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A HYBRID CUCKOO SEARCH AND BACK-PROPAGATION ALGORITHMS WITH DYNAMIC LEARNING RATE TO SPEED UP THE CONVERGENCE (SUBPL) ALGORITHM
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BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.

Publication Date
Mon May 25 2026
Journal Name
Journal Of Composites Science
Sectional and Stress Analysis of Hybrid Reinforced Concrete Beams with Embedded GFRP Profiles Under Monotonic Static Loading
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Glass fiber–reinforced polymer (GFRP) reinforcement provides an effective alternative to conventional steel in concrete structures due to its corrosion resistance. Nevertheless, the lower elastic modulus of GFRP necessitates careful consideration of serviceability behavior in GFRP-reinforced concrete members. This study presents a numerical sectional analysis model for predicting the flexural response and ultimate capacity of hybrid reinforced concrete beams incorporating embedded GFRP profiles in combination with either mild steel or GFRP reinforcement bars under monotonic static loading. The proposed model employs realistic nonlinear stress–strain relationships for concrete and steel, together with secant moduli of elasticity

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Publication Date
Wed Jun 01 2022
Journal Name
Civil And Environmental Engineering
A Soil-Pile Response under Coupled Static-Dynamic Loadings in Terms of Kinematic Interaction
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Abstract<p>Although the axial aptitude and pile load transfer under static loading have been extensively documented, the dynamic axial reaction, on the other hand, requires further investigation. During a seismic event, the pile load applied may increase, while the soil load carrying capacity may decrease due to the shaking, resulting in additional settlement. The researchers concentrated their efforts on determining the cause of extensive damage to the piles after the seismic event. Such failures were linked to discontinuities in the subsoil due to abrupt differences in soil stiffness, and so actions were called kinematic impact of the earthquake on piles depending on the outcomes of laboratory</p> ... Show More
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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Physics: Conference Series
Improve topic modeling algorithms based on Twitter hashtags
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Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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Publication Date
Sun Oct 01 2023
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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Publication Date
Sat Sep 30 2023
Journal Name
Wasit Journal Of Computer And Mathematics Science
Real time handwriting recognition system using CNN algorithms
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Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition

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Publication Date
Thu Jan 30 2014
Journal Name
Al-kindy College Medical Journal
Rising Rate of Cesarean Section
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Cesarean section, which was introduced into clinical practice as a lifesaving procedure for both the mother and the baby, is one of the most common surgeries performed in modern obstetrics. Formerly it was performed in interest of the mother; currently it is frequently done for fetal indication. As other procedures of some complexity, its use follows the health care inequity pattern of the world; underuse in low income setting, and adequate or even unnecessary use in middle and high income setting (1).
The first modern cesarean section was performed by German gynecologist Ferdinand Adolf Kehrer in 1881. Cesarean section is often performed when a vaginal delivery would put the baby's or mother's life or health at risk. Many are also pe

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Publication Date
Wed Dec 30 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The effect of salary Domiciliation in the rate of return on assets (ROA): Applied research of a sample of Iraqi banks
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Abstract

The research attempted to find an explanation and solution to a problem related to the fluctuation and decrease In the rate of return on assets for the research sample banks during the duration of the research, The search started from the hypothesis that, The effect of salary Domiciliation on the banking profitability of a sample of Iraqi banks participating in the salary settlement system for the period (2016-2019),The research used the descriptive historical approach, the quantitative analytical approach and the statistical approach. The research reached a set of conclusions, the most important of which is The effect of salary Domiciliation on banking profitability was achieved in three banks

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Publication Date
Thu Dec 08 2022
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
Implementation of RWP and Gauss Markov Mobility Model for Multi-UAV Networks in Search and Rescue Environment
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Future generations of wireless networks are expected to heavily rely on unmanned aerial vehicles (UAVs). UAV networks have extraordinary features like high mobility, frequent topology change, tolerance to link failure, and extending the coverage area by adding external UAVs. UAV network provides several advantages for civilian, commercial, search and rescue applications. A realistic mobility model must be used to assess the dependability and effectiveness of UAV protocols and algorithms.  In this research paper, the performance of the Gauss Markov (GM) and Random Waypoint (RWP) mobility models in multi-UAV networks for a search and rescue scenario is analyzed and evaluated. Additionally, the two mobility models GM and RWP are descr

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Publication Date
Sun Feb 01 2026
Journal Name
Agricultural Engineering
DEVELOPMENT AND EVALUATION OF A YOLO ALGORITHM-BASED ROBOTIC SPRAYER FOR REAL-TIME WEED DETECTION
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Abstract<p> Weed control with chemicals is a challenging process that should be performed in a rational way to reduce their negative impact on the surrounding environment. The growth of artificial intelligence algorithms encourages researchers to develop smart spraying robots that detect and spray weeds and distinguish them from the main crop which leads to sustainable use of these chemicals and achieves some of the sustainable development goals. However, few studies are available to comprehensively compare different versions of YOLO algorithm to detect weed. In this research, seven versions of YOLO algorithms were evaluated for their performance to detect and spray four t</p> ... Show More
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Publication Date
Sat Feb 11 2023
Journal Name
Applied Sciences
A Preliminary Study and Implementing Algorithm Using Finite State Automaton for Remote Identification of Drones
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Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the ene

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