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Satellite images play an important role for capturing Earth's surface. Using satellite images land cover monitoring could be done through which the modification or changes on land surface could be identified. Comparison can be made on the basis of past satellite image analysis, which helps to identify the changes that are occurring or have already occurred. Although there exist many techniques for land cover monitoring, proper land cover identification and detection of changes on the land cover is still a challenge. In the recent years, machine learning techniques have been utilized in distinct areas of image analysis and resulted in positive outcomes. Hence, in this paper, four supervised machine learning algorithms (i.e., support vector machine [SVM]), neural network [NN], maximum likelihood [MLC], and parallelepiped [PP] algorithms) have been utilized for land cover identification and detecting the amount of changes that have occurred in the individual land cover classes.

Abstract: Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced. Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected. Objective: COVID-19 attacks people with less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So, they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images. Method: This paper introduces a new denoising technique for low-dose Covid-19 CT images using a convolution neural network (CNN) and noise-based thresholding method. The major concern of the methodology for reducing the risk associated with radiation while diagnosing. Results: The results are evaluated visually and using standard performance metrics. From comparative analysis, it was observed that proposed works give better outcomes. Conclusions: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in noise suppression and clinical edge preservation.

Road region and non-road region separation in the unstructured road intends to be an important task for safe navigation and collision avoidance for autonomous driving vehicles. The road that connects rural areas and cities to the national highways are considered as unstructured roads. Absence of clear lane marking on these unstructured roads makes them more prone to accidents in comparison to highways, which have clear lane marking for indication of road and non-road regions. However, the unstructured roads have different color information from its background that paves an easy way for design and development of an efficient road detection system for recognition and classification of road and non-road regions. Hence, in this paper, we propose an efficient road detection system for the classification of unstructured roads into road and non-road regions using multiple nearest neighbors (NN) classifier and soft voting aggregation approach. The proposed system utilized the chromatic information (i.e. *a,*b, and Hue) to train the NN classifiers and aggregated their output using soft voting (SV) approach for final output response. The output results of multiple classifiers were aggregated using SV approach based on the confidence score obtained by each individual classifier. The performance of the proposed system is evaluated in terms of precision, recall, accuracy, intersection over union (IOU), true positive rate (TPR), and processing time and compared with current state of art methods reported in the literature. The proposed system achieved precision, recall, accuracy, IOU, and TPR of 96.79%, 96.92%, 97.8%, 96.08% and 96%, respectively with the processing time three times smaller than those of the existing state of art methods. The experimental results demonstrate that the proposed system can provide an effective guidance to the autonomous vehicles through recognition and classification of road and non-road regions in rural, urban, and city areas, wherein single unstructured roads connect the national highways.

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field.

In data analysis, data scientists usually focus on the size of data instead of features selection. Owing to the extreme growth of internet resources data are growing exponentially with more features, which leads to big data dimensionality problems. The high volume of features contains much of redundant data, which may affect the feature classification in terms of accuracy. In the current scenario, feature selection attracts the research community to identify and to remove irrelevant features with more scalability and accuracy. To accommodate this, in this research study, we present a novel feature selection framework that is implemented on Hadoop and Apache Spark platform. In contrast, the proposed model also includes rough sets and differential evolution (DE) algorithm, where rough sets are used to find the minimum features, but rough sets do not consider the degree of overlying in the data. Therefore, DE algorithm is used to find the most optimal features. The proposed model is studied with Random Forest and Naive Bayes classifiers on five well-known data sets and compared with existing feature selection models presented in the literature. The results show that the proposed model performs well in terms of scalability and accuracy.

LBP and majority of its variants performs extremely well in front of moderatelight variations. But when light variations becomes severe then performanceof LBP and its variants is not satisfactory. Therefore there is a need of themore promising and impressive descriptor which performs well in harsh lightvariations. To complement these LBP based descriptors the proposed worklaunches the novel descriptor for Face Recognition (FR) in harsh lightningvariations. This proposed descriptor is called as Radial Orthogonal MedianLBP (ROM-LBP). The main demerit of these LBP based descriptors is thatthey all consider the uniform coordination between the neighbors and centerpixel. Which mean raw pixel intensity is used for the comparison with thecenter pixel. The proposed work eliminates this problem in the introduceddescriptor ROM-LBP, by replacing the raw pixels intensity with the medianof the radial points in each orthogonal position of the two separate groups.The generated median is then used for comparison with the center pixel.The respective codes obtained from both the groups are concatenated toform the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor,the services of FLDA is used and then classification was conducted by SVMs.Experiments conducted on EYB and YB datasets demonstrates the ability ofthe proposed ROM-LBP against various LBP and non-LBP based descriptors.

PurposeThis paper aims to use the concept of machine learning to enable people and machines to interact more certainly to extend and expand human expertise and cognition.Design/methodology/approachIntelligent code reuse recommendations based on code big data analysis, mining and learning can effectively improve the efficiency and quality of software reuse, including common code units in a specific field and common code units that are not related to the field.FindingsFocusing on the topic of context-based intelligent code reuse recommendation, this paper expounds the research work in two aspects mainly in practical applications of smart decision support and cognitive adaptive systems: code reuse recommendation based on template mining and code reuse recommendation based on deep learning.Originality/valueOn this basis, the future development direction of intelligent code reuse recommendation based on context has prospected.

Background: With today’s emerging world of technology, fusion is the key factor in every field, especially in the field of medical and remote sensing. By fusing two or more images, we get necessary useful data in a particular fused image. So it is the best technique to remove noise data from those images. Methods: Noise or Blurring are those unwanted data that affects the quality of an image in terms of clarity. There are numerous methods for image fusion that can be used to eliminate noise or blurring that gives best results in the form of the fused image. Conclusion: In this paper, diverse techniques have been reviewed, studied and compared to discover the limitations of multiple image fusion techniques.

ZeroAccess is a distributed Peer-to-Peer (P2P) botnet used by attackers for various monetary benefits—including click-fraud, bitcoin mining, pay-per-install and information theft. ZeroAccess infected tens of millions of computers. It creates the hidden file system for stolen credentials, and employ the root-kit techniques for covert communication. In this paper, we have analyzed a ZeroAccess malware binary to investigate the features and functionalities of the bot. We extracted the list of IPs the bot communicates and identified the ports and protocols used by the bot via its dynamic analysis. In addition to this, we found the IPs from various nations the bot communicates, and also provide more insight into the features and functionality of the malware. Further, we present future research directions.

This paper presents a new wavelet-based multi-focus image fusion approach using method noise and anisotropic diffusion for two separate cases, i.e., with and without a reference image. It is specifically designed for real-time surveillance applications. It is a multi-step image fusion approach. Firstly, stationary wavelet transform (SWT) is performed to get low and high-frequency coefficients. Secondly, the input images' LL bands are fused using average operation. The rest of the respective bands are fused using a new correlation coefficient (CC) based fusion strategy using the threshold value calculated by structural similarity index metric (SSIM). Then inverse SWT is performed to reconstruct the fused coefficients. Thirdly, anisotropic diffusion-based method noise thresholding is introduced to recover the unprocessed and still damaged input images' components. Finally, the proposed approach's performance has experimented with various qualitative (visual perception) and quantitative factors (performance metrics). The experimental outcomes show that the proposed approach generates fine edges, high visual quality, high clarity of objects, and less degradation. The proposed multi-step hybrid technique is implemented to generate high-quality fused images. The experimental outcomes verify the achievement of the proposed approach.

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling approaches are needed to improve the quality of SAR images. However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image. The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task. However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images. Each method has their own norms, advantages and disadvantages. This article contains a review of some major work in the field of SAR image despeckling. Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images. Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here. The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed.

This research paper introduces the novel local binary pattern (LBP) variant for face recognition (FR) called as neighborhood and center difference-based-LBP (NCDB-LBP). In NCDB-LBP, the 4 labeled function is proposed to capture the robust features from 3 × 3 pixel window. For each neighborhood position , 2 first-order derivatives are computed, first computed between the adjacent neighborhood and the current neighborhood and the second computed between the center pixel and the current neighborhood. Employing the proposed function between the 2 first-order derivatives (produced from each neighborhood position) eventually results in 4 labeled window. All 8 neighborhoods are then placed in the 1 × 8 pixel window from which the 4 different binary patterns are produced. This concept is performed in both anticlockwise (ac) and clockwise (c) directions, termed as NCDB-LBPac and NCDB-LBPc descriptors. After binary patterns are encoded for each pixel position, the 4 transformed images are produced from ac direction and 4 from the c direction. All the respective directional transformed images are then divided into 3 × 3 subregions for histogram extraction. The combined histograms from all the respective subregions are the entire feature size of the NCDB-LBPac and NCDB-LBPc descriptors. To reduce the feature size, PCA and FLDA are utilized. Finally, classification is performed by SVMs and NN. The proposed FR approach is tested on ORL, GT, JAFFE, Yale, YB and EYB databases. The proposed FR approach achieves encouraging results.

The purpose of the study is to find the best shells available in Linux Operating System. The study has been narrowed down to seven most prominent shells in use today. With so many different Linux distributions and a plethora of shells, there exists a problem of plenty, which is rather a good problem to have. Amid all this confusion, one wonders which shell to use. Which is most suitable to one’s requirement, is modern, up to date and provides all sort of features which fulfils the desire of the user as well as the developer. The article deals in brief about Linux and its distributions, its philosophy and the freedom it provides. Shells, its scripting and shell types have been explained, one gets an idea of the study from the introduction. The seven Linux shells have been explained in detail to familiarize the reader with the most popular and most used shells available. A clear, concise and comparative analysis of the shells based on their general, interactive, programming and security features has been given. Programming features are important for the developers while a good interface makes everyone happy. Security features are a must for anyone who values their privacy and would like no intrusion in their system or their files. The mechanism of communication is important for I/O handling, as well as managing, interrupts. The speed of the system is greatly affected by the level of efficiency of inter-process communication.

This article discusses a hybrid wavelet-based multi- focus image fusion technique using Principal Component Analysis (PCA). Previously many authors have used many wavelet transforms in a different way for effective image fusion purpose but in this article, the authors have used Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) to process the input images in parallel. Later their results are further merged/ fused using standard PCA method. The results of the proposed method are compared with some of the conventional and non-conventional works related to SWT, DWT, and PCA. The result shows a good result than the compared method’s result. The results are analyzed and assessed using visual appearance of the final fused image and also using some of the parameters like Standard Deviation (SD), Petrovics metric (PM), Average Pixel Intensity (API) and Average Gradient (AG).

Image Fusion is the methodology to combining multiple images resulting in a single image keeping all the necessary aspects and features of original image with extended information. In this paper, we perform the comparative study between Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT). We will discover various sectors where SWT performs better than DWT. Stationary Wavelet Transform (SWT) is primarily applied on the original image for obtaining the edge information of image, in Level 1 and Level 2 respectively. Next, both edge images are fused to get the finalised absolute edge image using spatial frequency measurement. Therefore, simple fusion techniques are used for comparing images.

Disease diagnosis is the most critical health-care function. If an illness is diagnosed before the normal or planned period it can save people's lives. Classification method of machine learning can be useful to help the medical branch by delivering reliable and instant disease diagnosis. Hence the convenient time for both physicians and patients because heart disease is one of the world's most ultra-hazardous and dangerous diseases today, due to the difficulty to diagnose the disease. Within this paper we include a review of the classification methods for machine learning and image fusion that have been demonstrated to help healthcare professionals identify heart disease. We begin with the machine learning brief and summarize descriptions of the mainly used classification techniques for diagnosing diseases of heart. Then, we review and demonstrate some work on the use of classification techniques for machine learning and image fusion in this area. It also provides an overview of the working algorithm, and provides a description of the current work.

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Over recent years, machine translation has achieved astounding accomplishments. Machine translation has become more evident with the need to understand the information available on the internet in different languages and due to the up-scaled exchange in international trade. The enhanced computing speed due to advancements in the hardware components and easy accessibility of the monolingual and bilingual data are the significant factors that have added up to boost the success of machine translation. This paper investigates the machine translation models developed so far to the current state-of-the-art providing a solid understanding of different architectures with the comparative evaluation and future directions for the translation task. Because hybrid models, neural machine translation, and statistical machine translation are the types of machine translation that are utilized the most frequently, it is essential to have an understanding of how each one functions. A comprehensive comprehension of the several approaches to machine translation would be made possible as a result of this. In order to understand the advantages and disadvantages of the various approaches, it is necessary to conduct an in-depth comparison of several models on a variety of benchmark datasets. The accuracy of translations from multiple models is compared using metrics such as the BLEU score, TER score, and METEOR score.

Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.