A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

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.

https://irojournals.com/iroismac/V3/I1/04.pdf

Naive Bayes and Entropy based Analysis and Classification of Humans and Chat Bots

Analysis of Quantum Noise-Reducing Filters on Chest X-ray Images: A Review

Securing information became essential to exchange multimedia information safely. The exchanged data need to be transformed in a well-managed, secure, and reliable manner. In this paper, we will focus on securing RGB images via cryptography during transmission among users using our effective proposal of utilizing appropriate Pseudo Random Number Generator (PRNG). We implement many techniques of PRNG involved in two consecutive crypto-processes of substitution and transposition to present secure image transformation. Our technique proposal of PRNGs selection is based on testing to encrypt RGB images to be compared with current related used approaches. The work experimentation aims to identify suitability and reliability through security measures standard parameters. The research justifies its proper PRNG selection to model our approach as attractive effective work worth remarking.

Remodeling randomness prioritization to boost-up security of RGB image encryption

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

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.

A Comparative Analysis on Image Quality Assessment for Real Time Satellite Images

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.

Building a Medical Chatbot using Support Vector Machine Learning Algorithm

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.

https://www.beei.org/index.php/EEI/article/download/680/938

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

----------------------------------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.

Implementing Software Defined Load Balancer and Firewall

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.

S. Rajkumar

Papers

A Comparative Analysis on Image Quality Assessment for Real Time Satellite Images

Building a Medical Chatbot using Support Vector Machine Learning Algorithm

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Implementing Software Defined Load Balancer and Firewall

Impact of Image Artifact and Solution to the Image Quality Issues in Real Time SAR Images