<p class="0abstract"> </p><p class="0abstract">This paper researches the evolution process of what is called two-factor authentication technique and its adaptation related to the educational system through the Internet. This technique is a measure of security employed, particularly in scopes which have valuable information like bank services. It witnesses developments so far as today, in parallel with the developments occurring in technology. Since this technique consists of two phases, the security is going to be developed. Today, bank services, devices using the Internet of things, tickets of public transportation and lots of other scopes are utilized. In the information field, the researchers and scientists always update the techniques of two-factor authentication to resist the attacks related to security. Last years, the researchers studied novel technologies like behavioral biometric or biometrics. The training through the Internet may become much more useful than going to someplace to study a specific course. Mostly, the participants in the trainings through the Internet get many certificates for success, participation, etc. The principal problem is how to certify the truthiness of the participant who desires to get the certification. In this paper, and by researching the techniques of two-factor authentication, the Mimic Control Method with Sound Intensity (MCMSI) is proposed to be used for the training through the Internet.</p>
Alzheimer's Disease (AD) is a degenerative disease of the brain that results in memory loss due to the death of brain cells. Alzheimer's disease is more common as people get older. Memory loss happens over time, and as a result, the person loses the ability to react appropriately to their surroundings. Microarray technology has emerged as a new trend in genetic research, with many researchers utilizing it to look at the changes in gene expression in particular organisms. Microarray experiments can be used in various ways in the medical field, including the prediction and detection of disease. Large amounts of unprocessed raw gene expression profiles sometimes contribute to computational and analytic difficulties, including selecting dataset features and classifying them into an appropriate group or class. The large dimensions, lesser sample size, and noise in gene expression data make it difficult to attain good Alzheimer classification accuracy using the entire collection of genes. The categorization process necessitates careful feature reduction. As a result, a comprehensive review of microarray Alzheimer's disease studies is presented in this paper, focusing on feature selection techniques.
<p class="0abstract"><strong>—</strong> Chest imaging diagnostics is crucial in the medical area due to many serious lung diseases like cancers and nodules and particularly with the current pandemic of Covid-19. Machine learning approaches yield prominent results toward the task of diagnosis. Recently, deep learning methods are utilized and recommended by many studies in this domain. The research aims to critically examine the newest lung disease detection procedures using deep learning algorithms that use X-ray and CT scan datasets. Here, the most recent studies in this area (2015-2021) have been reviewed and summarized to provide an overview of the most appropriate methods that should be used or developed in future works, what limitations should be considered, and at what level these techniques help physicians in identifying the disease with better accuracy. The lack of various standard datasets, the huge training set, the high dimensionality of data, and the independence of features have been the main limitations based on the literature. However, different architectures of deep learning are used by many researchers but, Convolutional Neural Networks (CNN) are still state-of-art techniques in dealing with image datasets.</p>
<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>
The improvement of different data-sharing technology has increasingly permeated many industries as technology continues to improve. As a result, for the value of the data to be realized, data sharing and security are essential. However, a fundamental data sharing mechanism is difficult to check for electronic data usage traces. Furthermore, data providers' unwillingness to provide their data is a challenge. Taking use of the dispersed ledger, smart contract, data trust, and traceability aspects of blockchain technology. This research presents a data-sharing model based on blockchain technology optimizing to overcome the challenges in terms of security and control, of conventional centralized data sharing and management, enabling safe access to the data as a result. Moreover, the research assesses the prototype's usefulness and security. Additionally, this paper suggests a method for using blockchain technology to optimize the efficiency of data sharing. This study showed that data sharing via the blockchain technology paradigm proposed in this work is feasible, secure, controllable, and efficient. This was demonstrated in a novel way employing blockchain technology.
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