The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which can be quantified as a biomarker. The objective of the study reported in this paper is to develop robust EEG-based biomarkers for detecting AD in its early stages. We present a new approach to quantify the slowing of the EEG, one of the most consistent features at different stages of dementia, based on changes in the EEG amplitudes (ΔEEG A ). The new approach has sensitivity and specificity values of 100% and 88.88%, respectively, and outperformed the Lempel-Ziv Complexity (LZC) approach in discriminating between AD and normal subjects.
The phenomenon of poverty is one of the most important phenomena facing the world at large. Despite the tremendous technological progress witnessed by mankind and despite the unprecedented high levels of world economic production, poverty remains the greatest challenge facing the world. Statistics and studies have shown that poverty is caused by several problems: (health, social, economic, educational, etc.) These problems are obstacles to the ability to obtain employment opportunities, which leads in the beginning to the growth phenomenon of unemployment, and ultimately to the growth of poverty.
The results of a range of research in the field of psychology have confirmed that children from poor homes suffer from a high level of
... Show MoreIt is found in the book "Ibn Aqeel: Alfiya Ibn Malek" that there are some linqustical aspected are related to the native tribal speakers like Tamim or Tie or some others. Sometimes in the book he said "some Arabian said without mentioning the name of the tribe.
As weel, he hasn’t mentioned the accent but he does mention the language. In the book, he has brought back the most important and the biqqest Arabian tribes suchas tribes of Hegaz, Tamim, Hatheyal, son of Anber, Tie, Rabia Bin Wael, Bani Katham, Au there, Bani AL Harth, Bani Kalb, Bani Hgim, Zabid, Hamedan, Alia Qais, Bani Ameer and many others. However, the most mentioned tribes were Hegaz and Tami.
Hence, the importance of the book expiain Ibn Aqeel by mentioning these A
The objective of the present study is to determine the effect of Kaolin as a fuel oil additive to minimize the fireside corrosion of superheater boiler tubes of ASTM designation (A213-T22) by increasing the melting point of the formed slag on the outside tubes surface, through the formation of new compounds with protective properties to the metal surface. The study included measuring corrosion rates at different temperatures with and without additive use with various periods of time, through crucible test method and weight loss technique.
A mathematical model represents the relation between corrosion rate and the studied variables, is obtained using statistical regression analysis. Using this model,
... Show MoreSpraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...
The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di
... Show MoreIn this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.
Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural
... Show MoreThe recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med
... Show MoreThe use of credit cards for online purchases has significantly increased in recent years, but it has also led to an increase in fraudulent activities that cost businesses and consumers billions of dollars annually. Detecting fraudulent transactions is crucial for protecting customers and maintaining the financial system's integrity. However, the number of fraudulent transactions is less than legitimate transactions, which can result in a data imbalance that affects classification performance and bias in the model evaluation results. This paper focuses on processing imbalanced data by proposing a new weighted oversampling method, wADASMO, to generate minor-class data (i.e., fraudulent transactions). The proposed method is based on th
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