In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

 The current research aims to: (know the effectiveness of the harvesting strategy in the achievement of the students of the Institute of Fine Arts in environmental art.
- In order to know this effectiveness, the researcher put a main zero hypothesis and derived six sub-hypotheses from it. the usual way; As the research community reached (120) male and female students of the Fine Arts Institutes for the morning study in Baghdad. As for the research sample, it was chosen by the simple random method, and the number was (75) male and female students for the year 2021-2022 AD. The researcher applied a pre-knowledge test for the four groups of research to find out the level of previous experiences of students in the subject of environmen

The Role of the Deubiquitylase MYSM1 During Alphavirus Infection Amer Nubgan The members of the genus Alphavirus are positive-sense RNA viruses and it is one of two within the family Togaviridae. Most alphaviruses are predominantly transmitted to susceptible vertebrates by a mosquito vector. Alphavirus disease in humans can be severely debilitating, and depending on the particular viral species, infection may result in encephalitis and possibly life threatening symptoms. Chikungunya virus (CHIKV) is the aetiological agent represents a substantial health burden to affected populations, with clinical symptoms that include severe joint and muscle pain, rashes, and fever, as well as prolonged periods of disability in some patients. In recent

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me