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This research work explains a unique Multimodal Medical Image Fusion Technique (MIFT) in light-weight of MIFT-HDWRT, Non-subsampled Contourlet Transform (NSCT), and Pulse Coupled Neural Network (PCNN). The MIFT-DWNRT plot tells the benefits of each of the NSCT and PCNN to amass higher combination. The supply medical pictures are first decayed by NSCT. The low-recurrence sub bands (LFSs) are tangled utilizing the MIFT-HDWRT run the show. For melding the high-recurrence sub bands (HFSs), a NSCT-PCNN show is employed. Altered abstraction frequency (MSF) in NSCT area is contributed to propel the PCNN, and coefficients in NSCT space with expansive terminating times are chosen as coefficients of the tangled image. At long last, opposite of NSCT i.e. INSCT is connected to urge the tangled image. Abstract and target examination of the outcomes and correlations with leading edge MIF technique demonstrate the effectiveness of the MIFT-DWNRT plot in melding multimodal therapeutic picture.

Image fusion; Pulse-Coupled Neural Network; Multi scale Geometric Analysis; Medical Imaging, NSCT

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Manvi, Ashish Oberoi, "Multimodal Image Fusion Technique MIFT-DWNRT for Improvement of Diagnosis Abilities," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.360-366, 2019.

Recently, the idea of Internet of Things is seeking much attention because of its vast potential in the area of wireless transmission. IoT interlinks plentiful of heterogeneous, geographically diversified devices which usually have scarcity of resources, and therefore rely on cloud for the same. Unfortunately, the Cloud enabled IoT suffer from various limitations say, high network latency with the increase in volume of data processed. Due to the above limitation, majority of latency sensitive applications tend to provide poor performance. To alleviate this problem, the concept of Fog Computing came into the picture, in which Fog level is introduced as an intermediary level between Cloud and IoT devices. IoT unlocks handful opportunities for various sophisticated applications such as home applications, wearable devices etc. and also allow sharing of data over internet. This data being shared contains large amount of sensitive information that should not be shared amongst the connected users. In this paper, firstly brief overview about fog computing is provided and subsequently various security issues pertaining to both Fog and IoT environment are defined.

Internet of Things (IoT), Fog Computing, Security, Privacy

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Richa Verma, Shalini Chandra, "Security and Privacy Issues in Fog driven IoT Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.367-370, 2019.

The main purpose of this article is to compare the practice of five methods used to convert 2D images into 3D images. The 2D to 3D conversion technique plays an important role in 3DTV development and promotion as it supplies high quality 3D writing equipment. This article analyzes five methods and compares their results to the best ways to create high-quality 3D images. The first method to convert 2D images to 3D based on the depth information map with edge information. The second method uses information for a map of depth based on merger. The third method generates 3D images with random action algorithms. The fourth method creates 3D images using a combination of motion, edge detection, and image breakout, depth estimation, and relocation algorithms. Finally, the fifth method generates 3D images based on the deep nanoscale method. Many performance metrics are used to analyze the performance of these approaches. This file uses PSNR, SSIM, MSE and RMSE for operational analysis. Experimental results suggest that random way works better than the other two ways.

2D-to-3D conversion, depth boundaries, depthmap,nonlocal neighbors, nonlocal random walks

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K.A. Mohamed Riyazudeen, M. Mohamed Sathik, "Comparative Study on 2D to 3D Medical Image Conversion Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.371-379, 2019.

incurrent era of big data, thedata analytics has become more challenging issue. Data get mined for finding facts as well as to predict impact of various activities which is used everywhere in the life. Mining processes like classification and clustering becomes more crucial in case of dynamic data streams. As the nature of data stream is temporal there is always a difference in the concepts which causes a concept drift. This concept drift affects the reliability of classifiers and clustering methods. Classification is important technique in data mining, which has been applied with various modifications to handle concept drift issue. Data stream classification is different from normal classification process as it has restriction of time, memory size and speed of processing along with accuracy. This article presents a review of remarkable recent ensemble based classifiers, which are designed to detect concept drift based on the different way of data stream processing.

Ensemble Learning, Data Stream Mining, Concept Drift

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R. C. Samant, D. M. Thakore, "A Rigorous Review on an Ensemble Based Data Stream Drift Classification Methods," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.380-385, 2019.

Complementary information is provided in Medical images like PET, MRI, and CT. To make the correct diagnosis these images are fused and are providing additional information for clinical analysis. This paper proposes a new medical image fusion based on the combined effect of Discrete Wavelet Transfrom (DWT), and Discrete Ripplet Transform (DRT). The images are transformed at the start into multi-resolution image using 2-level DWT. The resultant images are transformed again using DRT. Applying the common and most fusion rule and inverse DRT, the united coefficients of the approximation image is obtained by applying inverse DWT to the united coefficients. The performance of the united image is evaluated using metrics like PSNR, Entropy, Standard Deviation, and Structural Similarity Index measure and it outperforms the opposite existing ways.

Medical image fusion; Discrete Wavelet Transform; Discrete Ripplet Transform; Multiscale geometric analysis

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Manvi, Ashish Oberoi, "Multimodal Image Fusion Technique MIFT-HDWRT for Improvement of Diagnosis Abilities," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.386-391, 2019.

Medical image fusion has been used to derive useful information from multimodal medical image data. Multimodal image fusion is to integrate images from different modalities (like MRI with PET, CT with PET, and MRI with CT) to enhance the contrast of an image, and amount of data in an image. In this present work, two-level discrete wavelet-based image fusion has been chosen. The two-level discrete wavelet-based image fusion is compared both subjectively and objectively by using suitable quality metrics with the other image fusion techniques. On the basis of experimental results, it shows that the two-level discrete wavelet-based image fusion shows better quality of an image as compared to other techniques of image fusion.

multimodal image fusion, wavelet-based image fusion, pixel-based image fusion

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Manvi, Ashish Oberoi, "Implementation and Performance Analysis of Pixel based and Wavelet based Image Fusion," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.392-399, 2019.

Increase in number of vehicles on Indian Highways as well as city road conditions lead to Traffic congestion and Road accidents which represent a serious social-economical problem. The majority of the accidents can be prevented if the driver uses relevant data about the road conditions of highways by using mobile technology. To relieve the danger plus seriousness of the route automobile accident. Many different innovative security applications could be recognized as a result of mobile interaction among the vehicles travelling close by each other. .Therefore in order to eliminated these problems non-dominated sorting differential evaluation dependent inter-cluster information aggregation have been suggested throughout this work.

Vanets, Routing Protocols, Proactive routing protocols, PBRP

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Anita, Sunil Kumar Gupta, Rajeev Kumar Bedi, "Design and Implementation of Non-Dominated Sorting Differential Algorithm Based Energy Efficient Protocol for VANETs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.400-406, 2019.

Data stream mining is very popular in recent years with advanced electronic devices generating continuous data streams. The performance of standard learning algorithms is been compromised with imbalance nature present in real-world data streams. In this paper we propose a novel algorithm dubbed as Incremental Fusion Sampling for Data Streams (IFSDS) which uses a unique over sampling and under sampling techniques to almost balance the data sets to minimize the effect of imbalance in the stream mining process. The experimental analysis is conducted on 10 data chunks of data streams with varied sizes and different imbalance ratios. The results suggest that the proposed IFSDS algorithm improves the knowledge discovery over benchmark algorithms like C4.5 and Hoeffding tree in terms of performance measures TN Rate, FP Rate, precision, and F-measure.

Knowledge Discovery, Data Streams, Imbalanced data, oversampling, under sampling, Increment Fusion Sampling for Data Streams (IFSDS)

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Anupama N, Sudarson Jena, V Ravi Sankar, "A Novel Approach Using Incremental Fusion Sampling for Data Stream Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.407-415, 2019.

In recent ten years, heart failure becomes the leading cause of death in whole world which is estimated by World Health Organization (WHO). Several types of heart diseases are expanding day by day because of way of life, genetic problem, blood pressure, cholesterol level, pulse rate etc. So the diagnose of disease plays important role for the prevention of heart related problems. Researchers received different methods to analyze it. These days the utilization of system innovation in the fields of medication zone, finding treatment of disease and patient activity has exceptionally expanded. The aim of this paper is to design a KNN based classification approach for prediction of the Heart failure which assists the doctors to identify disease easily. It is an intelligent classification approach because it provides accurate result. To accomplish the diagnosis process taken different risk factor, signs and symptoms from patients and experts. Classification approach consists of two algorithms such as KNN classification algorithm and Decision tree algorithm. The result of classification shows 86% accuracy by using n no. of neighbors in this approach.

Classification, KNN, Decision Tree, cross validation

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Gagandeep Kaur, Anshu Sharma, Anurag Sharma, "Heart Disease Prediction using KNN classification approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.416-420, 2019.

Grammar checking or proof reading is one of the major tool incorporated in almost every word processor software. Almost all the word processor software contains spell checker and grammar checker as an essential component. The function of the grammar checker is to check the grammatical mistakes in the text typed by the user. In this research article, authors have developed a grammar checking system for Hindi language using hybrid approach. All the components (Morphological analyzer, POS tagger, error detection system and error correction system) required for development of grammar checker have been developed from scratch. Some components like morph, POS tagger and error detection systems have been developed using statistical approach and grammar correction system has been developed using rule based approach. Hence overall hybrid approach has been used for development of complete Hindi grammar checker. The system is tested for four different types of errors (Adjective noun agreement errors in terms of number and gender, Noun Verb agreement errors in terms of number and gender) and on testing, the system shows an overall precision of 0.83, Recall as 0.91 and F-measure as 0.87.

Hindi Grammar checker, Hindi POS tagger, Hindi morph, HMM, Hybrid approach

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M. Mittal, S K Sharma, A Sethi, "Detection and Correction of Grammatical Errors in Hindi Language Using Hybrid Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.421-426, 2019.