In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

Abstract

The research aims to stand on the practice of operations management of solid waste in the city of Hilla, carried out by the mayor of Hilla Directorate - solid and the environment Waste Division, through field visits and personal interview to officials of the municipal departments and units of its data collection and information related to solid waste, and assess the current status of the processes of collection and transport waste through the questionnaire that had been prepared for citizens and employees, the search reach a set of conclusions was the most important, operations carried out by the municipality of Hilla Directorate only limited to two (collection, transportation and disposal of wa

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The Iraqi government seeks to overcome the financial crisis by investing and privatizing some projects to achieve sustainable growth. Most of the investment projects in Iraq suffer from many constraints that greatly impact the success of these projects. A survey of the opinions of a group of experts was conducted to identify the most important constraints facing the investment process in Iraq. Then the experts' answers were arranged in a closed questionnaire and distributed to the research sample for which the statistical analysis was conducted. Through it, the most important (17) factors that had the greatest impact on the failure of investment projects in Iraq were reached. One of the main constraints was

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.