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.
Spraying 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...
An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men
... Show MoreRecently, 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 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 MoreBackground: Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by bilateral stenosis starting at the supraclinoid internal carotid artery (ICA), with the development of a collateral network of vessels. It is an established cause of stroke in the pediatric age group. Despite its increasing prevalence in various parts of the world, it remains largely underrecognized in the Middle East, particularly in Iraq. This is the first case of MMD in an Iraqi patient undergoing surgery. Case description: A 12-year-old boy presents with a 3-months history of progressive behavioural changes. MRI revealed diffuse infarcts of different ages. MRA and CT angiography revealed extensive asymmetrical steno-occlusive changes of t
... Show MoreTo investigate the efficacy of polyether‐ether‐ketone (PEEK) wire as a fixed orthodontic retainer, by comparing its performance to other retainer wires and optimizing its adhesion to composite bonding materials.
Retainer wires of 15 mm segments were used, PEEK wires were prepared in cylindrical form with 0.8 mm diameter, and had two surface treatments namely air‐abrasion and conditioning with adhesive system. Three different metallic retainer wires were used for comparison and three tests were performed; two tests measured debonding force and associated wire deflec
One of the most essential components of asphalt pavements is the filler. It serves two purposes. First, this fine-grained material (diameter less than 0.075 mm) improves the cohesiveness of aggregate with bitumen. Second, produce a dense mixture by filling the voids between the particles. Aluminum dross (AD), which is a by-product of aluminum re-melting, is formed all over the world. This material causes damage to humans and the environment; stockpiling AD in landfills is not the best solution. This research studies the possibility of replacing part of the conventional filler with aluminum dross. Three percent of dross was used, 10, 20, and 30% by filler weight. The MarshallMix design method was adopted to obtain the op
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