In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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Deep Learning in Medical Image Analysis

Performance of deep learning vs machine learning in plant leaf disease detection

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.

Hybrid deep learning for detecting lung diseases from X-ray images

Finding human cognitive state is very useful for medical cares in our daily life. EEG eye state classification is a common time series problem for detecting human cognitive state. A cognitive state can be defined as the human thought processes and state of mind. This paper mainly focuses on the prediction and classification of EEG eye state dataset determining various parameters such as Mean absolute error, Relative absolute error, True positive rate, False positive etc. ROC area has also been graphically observed for various conditions. Six types of classifiers such as Naive Bayes multinomial, Logistic, PART, K nearest neighbor classifier, Decision table, and SVM have been applied in this work to represent a comparative analysis. Experimental result shows that Partial Decision Tree (PART) and K nearest neighbor classifier can correctly identify larger percentage of instances compared to the Naive Bayes multinomial, Logistic regression, SVM, Decision table. Weka Data mining tool has been used for simulation purpose.

EEG Eye State Prediction and Classification in order to Investigate Human Cognitive State

Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registration. Deep iterative registration is then described with emphasis on deep similarity-based and reinforcement learning-based registration. Moreover, the application areas of medical image registration are reviewed. This review focuses on monomodal and multimodal registration and associated imaging, for instance, X-ray, CT scan, ultrasound, and MRI. The existing challenges are highlighted in this review, where it is shown that a major challenge is the absence of a training dataset with known transformations. Finally, a discussion is provided on the promising future research areas in the field of DL-based medical image registration.

Deep Learning for Medical Image Registration: A Comprehensive Review

The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach’s benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.

https://downloads.hindawi.com/journals/scn/2022/8951961.pdf

Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

In lung cancer, tumor histology is a significant predictor of treatment response and prognosis. Although tissue samples for pathologist view are the most pertinent approach for histology classification, current advances in DL for medical image analysis point to the importance of radiologic data in further characterization of disease characteristics as well as risk stratification. Cancer is a complex global health problem that has seen an increase in death rates in recent years. Progress in cancer disease detection based on subset traits has enabled awareness of significant as well as exact disease diagnosis, thanks to the rapid flowering of high-throughput technology as well as numerous ML techniques that have emerged in recent years. As a result, advanced ML approaches that can successfully distinguish lung cancer patients from healthy people are of major importance. This paper proposed lung tumor detection based on histopathological image analysis using deep learning architectures. Here, the input image is taken as a histopathological image, and it has also been processed for removing noise, image resizing, and enhancing the image. Then the image features are extracted using Kernel PCA integrated with a convolutional neural network (KPCA-CNN), in which KPCA has been used in the feature extraction layer of CNN. The classification of extracted features has been put into effect using a Fast Deep Belief Neural Network (FDBNN). Finally, the classified output will give the tumorous cell and nontumorous cell of the lung from the input histopathological image. The experimental analysis has been carried out for various histopathological image datasets, and the obtained parameters are accuracy, precision, recall, and F-measure. Confusion matrix gives the actual class and predicted class of tumor in an input image. From the comparative analysis, the proposed technique obtains enhanced output in detecting the tumor once compared with an existing methodology for the various datasets.

https://downloads.hindawi.com/journals/cin/2022/3149406.pdf

Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources

Cloud Computing can be deliberated one of the revolutionary technology in the digital world in recent days. Cloud Computing has begun a new revolution not only technically but also economically in the computer based world. Employing cloud computing facilities has plentiful benefits such as the lessening of costs, enlargement of efficiency, central promotion of soft wares, limitless storage capacity, relaxed access and the independency of mentioned services from the hardware. But there are some problems and challenges in cloud computing. Fault tolerance can be mentioned as the one of the main restrictions. It becomes a critical requirement for attaining high performance in cloud computing. In this paper, various type of techniques of generating the capacity of Fault Tolerance has been pointed out. Subsequently, abstraction and algorithms of them are stated. The procedures of handling failure at any level has been described with flowchart in this paper. The fault tolerance architecture has been compared in terms of the type of the practical policies applied in the cloud computing system architecture, fault detection and fault recovery.

Subrato Bharati

Papers

EEG Eye State Prediction and Classification in order to Investigate Human Cognitive State

Deep Learning for Medical Image Registration: A Comprehensive Review

Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources

Fault Tolerance in Cloud Computing- An Algorithmic Approach