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3D scenes semantic segmentation using deep learning based Survey
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Abstract<p>Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the power and popular tool for data and image processing in computer vision, used for many applications like “image recognition”, “object detection”, “semantic segmentation”, In this research paper, provide survey a background for many techniques designed to 3 Dimensions point cloud semantic segmentation in different domains on many several available free datasets and also making a comparison between these methods.</p>
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Publication Date
Mon Sep 01 2025
Journal Name
Journal Of Information Hiding And Multimedia Signal Processing
Steganography Based on Image Compression Using a Hybrid Technique
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Information security is a crucial factor when communicating sensitive information between two parties. Steganography is one of the most techniques used for this purpose. This paper aims to enhance the capacity and robustness of hiding information by compressing image data to a small size while maintaining high quality so that the secret information remains invisible and only the sender and recipient can recognize the transmission. Three techniques are employed to conceal color and gray images, the Wavelet Color Process Technique (WCPT), Wavelet Gray Process Technique (WGPT), and Hybrid Gray Process Technique (HGPT). A comparison between the first and second techniques according to quality metrics, Root-Mean-Square Error (RMSE), Compression-

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Publication Date
Sun Nov 01 2020
Journal Name
Plant Archives
Survey of exotic plant species in Iraq
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Publication Date
Mon Aug 01 2016
Journal Name
Arpn Journal Of Engineering And Applied Sciences
SURVEY ON VANET TECHNOLOGIES AND SIMULATION MODELS
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Vehicular ad hoc network (VANET) is a distinctive form of Mobile Ad hoc Network (MANET) that has attracted increasing research attention recently. The purpose of this study is to comprehensively investigate the elements constituting a VANET system and to address several challenges that have to be overcome to enable a reliable wireless communications within a vehicular environment. Furthermore, the study undertakes a survey of the taxonomy of existing VANET routing protocols, with particular emphasis on the strengths and limitations of these protocols in order to help solve VANET routing issues. Moreover, as mobile users demand constant network access regardless of their location, this study seeks to evaluate various mobility models for vehi

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Publication Date
Thu Mar 10 2016
Journal Name
International Journal Of Current Microbiology And Applied Sciences
Survey of Coleopteran Insects on Alfalfa Plant
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Publication Date
Mon Dec 24 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
SURVEY OF THE MARINE FISHES IN IRAQ
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    A survey of fish species in the Iraqi marine waters was carried out for the period from November 2014 to March 2018. The list included 214 species representing 75 families.

 

     The family Carangidae dominated the marine fishes in Iraq, which was represented by 24 species, followed by Haemulidae with 11 species, and then Serranidae and Sparidae with nine species for each, while 34 families contained a single species only.

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
6G Wireless Communications Networks: A Comprehensive Survey
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Publication Date
Wed Jul 01 2020
Journal Name
Journal Of Engineering
Flexural Behavior of Reinforced Concrete Beams Reinforced with 3D-Textile Composite Fiber
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Normal concrete is weak against tensile strength, has low ductility, and also insignificant resistance to cracking. The addition of diverse types of fibers at specific proportions can enhance the mechanical properties as well as the durability of concrete. Discrete fiber commonly used, has many disadvantages such as balling the fiber, randomly distribution, and limitation of the Vf ratio used. Based on this vision, a new technic was discovered enhancing concrete by textile-fiber to avoid all the problems mentioned above. The main idea of this paper is the investigation of the mechanical properties of SCC, and SCM that cast with 3D AR-glass fabric having two different thicknesses (6, 10 mm), and different layers (1,2 laye

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Publication Date
Tue Jun 05 2018
Journal Name
The Cleft Palate-craniofacial Journal
Longitudinal 3D Assessment of Facial Asymmetry in Unilateral Cleft Lip and Palate
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Publication Date
Wed Nov 01 2023
Journal Name
Solar Energy
Optimizing performance of water-cooled photovoltaic-thermal modules: A 3D numerical approach
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To evaluate and improve the efficiency of photovoltaic solar modules connected with linear pipes for water supply, a three-dimensional numerical simulation is created and simulated via commercial software (Ansys-Fluent). The optimization utilizes the principles of the 1st and 2nd laws of thermodynamics by employing the Response Surface Method (RSM). Various design parameters, including the coolant inlet velocity, tube diameter, panel dimensions, and solar radiation intensity, are systematically varied to investigate their impacts on energetic and exergitic efficiencies and destroyed exergy. The relationship between the design parameters and the system responses is validated through the development of a predictive model. Both single and mult

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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