S. Rajkumar

Abstract: Objectives: The objective of this paper is to analyze the different image quality metrics by testing and comparing with different distorted set of satellite images. Methods/Statistical Analysis: In this paper, we propose the methods for analyzing the quality of real time images that are corrupted due to different distortions. The several quality metrics are applied and ultimately the best metrics are derived based on the type of degradation. Different metrics such as metric based on single image and metric based on two images have been tested with different real time satellite images from NASA data sets. Findings: This framework will help to identify the metrics in order to prove the proposed filtering schemes that are applied to the corrupted images. Based on the results, we have concluded the characteristics of different quality metrics and further we successfully identified the quality metric appropriate to various distortions. Application/Improvements: The proposed quality metric analysis is used to estimate the performance of any filtering schemes which are used to enhance the quality of any real time images such as remote sensing field.

Abstract: Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.

Abstract: ----------------------------------Abstract: Software-defined networking (SDN) is an architecture that aims to make networks fast and flexible. SDN's goal is to improve network control by enabling service providers as well as enterprises to respond quickly to changing business needs. In SDN, the administrator can shape traffic from a centralized control console without having to modify any of the individual switches belonging to the network. The SDN controller which is centralized directs the switches to deliver network services wherever they are needed, irrespective of the specific connections between a server and devices. This methodology is a shift from traditional network architecture, in which individual network devices make traffic decisions based on their configured routing tables. In this paper, I built and tested an SDN load balancer and firewall module using the Floodlight controller.

Abstract: Chatbot is used extensively to check the state of health at any time. It is the same as going to a doctor and having the medication prescribed. This paper discusses about medical chatbot using the machine learning algorithm which predicts the accuracy of the disease. There are many machine learning algorithms that can be used to predict the disease. Support Vector Machine learning technique is primarily used to achieve precise prediction and boost the efficiency of the model. The system uses Natural Language Processing to achieve the style of chatting. Using this approach people can reduce spending time in hospitals and receive low cost or cost-free services.

Abstract: Basically, the Synthetic Aperture Radar (SAR) images are often degraded due to three factors namely noise, blur and artifact. The noise is the undesirable fluctuation in a random portion of the image and is often detracts from the image. The blur will reduce the object visibility. According to the recent literatures the most dangerous effect which appear in real time images are artifacts. The shadowing effect is the best example to depict the image artifact. The presence of shadows mostly affects the vital information of an image. In the shadowing effect, the portion of the object is totally obscured or hidden from the image. In this paper, we focus the impact of image artifact such as shadow in real time images and we focus how to detect the shadowing effect. Further, this paper is devoted to removal of shadows from very high resolution (VHR) SAR images and aerial view Images.

Abstract: Over the past two decades, medical imaging has been extensively apply to diagnose diseases. Medical experts continue to have difficulties for diagnosing diseases with a single modality owing to a lack of information in this domain. Image fusion may be use to merge images of specific organs with diseases from a variety of medical imaging systems. Anatomical and physiological data may be included in multi-modality image fusion, making diagnosis simpler. It is a difficult challenge to find the best multimodal medical database with fusion quality evaluation for assessing recommended image fusion methods. As a result, this article provides a complete overview of multimodal medical image fusion methodologies, databases, and quality measurements.In this article, a compendious review of different medical imaging modalities and evaluation of related multimodal databases along with the statistical results is provided. The medical imaging modalities are organized based on radiation, visible-light imaging, microscopy, and multimodal imaging.The medical imaging acquisition is categorized into invasive or non-invasive techniques. The fusion techniques are classified into six main categories: frequency fusion, spatial fusion, decision-level fusion, deep learning, hybrid fusion, and sparse representation fusion. In addition, the associated diseases for each modality and fusion approach presented. The quality assessments fusion metrics are also encapsulated in this article.This survey provides a baseline guideline to medical experts in this technical domain that may combine preoperative, intraoperative, and postoperative imaging, Multi-sensor fusion for disease detection, etc. The advantages and drawbacks of the current literature are discussed, and future insights are provided accordingly.

Abstract: Internet users are largely threatened by abuse and manipulation of several automated chat service programs called as chat bots. Malware and spam is distributed by the popular chat networks using chat bots. The commercial chat network is surveyed in this paper with a series of measurements. A series of 15 advanced to simple chatbots are used for this purpose. When compared to the bot behavior, the complexity of human behavior is high. A classification system is proposed for accurate distinguishing between human user and chatbots based on the measurements obtained from the study. Naïve Bayes Classifier and entropy classifier are used for the purpose of classification. Chat bot detection is performed with improved efficiency and accuracy using these classifiers. The speed of Naïve Bayes Classifier and accuracy of entropy classifier compliments each other in the process of detection of chat bots. The improved efficiency of the proposed system is proved by testing and comparison with the existing schemes.

Abstract: Radiography is one of the important clinical adjuncts for preliminary disease investigation. The X-ray images are corrupted with inherent quantum noise affecting the performance of computer-aided diagnosis systems. This paper presents an extensive experimental review and impact of six benchmark filters for reducing noise and disease classification on chest X-ray images. The tradeoff between de-noising and texture preserving performance is investigated through classification performances using the state-of-the-art machine learning methods – Support Vector Machine and Artificial Neural Network. Moreover, the qualitative, subjective, and statistical evaluation is performed by using the image quality metrics, expert radiologist opinion, and statistical test, respectively. The experimental results confirm the significant improvement in classification performance using Guided filtered images. Furthermore, the results of qualitative measures and subjective analysis demonstrate that the guided filter and anisotropic diffusion filter both performed significantly better. Finally, a non-parametric statistical test is used to validate statistical significance of the obtained results.

Abstract: It is essential to secure the information to store or transfer medical digital files without destruction. Currently, all used e-health files requests to be utilized in well-controlled, protected, and dependable style avoiding breaches and hacking. This research focuses on medical confidentiality encrypting grayscale health images for comfortable safe utilization. The work depends on resilience randomization and XOR operations for its medical-image cryptography. It tests performance of some random generators conveying the best every time running that is dynamically changing depending on e-health image variations. The research tests several randomizations structures processed as two sequenced encryption methods adopting substitution and transposition. The work tested random variations to encrypt different medical grayscale images revealing attractive remarks. The paper investigation intends to recognize appropriate preference via secrecy testing typical notations. The work indicates this flexibility of best applicable PRNG and its change features interesting privacy intellectual medical gray-image security for open e-health research direction to benefit from.

Abstract: We present a novel technique to examine the consequence of cataract surgery. Iris is one of the best biometric due to its unique biological properties. It is stable throughout the life. Cataract surgery may cause changes in the iris structure. A cataract is an eye disease that causes the eye’s lens to become cloudy and opaque with decreased vision. Aging, obesity, family history, heavy drinking, high blood pressure, smoking, diabetes, myopia or sun exposure are the risk factors of cataract. This paper proposes an algorithm to improve the level of security or match the unmatched person due to structural and textural changes in irises. Feature vector results from gober are applied to SOM clustering approach. Our proposed method has been evaluated by a dataset composed of different irises of an individual before and after the surgery which are collected from various studies. Experimental results and performance comparisons have presented.