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Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
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Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).

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Publication Date
Sat Feb 01 2020
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Robust Estimation For Location Parameter
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 In this paper, we introduce three robust fuzzy estimators of a location parameter based on Buckley’s approach, in the presence of outliers. These estimates were compared using the variance of fuzzy numbers criterion, all these estimates were best of Buckley’s estimate. of these, the fuzzy median was the best in the case of small and medium sample size, and in large sample size, the fuzzy trimmed mean was the best.

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Publication Date
Sun Jun 01 2014
Journal Name
Ibn Al-haitham Jour. For Pure & Appl. Sci.
Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis
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Publication Date
Thu Jul 01 2021
Journal Name
University Of Northampton Pue
Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep
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Publication Date
Thu Apr 13 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis
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 This paper argues the accuracy of behavior based detection systems, in which the Application Programming Interfaces (API) calls are analyzed and monitored. The work identifies the problems that affecting the accuracy of such detection models. The work was extracted (4744) API call through analyzing. The new approach provides an accurate discriminator and can reveal malicious API in PE malware up to 83.2%. Results of this work evaluated with Discriminant Analysis

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Publication Date
Sat Nov 30 2024
Journal Name
Iraqi Journal Of Science
Utilizing the Gravity Method to Detect the Potential Effect of the Salt Dome in the Nahr Omar Oilfield, Southern Iraq
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Geophysics is one of the branches of Earth sciences and deals with studying the Earth's interior by studying the variation of physical properties within rock layers. Applied geophysics depends on procedures that involve the measurements of potential fields, such as the gravitational method. One of the significant oil fields in southern Iraq is represented by the Nahr Omar structure. A power spectrum analysis (SPA) technique was used to collect gravity data within the chosen oil field area in order to confirm the salt dome in the subsurface layers. The analysis of SPA resulted from six surfaces representing the gravity variation values of the depths (m)14300, 3780, 3290, 2170, 810, and 93.5. Gravity surfaces have been converted to de

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Publication Date
Tue Jan 01 2019
Journal Name
Malaysian Journal Of Biochemistry And Molecular Biology
Efficiency of primer design tools in evaluation of two molecular methods to detect two single nucleotide polymorphisms related with atherosclerosis
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Publication Date
Fri Oct 01 2021
Journal Name
Saudi Pharmaceutical Journal
Organization factors influencing nurse ability to prevent and detect adverse drug events in public hospitals using a patient safety model
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Publication Date
Sat Oct 20 2018
Journal Name
Journal Of Economics And Administrative Sciences
Calculate the Average Run Length (ARL) to Detect the Deviation in the Process A case Study in an Industrial Organization
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Statistical control charts are widely used in industry for process and measurement control . in this paper we study the use of markov chain approach in calculating the average run length (ARL) of cumulative sum (Cusum) control chart for defect the shifts in the mean of process , and exponentially weighted moving average (EWMA) control charts for defect the shifts for process mean and , the standard deviation . Also ,we used the EWMA charts based on the logarithm of the sample variance for monitoring a process standard deviation when the observations (products are selected from al_mamun factory ) are identically and independently distributed (iid) from normal distribution in continuous manufacturing .

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study
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The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

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