This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin

يشكل السياق جانبا مهماً في فهم الخطاب والمقصود هنا العرض المسرحي، من خلال ما يمده السياق للمتلقي من مؤشرات، يتم الاعتماد عليها في استكمال المعنى الظاهر بالمعنى المستتر، فالسياق يعتمد في الاصل على المحيط المادي الاجتماعي الذي يتم فيه التواصل وفيه يتعرف المرسل والمتلقي أحدهما على الآخر وتتبلور الصورة التي يحملها الطرفان أحدهما عن الآخر، الى جانب كونه يمثل الاحداث التي سبق لهما أن عاشاها والتبادل القولي

Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- south

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     The goal of the research is to develop a sustainable rating system for roadway projects in Iraq for all of the life cycle stages of the projects which are (planning, design, construction and operation and maintenance). This paper investigates the criteria and its weightings of the suggested roadway rating system depending on sustainable planning activities. The methodology started in suggesting a group of sustainable criteria for planning stage and then suggesting weights from (1-5) points for each one of it. After that data were collected by using a closed questionnaire directed to the roadway experts group in order to verify the criteria weightings based on the relative importance of the roadway related impacts

The meanings attributed to Female Genital Mutilation/Cutting (FGM/C) are shaped through complex negotiations within religious and socio-cultural frameworks, including those observed in Indonesia. Using a combined qualitative and quantitative (mixed methods)-ethnographic and survey approach, data from 109 students of religious tertiary institutions in East Kalimantan on their perspectives on FGM/C practices can be more comprehensively explored. The results of the study, which were analysed using the three principles of symbolic interactionism, showed that 72.5 per cent of religious college student families still practice FGM/C and 53.2 per cent stated that FGM/C practices are beneficial for women.  However, they are also willing, if

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye