has become essential. There are many techniques that have been proposed in that prospective. This paper highlights the need of managing the power in data centers, in order to reach the target of the efficiency of the power and observe several architectural techniques designed for its management of power. The detail of techniques is also given based on their behavior. The purpose of this paper is to provide intuition into the techniques to improve energy efficiency of data centers by using self-organizing architecture proposed and also to request the designers to continue producing solutions which can help for managing the dissipation of the power of data centers.

Self-Organizing, virtualization, server consolidation, power consumption

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Sebagenzi Jason, Suchithra. R, "Self-Organizing Architecture In Data Centers For Power Management," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.97-104, 2018.

In spite of the commendable success of Big Data, there are still many challenges with regard to storage, retrieval, analysis and prediction. A framework was proposed for information retrieval by integrating ontology models with Big Data. Evaluation of this framework with different domain models is a invariable work to validate this model. This study applies the framework to health care data and thus identifies the strengths and weaknesses of the framework.

MongoDB, Ontology

[1] Fr. Thaddeus, C. DharmaDevi, S. Deepa, “A Framework for ontology-driven big data applications”, IJAER, Vol.10 (No.79), ISSN 0973-4562, 2015.
[2] Karl Seguin, “The Little MongoDB for 2.6”.
[3] Chieh Ming Wu, “Comparisons between MongoDB and MS-SQL Databases on the TWC Website”.
[4] Natalya F.Noy and Deborah L. McGuinness, “Ontology Development”.
[5] Fr. Thaddeus, C. DharmaDevi, “Ontology Bridge for Information Retrieval”, in the Proceedings of International Conference on Computing Paradigms, 2016.
[6] Furkh Zeshan and Radziah Mohamada, “Medical Ontology in the Dynamic Healthcare Environment”, in Elsevier Ltd, 1877-0508, 2012.
[7] Sreekanth R, Golajapu Venu Madhava Rao, Srinivas Nanduri, “Big Data Electronic Health Records Data Management and Analysis on Cloud with MongoDB: A NoSQL Database”, in IJAGT, 2309-4893, 2015.
[8] Manju K.K, Srinitya G, “Analysis and Prognosis of Cancer with Big Data Analytics”, in IJRASET, 2321-9653, 2016.
[9] Muthulakshmi P and Udhayapriya S, “A Survey on Big Data Issues and Challenges” in IJCSC, Vol.6 (Issue 6), pp.1238-1244, 2018.
[10] Rabie A. Ramadan, “Big Data Tools-An Overview” in IJCSE, Vol.2 (125), 2456-4451, 2017.

C. Dharma Devi, S. Thaddeus, "Information Retrieval Mechanism for Dynamic Health Care," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.105-107, 2018.

The presence of nonlinear loads in the distribution system results in poor power quality parameters such as low total harmonic distortion (THD), poor distortion power factor and produces nearby communication interference. Shunt active power filters (SAF) are used for improving the power quality in high power distribution systems. The Shunt active filter controller has the low frequency voltage control loop for regulating the DC voltage of SAF capacitor and the faster acting current control loop for realizing the compensation current. Conventionally, the current and voltage controllers are realized by distinct hysteresis and PI controllers respectively. This paper discusses the finite control set Model Predictive Control (FCS- MPC) that realizes the voltage and current control of SAF. The reference current of SAF is calculated from the instantaneous PQ theory. The phase locked loop (PLL) is adopted for generating the reference value of compensation currents. A discrete time mathematical model of the SAF is presented and the design steps of FCS- MPC are explained. The control objectives such as compensation current error minimization and DC link voltage regulation are defined in cost functions. During each sampling interval, the controlled variables such as SAF current and DC voltage of the capacitor are predicted by the mathematical model. The predicted variables are assessed by the cost function minimization and the switching state that provides minimum cost function is selected and applied to the SAF. The performance of the FCS-MPC strategy for the current control of SAF is validated for sinusoidal and non-sinusoidal distribution system voltages in MATLAB-SIMULINK simulations.

Active filter, Controller, Model predictive, Power quality, THD

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[7] Nunna Sushma, “ Comparative analysis of STATCOM and SVC for Reactive power enhancement in a long transmission line”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1579-1581, 2018.
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[16] Jose Rodriguez, Marian P. Kazmierkowski, Jos´e R. Espinoza, Haitham Abu-Rub, H´ector A. Young, and Christian A. Rojas, “State of the Art of Finite Control Set Model Predictive Control in Power Electronics”, IEEE Transactions on Industrial Informatics, Vol.9, No.2, pp.1003–1016, 2013.

S. Kumaresan, H. Habeebullah Sait, "Model Predictive Control of Shunt Active Filter for Power Quality Improvement in Distribution systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.108-115, 2018.

Intrusion Detection systems (IDS) play an important role in network security and protection. Intrusion detection system uses either misuse or anomaly based techniques to identify malicious activities. To detect malicious activity, misuse detection systems is used to identify signatures or previously known malicious activities. On the other hand, anomaly based systems is used to identify unknown attacks. Intrusion detection system is now an essential tool to protect the networks by monitoring inbound and outbound activities and identifying suspicious patterns that may indicate a system attack. In recent years, some researchers have employed data mining techniques for developing IDS. In this paper, hybrid Particle Swarm Optimization (PSO) and Fuzzy c-means clustering for network Intrusion Detection is proposed to identify intrusion over NSL-KDD dataset. An attempt has been made to cluster the dataset into normal and the major attack categories i.e. DoS, R2L, U2R and Probe. The experimental results demonstrate the efficiency of the proposed approach.

IDS, Fuzzy c-means Algorithm, PSO, Mutual Information, NSL-KDD Dataset

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[5] Manmohan Dagar and Rashmi Popli (2018), “Honeypots: Virtual Network Intrusion Monitoring System”, IJSRNSC, Vol.6, Issue 2, pp.45-49
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Partha Sarathi Bhattacharjee, Arif Iqbal Mozumder, Shahin Ara Begum, "Hybrid Particle Swarm Optimization and Fuzzy C-Means Clustering for Network Intrusion Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.116-122, 2018.