Classification of Sleep Apnea using Multi Scale Entropy on Electrocardiogram Signal

One of sleep-disordered breathing (SDB) form is sleep apnea, commonly known as snoring during sleep, based on various complex mechanisms and predisposing factors. Sleep apnea is also related to various medical problems. It impacts morbidity and mortality so that it becomes a burden on public health services. Its detection needs to be done correctly through electrocardiogram signals to detect sleep apnea more quickly and precisely. This study was conducted to detect sleep apnea based on electrocardiogram signals using multi-scale entropy analysis. Multi-scale entropy (MSE) is used in a finite length of time series for measuring the complexity of the signal. MSE can be applied to both physical and physiological data sets and. In this paper we used MSE to detect Sleep Apnea on electrocardiogram (ECG) signals. MSE was applied two classes of ECG data, normal ECG signals, and apnea ECG signals. In this paper, classification and verification were carried out using the Support Vector Machine (SVM) and N-fold cross-validation (N-fold CV). From the experimental results, the highest accuracy was 85.6% using 5-fold CV and MSE scale of 10. The result shows that the system model that can detect sleep using the multi-scale entropy method

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

Image Compression Using Neural Networks: A Review

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.

Sensitive Data Exposure: Data Forwarding and Storage on Cloud Environment

Sensitive data has become an essential part of life today. With the increase in sensitive data, the importance of maintaining its confidentiality and integrity has increased. One of the solutions became to store this data in the cloud. But the risk of revealing this data still exists. This is because the rate of attack, leakage and loss of this data has become a serious matter. The importance of sensitive data in our current era is considered our oil, as it is very important in several uses in statistical analyzes and other important matters that help the authorities to know the type of people and their interests, and when publishing this information it is important to know what information should be available and What information should not appear or be used on the sites. In this paper, discuss this issue, which is one of the most important security issues that is sensitive data exposure. We touched on this research and the techniques used to reduce these risks to the data stored in the cloud. Mention the types of sensitive data and the types of attacks that may affect these data, and mention the points of weakness, and then the methods of protecting this data.

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

EduGram: Education Development Based on Hologram Technology

Education is one of the most important areas of life that affect the development and progress of societies, and the usage of images and visual representation methods is of real value in the educating process. Over time, different simple methods were used to display the information visually, which mostly are considered weak methods that may not perform its full purpose and the information may be transferred slowly and in an incomplete manner. Technology has contributed from the beginning of its emergence in the development of education and improve its output, and one of the most prominent contributions made by technology is the developments in the field of displaying information visually using different technologies, as the three-dimensional displaying technologies that are considered as an advanced solution which provides people with a more comprehensive view and facilitates the task of transferring information to learners and so improving the educating process. Many technologies are used to create and display the 3D visual content, and two of the most important 3D display technologies are Augmented Reality technology and Hologram technology, they both insert a three-dimensional image to the real world, but there are many differences between the two technologies in many aspects. In the first part of this research, and after defining and comparing the two technologies and the effect of each one on education, Hologram technology showed features that enabled it to be a suitable option to be used in education for displaying 3D educational content. This research then introduces the method for implementing the usage of Hologram technology in education as a 3D educational content displaying tool, introducing an implementation model by first transferring a sample of a 2D educational image to Holograms and using the Hologram fan projector to display it to the students. The results of a simple questionnaire on a number of people showed the effectiveness of using Holograms instead of the traditional 2D content found in school curricula, and a good level of people's acceptance to use this technology.

Justification of using Computed Tomography and Magnetic Resonance Imaging for Deep Venous Thrombosis and Pulmonary Embolism

Deep vein thrombosis (DVT) is a medical condition, occurs when a blood clot forms in a deep vein and pulmonary embolism (PE) occurs when a blood clot gets lodged in an artery in the lung, affecting blood flow to part of the lung.The frequencies of using computed tomography (CT) and magnetic resonance imaging (MRI) to diagnose deep venous thrombosis and pulmonary embolism is increasing day by day.Both the technics are noninvasive and provide prompt results. But there are a good number of alternative technics for the same purposes. That is why, till now scholars and respective professionals are interested to know more about the justification and comparative effectiveness of CT and MRI in detecting DVT and PE.This review aimed to analyze the history of several detecting methods for DVT and PE and to dig out the clear concepts about the effectiveness and patient compliances of CT and MRI in detecting deep venous thrombosis and pulmonary embolism. For proper analysis a lot of research as well as meta-analysis had been studied.From this article besides scholars and professionals, general readers will get a clear concept about the features, effectiveness and justifications of CT and MRI in treating DVT and PE.

Remote Laboratory, Based on Raspberry Pi, to Facilitate Scientific Experimentation for Secondary School Students

The pandemic caused by COVID 19 forced education systems to offer their services remotely due to social distancing policies. This article discusses research results on the development of remote laboratory architectures to deliver scientific experimentation in the area of physics for secondary school students using desktop computers or mobile devices.The design of the remote laboratory is based on the Raspberry Pi device, using various sensors and a graphical interface through which access and communication is given.The purpose of the development of this remote laboratory is to provide teachers and students of secondary education access to the development of remote activities for scientific experimentation in physics courses, using low-cost devices and free software.

Detection of Cancer in Medical Images using Deep Learning

Cancer is a serious disease that causes death by genomic disorder combination and diversity of unreasoning changes. This paper study the major deep learning techniques that are addressing medical image analysis and summarizes over 200 contributed articles to the subject, in particular those studies that are published in the last 6 years (since 2016). The main purpose of this paper study is to survey the deep learning algorithms for cancer detection and diagnosis. the results show that the convolutional neural network (CNN) is the most