Speech Genres And Other Late Essays

1D metal-oxide nanostructures have attracted much attention because metal oxides are the most fascinating functional materials. The 1D morphologies can easily enhance the unique properties of the metal-oxide nanostructures, which make them suitable for a wide variety of applications, including gas sensors, electrochromic devices, light-emitting diodes, field emitters, supercapacitors, nanoelectronics, and nanogenerators. Therefore, much effort has been made to synthesize and characterize 1D metal-oxide nanostructures in the forms of nanorods, nanowires, nanotubes, nanobelts, etc. Various physical and chemical deposition techniques and growth mechanisms are exploited and developed to control the morphology, identical shape, uniform size, perfect crystalline structure, defects, and homogenous stoichiometry of the 1D metal-oxide nanostructures. Here a comprehensive review of recent developments in novel synthesis, exceptional characteristics, and prominent applications of one-dimensional nanostructures of tungsten oxides, molybdenum oxides, tantalum oxides, vanadium oxides, niobium oxides, titanium oxides, nickel oxides, zinc oxides, bismuth oxides, and tin oxides is provided.

One-Dimensional Metal-Oxide Nanostructures: Recent Developments in Synthesis, Characterization, and Applications

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

/pdf/an-efficient-deep-learning-approach-to-pneumonia-44jrkv2wrx.pdf

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.

The proliferation of current wireless communications and information technologies have been altering humans lifestyle and social interactions—the next frontier is the smart home environments or spaces. A smart home consists of low capacity devices (e.g., sensors) and wireless networks, and therefore, all working together as a secure system that needs an adequate level of security. This paper introduces lightweight and secure session key establishment scheme for smart home environments. To establish trust among the network, every sensor and control unit uses a short authentication token and establishes a secure session key. The proposed scheme provides important security attributes including prevention of various popular attacks, such as denial-of-service and eavesdropping attacks. The preliminary evaluation and feasibility tests are demonstrated by the proof-of-concept implementation. In addition, the proposed scheme attains both computation efficiency and communication efficiency as compared with other schemes from the literature.

/pdf/lightweight-and-secure-session-key-establishment-scheme-in-5cphwhnuq1.pdf

Lightweight and Secure Session-Key Establishment Scheme in Smart Home Environments

Cloud computing is combination of various computing entities, globally separated, but electronically connected. As the geography of computation is moving towards corporate server rooms, it bring more issues including security, such as virtualization security, distributed computing, application security, identity management, access control and authentication. However, strong user authentication is the paramount requirement for cloud computing that restrict illegal access of cloud server. In this regard, this paper proposes a strong user authentication framework for cloud computing, where user legitimacy is strongly verified before enter into the cloud. The proposed framework provides identity management, mutual authentication, session key establishment between the users and the cloud server. A user can change his/her password, whenever demanded. Furthermore, security analysis realizes the feasibility of the proposed framework for cloud computing and achieves efficiency.

A Strong User Authentication Framework for Cloud Computing

Smart meters (SMs) are considered as foundational part of the smart metering infrastructure (SMI) in smart energy networks (SENs). SM is a digital device that makes use of two-way communication between consumer and utility to exchange, manage and control energy consumptions within a home. However, despite all the features, an SM raises several security-related concerns. For instance, how to exchange data between the legal entities (e.g., SM and utility server) while maintaining privacy of the consumer. To address these concerns, authentication and key agreement in SMI can provide important security properties that not only to maintain a trust between the legitimate entities but also to satisfy other security services. This work presents a lightweight authentication and key agreement that enables trust, anonymity, integrity and adequate security in the domain of SEN. The proposed scheme employs hybrid cryptography to facilitate mutual trust (authentication), dynamic session key, integrity, and anonymity. We justify the feasibility of the proposed scheme with a test-bed using 802.15.4 based device (i.e., SM). Moreover, through the security and performance analysis, we show that the proposed scheme is more effective and energy efficient compared to the previous schemes.

/pdf/lightweight-authentication-and-key-agreement-for-smart-2jskpuedtx.pdf

Lightweight Authentication and Key Agreement for Smart Metering in Smart Energy Networks

In the current era, blockchain based system getting a lot of attention across different industries especially in the domain of supply chain because of its streamlined nature at the same time keep the privacy intact as well as more efficient than the conventional system. However the traditional supply chain faced lot of challenges these days because of the complex networks. Blockchain integrated with IoT framework has the potential to overcome the challenges faced by the conventional systems across industries. Basically the IoT integrated blockchain system provides enough benefits such as connecting the link between the flows in the supply chain to improve the efficiency of the supply chain network, makes the system transparent that can reduce the code of conduct violation throughout the supply chain networks, the immutable properties helps the stakeholders to trace the products throughout the networks. Even though blockchain based system provides enough benefits across industries, so far the implementation rate is very low because of the lack of knowledge about the benefits of blockchain system as well as lack of understanding about how and why it is needed in their fields. In this paper we will highlight the difference between the conventional supply chain as well as blockchain based supply chain and the benefits of implementing blockchain in different sectors such as automotive, pharmaceutical, food industry, retail. This article will highlights the problems currently facing by the above mentioned industries by using the conventional supply chain as well as the solutions to the problems provide the blockchain based systems in those industries. This articles helps the people across sectors to understand the benefits of the blockchain based system in their respect fields as well as implement it to improve the efficiency of the overall system.

Mangal Sain

Papers

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.

Lightweight and Secure Session-Key Establishment Scheme in Smart Home Environments

A Strong User Authentication Framework for Cloud Computing

Lightweight Authentication and Key Agreement for Smart Metering in Smart Energy Networks

A Review on Benefits of IoT Integrated Blockchain based Supply Chain Management Implementations across Different Sectors with Case Study