Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental stages (pre-and post-lesion) using electromyography signals. Eight time-domain features were extracted from the collected electromyography data. To overcome the imbalanced dataset issue, synthetic minority oversampling technique was applied. Different ML classification techniques were applied including multilayer perceptron, support vector machine, K-nearest neighbors, and radial basis function network; then their performances were compared. A confusion matrix and five other statistical metrics (sensitivity, specificity, precision, accuracy, and F-measure) were used to evaluate the performance of the generated classifiers. The results showed that the best classifier for the left- and right-side data is the multilayer perceptron with a total F-measure of 79.5% and 86.0% for the left and right sides, respectively. This work will help to build a reliable classifier that can differentiate between these two phases by utilizing some extracted time-domain electromyography features.
This research is qualitative in nature. It aims to investigate descriptively, analytically, and comparatively the modern AK model represented by the Sudan Open University Series, and the European framework, the common reference for Teaching Foreign Languages, to uncover what was achieved in them in terms of communication and language use. Accordingly, an integrated, multi-media approach has been adopted to enable the production and reception activities, and the spread of Arabic in vast areas of the world. Such a spread helps Arabic language to be in a hegemonic position with the other living languages. The study is based on getting benefit from human experiences and joint work in the field of teaching Arabic to non-Arabic speakers to mee
... Show MoreThis work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it
... Show MoreIn this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
... Show MoreText categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th
... Show MoreA theoretical study on corrosion inhibitors was done by quantum calculations includes semi-empirical PM3 and Density Functional Theory (DFT) methods based on B3LYP/6311++G (2d,2P). Benzimidazole derivative (oxo(4- ((phenylcarbamothioyl) carbamoyl)phenyl) ammonio) oxonium (4NBP) and thiourea derivative 2-((4- bromobenzyl)thio) -1H-benzo[d] imidazole (2SB) were used as corrosion inhibitors and an essential quantum chemical parameters correlated with inhibition efficiency, EHOMO (highest occupied molecular orbital energy) and ELUMO (lowest molecular orbital energy). Other parameters are also studied like energy gap [ΔE (HOMO-LUMO)], electron affinity (EA), hardness (Δ), dipole moment (μ), softness (S), ionization potential (IE), absolut
... Show MoreDiese Forschungsarbeit versteht sich als ein Versuch zur Bestimmung einer der neuen Unterrichtsmethoden, die den Lernenden im Fach Deutsch besonders interessant vorkommen.Der Unterricht soll in einer Atmosphäre, die frei von Zeitdruck, Angst und Zensurdruck ablaufen lassen, damit werden die Studenten ermutigt, die Nutzung von Spiel zu ihrer eigenen Sache machen, um die Spielziele Spaß, Empathie, Zusammenarbeit und Kommunikation zu realisieren. Das Hauptlernziel des Fremdsprachenunterrichts ist es, Lernenden zu ermöglichen, in der Zielsprache zu kommunizieren.
Wir konzentrieren uns in diesem Beitrag auf eine wichtige sprachliche Fertigkeit, die den rezeptiven Fertigkeiten gehört, die ist das Hörverstehen.
Das Hörvers
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