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An Orthogonal Polynomial Framework using 3 x 3 mathematical model has been proposed and attempted for the textureanalysis by L.Ganesan and P.Bhattacharyya during 1990. They proposed this frame work which was unified to address both edgeand texture detection. Subsequently, this work has been extended for different applications by them and by different authors overa period of time. Now the Orthogonal Polynomial Framework has been shown effective for larger grid size of (5 x 5) or (7 x 7) orhigher, to analyze textured surfaces. The image region (5 x 5) under consideration is evaluated to be textured or untextured usinga statistical approach. Once the image region is concluded to be textured, it is proposed to be described by a local descriptor,called pro5num, computed by a simple coding scheme on the individual pixels based on their computed significant variances. Thehistogram of all the pro5nums computed over the entire image, called pro5spectrum, is considered to be the global descriptor.The novelty of this scheme is that it can be used for discriminating the region under consideration is micro or macro texture,based on the range of values in the global descriptor. This method works fine for many standard texture images. The works usingthe proposed descriptors for many texture analysis problems with (5 x5) including higher grid size and applications are underprogress

In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of gaming and video streaming over the network require high availability with very small latency rate. 5G networks provides muchfaster services than 4G but link failure occurrence can affect the quality of service. In 5G networks environmentalfactors also affect the efficiency of wireless signals. To overcome such type of issues, base stations are placed indistributed manner around urban areaswhen decision is required for the placement. However, in some scenarioswe can have few similarities, as in general, highways are same. In existed systems signals distribution is performedhomogenously, so it will be generating issues like fractal and environmental. It will be cause of great economicloss. However, being an emerging network paradigm, Software defined Network (SDN) is easy to manage due tological separation of control plane and data plane. SDN supports numerous advantages, one of them is link robustness to avoid service unavailability. To cope with network link failures there are many mechanisms existedlike proactive and reactive mechanisms, but these mechanisms calculate multiple paths and stored in flow tableswithout considering reliability of link. Therefore, it will be cause of high latency rate due to calculation of manymultiple paths and increased traffic overhead too. To overcome these issues, we proposed a new approach in whichmultipath numbers depend on reliability of primary path. Number of alternative paths decreased as reliability oflink increased it leads to less time required to calculate alternative path. In addition, traffic overhead decreases ascompared to existing approaches. Secondly, we also integrate the shortest distance factor with reliability factor,and we get better results than existing approaches. Our proposed system will be helpful in increasing the availability of services in 5G network due to low latency rate and small traffic overhead required in link failure recovery

Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people’s opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper.

Due to the increased disease occurrence rates in humans, the need for the Automated Disease Diagnosis (ADD) systems is also raised. Most of the ADD systems are proposed to support the doctor during the screening and decision making process. This research aims at developing a Computer Aided Disease Diagnosis (CADD) scheme to categorize the brain tumour of 2D MRI slices into Glioblastoma/Glioma class with better accuracy. The main contribution of this research work is to develop a CADD system with Convolutional-Neural-Network (CNN) supported segmentation and classification. The proposed CADD framework consist of the following phases; (i) Image collection and resizing, (ii) Automated tumour segmentation using VGG-UNet, (iv) Deep-feature extraction using VGG16 network, (v) Handcrafted feature extraction, (vi) Finest feature choice by firefly-algorithm, and (vii) Serial feature concatenation and binary classification. The merit of the executed CADD is confirmed using an investigation realized using the benchmark as well as clinically collected brain MRI slices. In this work, a binary classification with a 10-fold cross validation is implemented using well known classifiers and the results attained with the SVM-Cubic (accuracy >98%) is superior. This result confirms that the combination of CNN assisted segmentation and classification helps to achieve enhanced disease detection accuracy.

Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.

Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans The scheme operates in four steps Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans These images were retrieved from the Radiopaedia COVID-19 website The images were divided into training and test sets in a ratio of 70:30 Then, multiple features were extracted from the training data We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served to assess GA fitness Based on the ELM losses, the most discriminatory features were selected and saved as an ELM Model Test images were sent to the model, and the best-selected features compared to those of the trained model to allow final predictions Validation employed the collected chest CT scans The best predictive accuracy of the ELM classifier was 93 9%;the scheme was effective

Stock market forecasting is an important research area, especially for better business decision making Efficient stock predictions continue to be significant for business intelligence Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns However, major events’ news also contains significant information regarding market drivers An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market This research proposes an efficient model for stock market prediction The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction The LSTM has the advantage of analyzing relationship between time-series data through memory functions The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model © 2021 Tech Science Press All rights reserved

In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for efficient spectrum utilization. Thus, we propose a cloud based intelligent system for remote COVID-19 screening using cognitive radio-based Internet of Things and deep learning. Specifically, a deep learning technique recognizes radiographic patterns in chest computed tomography (CT) scans. To this end, contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality. The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator. Then, a deep transfer learning model, VGG-16, is trained to diagnose a suspected CT scan as either COVID-19 positive or negative. Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy, sensitivity, and specificity. The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.