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Automate Clinical decision support system(CRS) provide assistance to physician as well as to society to enhance quality of healthcare. Methodical and apposite testing a of automate reporting system prior to liberate to end-users is kind of critical aspect any automate expert system related to healthcare domain. Testing and validation, is one of the most vital and critical step of CRS because lack of well defined testing tools , oversight this step may lead to dangerous and severe outcome issues. Great efforts are required for testing of system as data collecting form number of resources and may be in different formats. Clinic data available in Electronic Health Records (EHR) form. Testing of such huge amount of clinical data by human became to tedious and risky because chances of mistakes are there. Adaption rate of clinical reporting system quite slow, as many of them not tested properly prior to liberate .Testing and Validation of CRS depends on various factors that considered in this paper. For testing technique, considered functional and structural techniques by receiving information for input from every level of progress.

Testing, Clinical reporting System, Evaluation factors, EHR

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Meenakshi Sharma, Himanshu Aggarwal, "Evaluation factors for testing and validation of Clinical Reporting System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.264-268, 2018.

website improvement is a deliberately procedure to take a web archive in top indexed lists of an internet searcher. Search engine optimisation (SEO) is a procedure of enhancing the conspicuousness of a site. This work depicts the changes of taking the page on top position in Google by expanding the Page rank which may bring about the enhanced perceivability and productive arrangement for an association. Google is most easy to use internet searcher demonstrated for the Indian clients which give client arranged outcomes more-over, the majority of other web indexes utilize Google look designs so we have focused on it. Along these lines, if a page is improved in Google it is streamlined for the greater part of the web search tools.

Search engine optimisation, SEO, Google optimisation, On page optimisation, Off page optimisation, Image optimisation, URL structure optimisation, Web Security, Web Application

[1] Neshat, Hamed Sadeghi: Ranking of New Sponsored Online Ads. (IEEE), 2011.
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Shreekishan Jewliya, Vikram Singh Rathore, "Analysis of Search Engine Optimization (SEO) Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.269-274, 2018.

In this paper design of continuous mode Hybrid Stand alone power station with battery and Diesel generator is modelled. The controller is designed to maximize the usage of energy sources and reduces the use of diesel generator. The state of battery charging is maintained. This paper firstly focuses on design of hybrid power station is to full fill load demand. Secondly a relay based controller is designed to select the best optimal power output as per availability of sources to minimize the use of Diesel generator and Battery operation. A supervisor control modes are further discussed for hybrid controller as per the load demand.

Diesel Generator(DG), Hybrid Controller,Supervisory control, Wind turbine Generator(WTG)

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[4] Phd. Thesis. Riso National Laboratory, Roskilde. University of Denmark, June 2000.
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[7] Meinhardt, M and Cramer, G. "Past, Present and Future of grid connected Photovoltaic and Hybrid PowerSystems". IEEE Power Engineering Society Summer Meeting. Vol.2, pp. 1283 -1288, July 2000.

Arpan Dwivedi, Yogesh Pahariya, "Design of Continuous Mode Hybrid Standalone Power Station with Hybrid Controller to Select the Best Optimal Power Flow," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.275-277, 2018.

Segmenting a broad class of histological structures is a necessary to identify the presence of cancer, to clarify spatial relation between histological structures in the tumor environments, making precise medicine studies easy, and provide an exploratory tool for pathologists. Histological structure determination helps explain spatial tumor biology and adds an advantage for health care organizations. Role focuses on the segmentation of histological structures present in colored images with stains (H & E) of the breast tissue. Accurate segmentation of histological structures can help build a spatial interaction map identifying the relations between the pixels to serve as an exploratory tool for pathologists. Graph theory based methods proposed based on spatial color statistics and neighborhood of nuclei statistics as well as designed a new region-based score for evaluating segmentation algorithms. In the first method, pair wise pixel color statistics measures in an H&E optimized color space built to enhance the separation between hematoxylin and eosin stains. The first method is expected to be successful in segmenting structures with well-defined boundaries (e.g., adipose tissues, blood vessels).The second method is designed to segment large amorphous histological structures (e.g., tumor nests), the spatial statistics of inter-nuclei distances is considered. Working with expertly annotated breast H&E images, this paper demonstrated the ability of proposed algorithms to identify significant histological structures, and thus enable the understanding of their spatial relationships, and perhaps infer the status of the disease.

histopathalogical image analysis, image segmentation, image statistics

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S.B. Pawar, V. Gaikwad, "A Survey on Advanced Methods for Segmentation of Structures in H & E Stained Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.278-282, 2018.

Diabetes Disease is one of most common disease in our modern life, and in this paper we are using different Super vised and un Super vised Machine learning Algorithms to Analyze and optimize accuracy of Training Data and classify , diagnosis , accuracy of Algorithms with python Machine learning modules like pandas, sklearn, Seaborn.

python, Machine Learning, Pandas, Seaborn, Sklearn, Diabetes

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M. Samba Siva Rao, M.Yaswanth, K. Raghavendra swamy, "Optimization of Diabetes Training DATA using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.283-286, 2018.

Cloud Computing is considered as one of the emerging technologies. Resources are allocated to users for a period of time and payment is made accordingly. Demand based preferential resource allocation is proposed which is based on the demand-supply scenario in the market. Preference based resource allocation technique allocates resources to the users based on the auction mechanism that follows market driven strategy where bid price of resources reflects the current demand. Users are discovered based on the amount capabilities. Payment is calculated based on the service preference chosen by the user. This preference is set by the Cloud Service Provider. Chosen preference decides the actual payment criteria of the winner. Time duration is divided into n time slots in order to give chance to unselected users to bid for resources in next time slot.

Cloud Computing, Resource Allocation, Auction, Payment, Preference

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Allocation for spot markets in cloud computing environments” Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on IEEE, 2011.

M.R. Dave, H.B. Patel, B. Shrimali, "Preference Based Resource Allocation In Cloud Data Center," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.287-292, 2018.

It has now been a well-known fact, as a result of extensive research, that energy obtained through solar radiation is intermittent in nature with nearly unpredicted fluctuations in output power. By the virtue of such irregularities, the stability and power quality of Photo Voltaic based power system operation are affected. The present work is aimed to achieve the PV system stability by using battery and ultra-capacitors to support a stand-alone PV power system. Certain loads like induction motor or DC motor draws large current at the time of starting resulting in deep-discharging of the battery as a consequence of which the battery undergoes accelerated aging. Using ultra-capacitors in conjunction with the battery takes care of the sudden initial power demand of the dynamic systems. In the present work a practical approach has been presented to derive the benefit of using ultra-capacitor in conjunction with battery to store energy obtained by solar PV system. Furthermore, the performance of the ultra-capacitor and battery hybrid energy storage scheme is tested and verified by applying different loads on the system. The experimental setup consisted of a 24V, 6.5Ah, lead acid battery bank unit; 3.33F, 24.3V, ultra-capacitor bank unit; a 70W, 32V, PV Panel; dc-dc converter unit; inverter unit, controller unit and load. A comparison has been done between input and output voltages and current waveforms of the inverter with and without ultra-capacitor bank.

Solar Photo Voltaic (SPV), Battery Energy Storage (BES), Ultra-Capacitor (UC), Hybrid Energy System

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H. S. Thakur, R. N. Patel, "Assessment of Performance of Integrated Solar PV System with Hybrid Energy Storage System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.293-302, 2018.

A traffic-aware routing in VANET is a prime step in transmitting the long data for applications. Researchers’ address that the traditionally used routing protocols employed in Mobile Ad Hoc Networks are not suitable for routing in VANET, as VANETs differ from MANETs in the mobility model and environment. The demand to develop a traffic-aware protocol in VANET initiated to propose a routing protocol, termed as Adaptive Autoregressive Whale Optimization algorithm (Adaptive-ARW). The main goal of the proposed algorithm is to select the optimal path for performing routing in VANETs, for which the traffic required to be predicted. For predicting the traffic in the road segment, Exponential Weighed Moving Average (EWMA) is employed that predicts the traffic based on the average vehicle speed and the average traffic density. The minimum values of average speed and vehicles average traffic density to the less traffic density. Using the predicted traffic, the routing paths are generated, and the optimal paths are selected using the proposed algorithm that exhibits adaptive property. The analysis of the proposed algorithm provides the End-to-End delay, distance, average traffic density, and throughput of 2.938, 2.08, 0.0095, and 0.1354, respectively.

Exponential Weighed Moving Average (EWMA), End-to-End Delay (EED), Whale Optimization algorithm (WOA), Autoregressive Model, Adaptive property

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Deepak Rewadkar, Dharmpal Doye, "Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.303-312, 2018.

In most of the clustering algorithms, the number of clusters has to be supplied in as an input. In CURE clustering algorithm also, the same problem exists. In this paper, we try to find the optimal cluster number in the CURE clustering algorithm by calculating an optimality measure corresponding to each cluster number produced by CURE clustering algorithm after it enters a range ,based on the intra cluster measure and the inter cluster measure of the clusters. The clustering along with the optimality check continues as long the optimality measure is increasing and the cluster number doesn’t fall below the minimum boundary of our range.

Algorithm, Clustering, CURE, Measure

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Khumukcham Robindro, Bisheshwar Khumukcham, Ksh. Nilakanta Singh, "Determination of Optimal Number of Clusters in Cure Using Representative Points," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.313-320, 2018.

In the modern computer era, the greatest importance is given to the individuals to secure and verify. Among all other Biometric, Iris recognition is one of the best methods to provide distinctive verification for each person based on the structure of the iris. Support Vector Machines (SVMs) are generally known as an efficient supervised learning model for taxonomy problems. The success of an SVM classifier depends on its parameters as well as the structure of the data. In this paper, we present the various uses of SVM based iris classifications.

Support Vector Machines (SVMs), parameters, iris classifications, verification

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R. Subha, M. Pushpa Rani, "SVM based Iris Classification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.321-323, 2018.