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Go to Editorial ManagerPower outages are a common and persistent problem in Iraq, significantly impacting various aspects of life and business. These interruptions disrupt routine household tasks and hinder more complex technical operations in industries and services. Emphasizing the need for careful management and proactive solutions. This paper introduces a real-world time series dataset for Baghdad city, including historical outages, weather conditions (such as temperature), and power overloads, and analyzes the correlation among these parameters in different seasons. The research uses this dataset to train one-dimensional Convolutional Neural Networks (1D CNN) to find patterns and relationships that can accurately predict when power outages can happen in the long term and short term to improve the management of the Baghdad electricity grid through data-driven networks. This model was evaluated using performance metrics, and the results show that CNN is accurate in predicting outages in the short term with a Mean Absolute Error (MAE) of (0.0077), whereas, in the long term, it has achieved an MAE of (0.0775). These predictive models have the potential to facilitate the development of proactive measures aimed at reducing the impact of power outages by anticipating potential outages in advance. This research focuses on enhancing the reliability and efficiency of Baghdad's electricity supply, ultimately contributing to economic growth and stability.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.
Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.