In 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

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

This paper delves into the significant role played by local social and traditional structures in shaping Traditional Community Tenure (TCT) within Iraqi Land Tenure Legislation (ILTR), and examines their impact on gender inequalities, with a specific focus on women's land tenure rights. The methodological approach employed in this study identified the sources of barriers to gender equality within TCT as outlined in ILTR at two different bilateral levels, with input obtained from key stakeholders in a selected city in Iraq. The case study survey encompassed three districts, which served as local layers within the historic sectors of the Iraqi city of Al-Nasiriya. the study employed quantitative methods, including a household surveyو with

Iraqi EFL teachers face problems in teaching “English for Iraq Series” for primary public school pupils. In this paper, the researchers are going to identify the main problems faced by our teachers and try to find solutions to these problems. To achieve the aim of the study, list of questions asked and from teachers’ responses, the researchers have got an idea about the main problems which are related to textbook material, parents, learners, environment and technology. Therefore, the researchers adapted a questionnaire to achieve the purpose of the study with some changes and modifications. This questionnaire with five point scale (strongly agree, agree, undecided, disagree, strongly disagree). To achieve face validity, the

Breast cancer constitutes about one fourth of the registered cancer cases among the Iraqi population (1)
and it is the leading cause of death among Iraqi women (2)
. Each year more women are exposed to the vicious
ramifications of this disease which include death if left unmanaged or the negative sequels that they would
experience, cosmetically and psychologically, after exposure to radical mastectomy.
The World Health Organization (WHO) documented that early detection and screening, when coped
with adequate therapy, could offer a reduction in breast cancer mortality; displaying that the low survival rates
in less developed countries, including Iraq, is mainly attributed to the lack of early detection programs couple

The study aims to build a water quality index that fits the Iraqi aquatic systems and reflects the environmental reality of Iraqi water. The developed Iraqi Water Quality Index (IQWQI) includes physical and chemical components. To build the IQWQI, Delphi method was used to communicate with local and global experts in water quality indices for their opinion regarding the best and most important parameter we can use in building the index and the established weight of each parameter. From the data obtained in this study, 70% were used for building the model and 30% for evaluating the model. Multiple scenarios were applied to the model inputs to study the effects of increasing parameters. The model was built 4 by 4 until it reached 17 parame

    The present paper confirmed the presence of Phrynocephalus maculatus longicaudatus Haas, 1957 in Iraq and recorded the first observations of this taxon in Al-Muthanna province southwestern of Iraq. The existence of the species is yet uncertain in Iraq. The habitat and morphological characteristics of this species were reviewed.

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.