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Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System
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An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.

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Publication Date
Tue Jun 01 2021
Journal Name
Baghdad Science Journal
Synthesis, Characterization and Gas Sensor Application of New Composite Based on MWCNTs:CoPc:Metal Oxide
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The synthesis of new substituted cobalt Phthalocyanine (CoPc) was carried out using starting materials Naphthalene-1,4,5, tetracarbonic acid dianhydride (NDI) employing dry process method. Metal oxides (MO) alloy of (60%Ni3O4 40%-Co3O4 ) have been functionalized with multiwall carbon nanotubes (F-MWCNTs) to produce (F-MWCNTs/MO) nanocomposite (E2) and mixed with  CoPc to yield (F-MWCNT/CoPc/MO) (E3). These composites were investigated using different analytical and spectrophotometric methods such as 1H-NMR (0-18 ppm), FTIR spectroscopy in the range of (400-4000cm-1), powder X-rays diffraction (PXRD, 2θ o = 10-80), Raman spectroscopy (0-4000 cm-1), and UV-Visib

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Publication Date
Wed Nov 01 2023
Journal Name
Journal Of Dentistry
The in-vitro development of novel enzyme-based chemo-mechanical caries removal agents
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Objectives Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents. Methods The novel agents were prepared using different concentrations of bromelain (10–40 wt. %), with and without 0.1–0.3 wt. % chloramine T or 0.5–1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficiency

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Publication Date
Mon Oct 24 2022
Journal Name
Energies
Double-Slope Solar Still Productivity Based on the Number of Rubber Scraper Motions
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In low-latitude areas less than 10° in latitude angle, the solar radiation that goes into the solar still increases as the cover slope approaches the latitude angle. However, the amount of water that is condensed and then falls toward the solar-still basin is also increased in this case. Consequently, the solar yield still is significantly decreased, and the accuracy of the prediction method is affected. This reduction in the yield and the accuracy of the prediction method is inversely proportional to the time in which the condensed water stays on the inner side of the condensing cover without collection because more drops will fall down into the basin of the solar-still. Different numbers of scraper motions per hour (NSM), that is

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Publication Date
Sat Jul 06 2024
Journal Name
Multimedia Tools And Applications
Text classification based on optimization feature selection methods: a review and future directions
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A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

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Publication Date
Tue Feb 24 2015
Journal Name
Robotica
Multi-level control of zero-moment point-based humanoid biped robots: a review
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SUMMARY<p>Researchers dream of developing autonomous humanoid robots which behave/walk like a human being. Biped robots, although complex, have the greatest potential for use in human-centred environments such as the home or office. Studying biped robots is also important for understanding human locomotion and improving control strategies for prosthetic and orthotic limbs. Control systems of humans walking in cluttered environments are complex, however, and may involve multiple local controllers and commands from the cerebellum. Although biped robots have been of interest over the last four decades, no unified stability/balance criterion adopted for stabilization of miscellaneous walking/running modes of biped </p> ... Show More
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Publication Date
Wed Aug 28 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Scopus (7)
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Publication Date
Wed Nov 01 2023
Journal Name
Journal Of Dentistry
The in-vitro development of novel enzyme-based chemo-mechanical caries removal agents
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Objectives: Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents. Methods: The novel agents were prepared using different concentrations of bromelain (10–40 wt. %), with and without 0.1–0.3 wt. % chloramine T or 0.5–1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficie

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Scopus (9)
Crossref (9)
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Publication Date
Wed Aug 28 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Scopus (7)
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Publication Date
Fri May 31 2019
Journal Name
Journal Of Engineering
A Comparative Study of Various Intelligent Algorithms based Path Planning for Mobile Robots
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In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot.  Simulation results, whi

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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