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A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures and values of learning parameters are determined through cross-validation, and test datasets unseen in the cross-validation are used to evaluate the performance of the DMLP trained using the three-stage learning algorithm. Experimental results show that the proposed method is effective in combating overfitting in training deep neural networks.

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Publication Date
Mon Apr 01 2024
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

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Publication Date
Mon Feb 14 2022
Journal Name
Journal Of Educational And Psychological Researches
Cognitive Beliefs and Their Relationship to Self-Organized Learning Strategies at the Preparatory Stage
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The study aims to identify the level of cognitive beliefs, as well as to identify the level of self-organized learning strategies among intermediate school students. The study also aims to identify the differences in the level of self-organized learning strategies among intermediate school students in term of gender, branch (scientific, literary). In order to achieve the research objectives, the researcher designed a scale to measure the cognitive beliefs. As for the scale of self-organized learning strategies, the researcher adopted a scale of (Pintrich et al. 1991), which was translated by (Izzat Abdelhamid, 1999) , For self-organized learning strategies, the sample consisted of (400) students from the research population, whic

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Publication Date
Thu Jan 06 2022
Journal Name
Iraqi National Journal Of Nursing Specialties
Assessment of Health Follow up and Weight Control for Women with Osteoporosis in Baqubah City
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Objective(s): assessment of  the health follow up and weight control for women with osteoporosis and find out the relationship between their health follow up and weight control and their socio-demographic characteristics.

Methodology: A descriptive study was conducted on women with osteoporosis for the period of September, 26th 2020 to Jun, 20th 2021. Non- probability (convenient) sample of (70) women with osteoporosis selected from (5) Private Clinics for Joints and Fractures in Baqubah City. A questionnaire was designed though extensive review of literatures and it consists of three parts: the first part includes women’s socio demographic characteristics, the second part inclu

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Publication Date
Sat Jul 31 2021
Journal Name
Iraqi Journal Of Science
A Decision Tree-Aware Genetic Algorithm for Botnet Detection
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     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

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Publication Date
Wed Jan 02 2019
Journal Name
Journal Of Educational And Psychological Researches
Training Program based on Some Strategies of Self-Regulated Learning for the Teachers of Mathematics and its Effect on their Relational Mathematics
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This research aims at building a proposed training program according to the self-regulated strategies for the mathematics teachers and to identify the effect of this program on relational Mathematics of teachers. The sample of the research was (60) Math teachers; (30) teachers as experimental group and (30) teachers as control group. The results of the current research reacheded that the proposed training program according to some self-managed learning strategies, meets the needs of trainees with remarkable effectiveness to improve the level of their teaching performance to achieve the desired goals. Training teacher according to self-managed learning strategies is effective in bringing about the transition of training to their students

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Publication Date
Fri Dec 15 2023
Journal Name
Journal Of Baghdad College Of Dentistry
Effects of fixed orthodontic appliance with antihypertensive drugs on the body weight of experimental rats
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Background: This study aims to investigate the effect of fixed orthodontic appliances and/or antihypertensive drugs on the weight of experimental rats. Materials and Methods: Thir-ty-six male Wistar albino rats were subjected to a split-mouth design study, in which an orthodontic appliance was inserted in one side to move the first molar mesially for 2 weeks while the other side acted as a control to tooth movement. The rats were allocated into three groups: group A (n = 12), without any pharmacological treatment; group B (n = 12), subcu-taneous injection of bisoprolol fumarate (5 mg/kg) daily; and group C (n = 12), subcutaneous injection of valsartan (10 mg/kg) daily. A fixed orthodontic appliance with a closing coil spring delivering 5

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Publication Date
Thu Jun 26 2014
Journal Name
Engineering Optimization
A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising
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The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isola

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Publication Date
Wed Aug 04 2021
Journal Name
Membranes
Model Based Simulation and Genetic Algorithm Based Optimisation of Spiral Wound Membrane RO Process for Improved Dimethylphenol Rejection from Wastewater
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Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCG

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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Intelligent Engineering And Systems
Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks
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Publication Date
Tue Oct 12 2021
Journal Name
Engineering, Technology & Applied Science Research
Production of Light Weight Foam Concrete with Sustainable Materials
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Most of the recent works related to the construction industry in Iraq are focused on investigating the validity of local raw materials as alternatives to the imported materials necessary for some practical applications, especially in thermal and sound insulation. This investigation includes the use of limestone dust as partial substitution of cement in combination with foam agent and silica fume to produce sustainable Lightweight Foam Concrete (LWFC). This study consists of two stages. In the first stage, trial mixes were performed to find the optimum dosage of foam agent. Limestone dust was used as a partial replacement for cement. Chemical analysis and fineness showed great similarity with cement. Many concrete mixes were prepared

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