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Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance while minimizing redundancy. This optimization process improves the performance of the classification model in general. In case of classification, the Support Vector Machine (SVM) and Neural Network (NN) hybrid model is presented. This combines an SVM classifier's capacity to manage functions in high dimensional space, as well as a neural network capacity to learn non-linearly with its feature (pattern learning). The model was trained and tested on an EEG dataset and performed a classification accuracy of 97%, indicating the robustness and efficacy of our method. The results indicate that this improved classifier is able to be used in brain–computer interface systems and neurologic evaluations. The combination of machine learning and optimization techniques has established this paradigm as a highly effective way to pursue further research in EEG signal processing for brain language recognition.

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Publication Date
Tue Feb 05 2019
Journal Name
Journal Of The College Of Education For Women
African landlocked countries problems and outlets in the study and classification of geo-political
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Landlocked countries are displayed geopolitical new geo-political and intended to
countries that do not have sea views, a phenomenon present in four continents of the world
are: Africa, Europe, and Asia, and South America and the number arrived at the present time
to the (44) state the largest number of them in the continent it arrived in Africa (16) countries
in Asia (13) countries and Europe (13) In the State of South America two. This phenomenon
emerged due to the division of federations and empires and colonial treaties and others. But
the negative effects suffered by these countries may vary from one country to another, since
these countries in the continent of Europe, for example, is different from the same cou

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Publication Date
Sat Mar 29 2014
Journal Name
International Journal Of Academic Research In Progressive Education And Development
The Effects of Problem-Based Learning on Self-Directed Learning Skills among Physics Undergraduates
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The aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq. In this study, the pre- and post-test were done and the instruments were administered to the students for data collection. The data was analyzed and statistical results rejected null hypothesis of this study. This study revealed that there are no signifigant differences between PBL and PBL with lecture method, thus the PBL without or with lecture method enhances the self-directed learning skills bette

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Robust Optimization with practical application
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The purpose of this paper is applying the robustness in Linear programming(LP) to get rid of uncertainty problem in constraint parameters, and find the robust optimal solution, to maximize the profits of the general productive company of vegetable oils for the year 2019, through the modify on a mathematical model of linear programming when some parameters of the model have uncertain values, and being processed it using robust counterpart of linear programming to get robust results from the random changes that happen in uncertain values ​​of the problem, assuming these values belong to the uncertainty set and selecting the values that cause the worst results and to depend buil

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Publication Date
Mon Jul 11 2022
Journal Name
Aip Conference Proceedings
Gas lift optimization: A review
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Optimization of gas lift plays a substantial role in production and maximizing the net present value of the investment of oil field projects. However, the application of the optimization techniques in gas lift project is so complex because many decision variables, objective functions and constraints are involved in the gas lift optimization problem. In addition, many computational ways; traditional and modern, have been employed to optimize gas lift processes. This research aims to present the developing of the optimization techniques applied in the gas lift. Accordingly, the research classifies the applied optimization techniques, and it presents the limitations and the range of applications of each one to get an acceptable level of accura

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Publication Date
Mon Jul 11 2022
Journal Name
Aip Conference Proceedings
Well placement optimization: A review
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Optimization of well placement plays a considerable role in the production and maximizing the net present value of the investment of oil field developments. However, the application of the optimization techniques in well placement developments is so complicated because many decision variables, objective functions, and constraints are involved in the well placement optimization case. Furthermore, many computational techniques; conventional and non-conventional, have been utilized to optimize well placement operations. This study displays the advancement of the optimization methods applied in the well placement. Subsequently, the study assorted the applied optimization methods, and it demonstrates the restriction and the range of implementati

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Publication Date
Mon Dec 05 2022
Journal Name
Baghdad Science Journal
Cloud Data Security through BB84 Protocol and Genetic Algorithm
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In the current digitalized world, cloud computing becomes a feasible solution for the virtualization of cloud computing resources.  Though cloud computing has many advantages to outsourcing an organization’s information, but the strong security is the main aspect of cloud computing. Identity authentication theft becomes a vital part of the protection of cloud computing data. In this process, the intruders violate the security protocols and perform attacks on the organizations or user’s data. The situation of cloud data disclosure leads to the cloud user feeling insecure while using the cloud platform. The different traditional cryptographic techniques are not able to stop such kinds of attacks. BB84 protocol is the first quantum cry

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Publication Date
Sun Mar 10 2024
Journal Name
Journal Of Optics
Optical signal transmission for the visible light communication system through the water–air interface link
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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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Publication Date
Fri Dec 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Effect of speed and die holes diameter in the machine on feed pellets quality
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Abstract<p>The study aimed to effect of speed and die holes diameter in the machine on feed pellets quality. In this study was measured pellet direct measurement (%), pellet lengths (%), pellet durability (%) and pellet water absorption (%). Three die speeds 280, 300, and 320 rpm, three diameters of die holes in the machine 3, 4, and 5 mm, have been used. The results showed that increasing the pellet die speeds from 280 to 300 then to 320 rpm led to a significant decrease in direct measurement, pellet durability, and pellet water absorption was increased, whereas it did not significantly affect the pellet lengths. Increasing the die holes diameter from 3 to 4 then to 5 mm led to a significant de</p> ... Show More
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