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Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
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Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.

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Publication Date
Wed Oct 26 2022
Journal Name
Iraqi Journal Of Science
Gene Expression Analysis via Spatial Clustering and Evaluation Indexing
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The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

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Publication Date
Sat Dec 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box
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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Optimal Dimensions of Small Hydraulic Structure Cutoffs Using Coupled Genetic Algorithm and ANN Model
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A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa

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Publication Date
Fri Jul 07 2017
Journal Name
International Journal Of Science And Research (ijsr)
Automatic brain tumor segmentation from MRI Images using superpixels based split and Merge algorithm
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RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

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Publication Date
Tue Dec 19 2017
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
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In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process.

The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and a

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Publication Date
Thu Jul 01 2021
Journal Name
Journal Of Physics: Conference Series
Wireless Optimization Algorithm for Multi-floor AP deployment using binary particle swarm optimization (BPSO)
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Abstract<p>Optimizing the Access Point (AP) deployment is of great importance in wireless applications owing the requirement to provide efficient and cost-effective communication. Highly targeted by many researchers and academic industries, Quality of Service (QOS) is an important primary parameter and objective in mind along with AP placement and overall publishing cost. This study proposes and investigates a multi-level optimization algorithm based on Binary Particle Swarm Optimization (BPSO). It aims to an optimal multi-floor AP placement with effective coverage that makes it more capable of supporting QOS and cost effectiveness. Five pairs (coverage, AP placement) of weights, signal threshol</p> ... Show More
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Publication Date
Thu Sep 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
The use of the methods of the lower squares and the smaller squares weighted in the estimation of the parameters and design of the sample acceptance schemesFor general exponential distribution
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The acceptance sampling plans for generalized exponential distribution, when life time experiment is truncated at a pre-determined time are provided in this article. The two parameters (α, λ), (Scale parameters and Shape parameters) are estimated by LSE, WLSE and the Best Estimator’s for various samples sizes are used to find the ratio of true mean time to a pre-determined, and are used to find the smallest possible sample size required to ensure the producer’s risks, with a pre-fixed probability (1 - P*). The result of estimations and of sampling plans is provided in tables.

Key words: Generalized Exponential Distribution, Acceptance Sampling Plan, and Consumer’s and Producer Risks

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Publication Date
Sat Jan 31 2026
Journal Name
International Journal Of Intelligent Engineering And Systems
Low-complexity Deep Learning for Joint Channel-type Identification and SNR Estimation in MIMO-OFDM Using CNN–BRNN with LUT Labels
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Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

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Publication Date
Thu Jan 31 2019
Journal Name
Journal Of The College Of Education For Women
The Quantitative Analysis for Road networks in Karbala Governorate
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The road networks is considered to be one of the determinants that controls to specify the areas of human activities, which it depend on to specify the arrival cost , in addition it is useful to achieve the connectivity for interaction and human activities , and shorten the distance and time between the population and places of service. The density of the road network in any space directly affected by the density of population and the type of economic activities and administrative functions performed by the space. On this basis, the subject of this study is reflected in the quantitative analysis of the roads network in the Governorate of Karbala. The study consists the quantitative analysis for the roads network and the Urban Nodes in th

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Publication Date
Thu Sep 13 2018
Journal Name
Baghdad Science Journal
Estimation of some Organophosphorus Pesticides Using Carbon Paste Electrode Coupled with Molecularly Imprinted Polymers
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The aim of this study was to develop a sensor based on a carbon paste electrodes (CPEs) modified with used MIP for determination of organophosphorus pesticides (OPPs). The modified electrode exhibited a significantly increased sensitivity and selectivity of (OPPs). The MIP was prepared by thermo-polymerization method using N,N-diethylaminoethymethacrylate (NNDAA) as functional monomer, N,N-1,4-phenylenediacrylamide (NNPDA) as cross-linker, the acetonitrile used as solvent and (Opps) as the template molecule. The three OPPs (diazinon, quinalphos and chlorpyrifos) were chosen as the templates, which have been selected as base analytes which used widely in agriculture sector. The extraction efficiency of the imprinted polymers has been evaluat

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