The primary objective of this study is to manage price market items in the construction of walls for affordable structures with load-bearing hollow masonry units using the ACI 211.1 blend design with a slump range of 25-50 mm that follows the specification limits of IQS 1077. It was difficult to reach a suitable cement weight to minimum content (economic and environmental goal), so many trail mixtures were cast. A portion (10-20%) of the coarse aggregates was replaced with concrete, tile, and clay-brick waste. Finally, two curing methods were used: immersion under water as normal curing, and water spraying as it is closer to the field conditions. The recommendation in IQS 1077 to increase the curing period from 14 to 28 days was tak

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

Due to economic reasons or need for environmental conservatism or also preserve the natural resources; there has been an increasing shift towards the use of reclaimed asphalt pavement (RAP) materials in the pavement construction industry. Therefore, use the Reclaimed Asphalt Pavement (RAP) has been enormously increased in pavement construction and has been become common practice in many countries. Nevertheless, this is a relatively new concept in Iraq, and has to be remarked that is not used RAP in the production of HMA and this valuable material is mostly degraded. For this purpose, the reclaimed materials were collected from deteriorated pavement segments. The components of asphalt mixtures consist of: two asphalt penetration grades (40-5

Enhancing fatigue resistance in asphalt binders and mixtures is crucial for prolonging pavement lifespan and improving road performance. Recent advancements in nanotechnology have introduced various nanomaterials such as alumina (NA), carbon nanotubes (CNTs), and silica (NS) as potential asphalt modifiers. These materials possess unique properties that address challenges related to asphalt fatigue. However, their effectiveness depends on proper dispersion and mixing techniques. This review examines the mixing methods used for each nanomaterial to ensure uniform distribution within the asphalt matrix and maximize performance benefits. Recent research findings are synthesized to elucidate how these nanomaterials and their mixing proce

Image Fusion Using A Convolutional Neural Network

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o