ITM University, Gurgaon, Haryana

About: ITM University, Gurgaon, Haryana is a education organization based out in Gurgaon, India. It is known for research contribution in the topics: Encryption & Cryptosystem. The organization has 749 authors who have published 1159 publications receiving 12997 citations.

Enhancing the quality of satellite images using fuzzy inference system

Abstract: This Satellite Images are used nowadays in multiple applications like astronomy, weather forecasting, Space Sciences and earth and other planet observation. Generally Satellite Images are very noisy and blurry. In this paper we are presenting different techniques for satellite image enhancement. For removal of noise and blur different instinctive procedure suggested in this paper that pre-processes poorly concentrated or degraded satellite images by composing numerous consecutive autonomous processing phases which overturn noise, enrich contrast & upturn the clearness of the image and it also lessens the effect of blur and noise. Contrast improvement is being aided by the universal data content of an input image by enlarging the dynamic variety of intensity levels, utilized by Conversion functions. Certain conversion functions utilize local substantial content for modifying image details, such as quality & boundaries. In this paper, an effective method for improving the image quality, is introduced, in which a plotting utility, blend of universal and local conversion functions, is utilized which preserves the intensity and fine facts of the input image in addition. Contrast widening and Image intensity is preserved completely by universal conversion function. Fuzzy based enhancement is also applied to enhance the image. Sharpening filters are used in order to highlight fine details within an image. They are based on first and second order derivatives. Finally output of each stage is compared by using PSNR parameters.

Effects of SiC, Al2O3, and ZrO2 particles on the LBMed characteristics of Al/SiC, Al/Al2O3, and Al/ZrO2 MMCs prepared by stir casting process

Abstract: The effects of SiC, Al2O3, and ZrO2 particles on the characteristics of Al/SiC, Al/Al2O3, and Al/ZrO2 metal matrix composites (MMCs) have been studied in the present research work. The comp...

Diagnosis of Breast Cancer and Diabetes using Hybrid Feature Selection Method

Abstract: Diagnosis of diseases at an early stage is a crucial task in the medical field. A hybrid machine learning framework is presented for the diagnosis of breast cancer and diabetes using efficient feature selection and classification technique. This research identifies significant risk factors related to both chronic disease datasets by applying different feature selection techniques and hybridization of ReliefF Feature Ranking with Principal Component Analysis (PCA) method. To evaluate the effectiveness of the presented feature selection method, k-nearest neighbor method for classification is used. The hybridization enhances the accuracy of the classifier with the proposed feature selection technique for both chronic disease datasets. The performance of the presented hybrid framework is found to be best in comparison to five other techniques - Correlation Based feature Selection (CBS), Fast Correlation Based Feature Selection (FCBF), Mutual Information Based Feature Selection (MIFS), MODTree Filtering Approach and ReliefF Feature Selection. Moreover, the proposed ReliefF-PCA method eliminates 25% and 33.3% of irrelevant features for diabetes and breast cancer dataset respectively.

Multiple user pair scheduling in TWR assisted FSO systems

Abstract: In this paper, various multiuser scheduling schemes are applied to a two-way relay (TWR) assisted multiple user pair free-space optical (FSO) communication network, when the optical beam is subjected to path loss, misalignment errors, and atmospheric turbulence. The FSO network consists of a single, half-duplex, decode-and-forward, two-way optical relay that serves multiple user pairs by employing a scheduler. Three types of schedulers based on absolute signal-to-noise ratio (SNR), normalized SNR, and selective multiuser diversity are considered. Further, the atmospheric turbulence is modeled by a recently proposed statistical model, namely the Malaga M-distribution. For the presented system and channel models, closed-form and asymptotic expressions for outage probability and bit error rate are derived. The mathematical analysis is accompanied by Monte Carlo simulations and several numerical examples to illustrate the effect of the key system parameters.

Sentiment Analysis Using Lexicon Based Approach

Abstract: With increasing easy access to internet there is an emergence of e-commerce and social media portals. There is a huge surge in the production of the human sentiments in form of customer reviews and feedback on these platforms. As per a survey approximately 2,500,000 Terabytes of data is created every day and 90% of the data which exist today has been created in the past 2 years only. This huge amount of data created is called big data. The main problem associated with this huge data is that it is in unstructured form. So, to gain information, first we must process it using various methods. This paper proposes a Human sentiment analysis model (HSAM), which can perform sentiment analysis on any given data set.