In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather than the whole conductance as reported in the literature. Anti-Hebbian and Hebbian (AHaH) learning rules are used to mimic the changes in memristance of the memristors. This research will concentrate on the effect of conductance on an individual MSS to simulate the nanotechnology devices of the memristors. A single synapse is presented by a couple of memristors to mimic its resistance switching. The learning circuit of artificial synapses could be used in many applications, such as image processing and neural networks, for pattern classification of synapses, represented by a map of the memeristors. These synapses are essential elements for data processing and information storage in both real and artificial neural systems.
The main objectives of this study were investigating the effects of the maximum size of coarse Attapulgite aggregate and micro steel fiber content on fresh and some mechanical properties of steel fibers reinforced lightweight self-compacting concrete (SFLWSCC). Two series of mixes were used depending on maximum aggregate size (12.5 and 19) mm, for each series three different steel fibers content were used (0.5 %, 1%, and 1.5%). To evaluate the fresh properties, tests of slump flow, T500 mm, V funnel time, and J ring were carried out. Tests of compressive strength, splitting tensile strength, flexural tensile strength, and calculated equilibrium density were done to evaluate mechanical properties. For reference mixes, the
... Show MoreComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
... Show MoreThe hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s
... Show MoreTo investigate the effects of losartan and enalapril on serum uric acid in hypertensive patients with metabolic syndrome, one hundred and twenty six newly diagnosed mild hypertensive patients, having markers of metabolic syndrome included in the study. The patients were divided into two groups. Group 1 (60 patients) was given losartan (50 mg/ day) and group 2 (66 patients) enalapril (20 mg/ day) for a duration of 2 months. A control group of seventy apparently healthy individuals were included. Metabolic syndrome was diagnosed according to diagnostic criteria of metabolic syndrome related to the American National Cholesterol Education Program-Adult Treatment Panel III. Serum uric acid levels were measured bef
... Show MoreFeatures is the description of the image contents which could be corner, blob or edge. Corners are one of the most important feature to describe image, therefore there are many algorithms to detect corners such as Harris, FAST, SUSAN, etc. Harris is a method for corner detection and it is an efficient and accurate feature detection method. Harris corner detection is rotation invariant but it isn’t scale invariant. This paper presents an efficient harris corner detector invariant to scale, this improvement done by using gaussian function with different scales. The experimental results illustrate that it is very useful to use Gaussian linear equation to deal with harris weakness.