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Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification, including ResNet50, VGG19, and InceptionV4; They were trained and tested on an open-source satellite image dataset to analyze the algorithms' efficiency and performance and correlated the classification accuracy, precisions, recall, and f1-score. The result shows that InceptionV4 gives the best classification accuracy of 97% for cloudy, desert, green areas, and water, followed by VGG19 with approximately 96% and ResNet50 with 93%. The findings proved that the InceptionV4 algorithm is suitable for classifying oil spills and no spill with satellite images on a validated dataset.

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Publication Date
Tue Mar 01 2016
Journal Name
International Journal Of Computer Science And Mobile Computing
Content-Based Cartoon Image Retrieval
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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
The Role of Satellite Channels in Imparting Behavior to Children from the Point of View of their Parents in Tulkarm City
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This study aims to identify the role of satellite channels in imparting behavior to children from the point of view of their parents in Tulkarm city. The researcher used a descriptive technique. A sample of (18000) males and females married couples was used above 20 years old in the city of Tulkarm. The study sample size is (201) married couples. It took place in September 2020. The questionnaire was the main tool for collecting data. The study found that the total degree of satellite channels contribution in imparting negative behaviors to children was high, as it reached (72.20%). The total degree of the role of satellite channels in imparting positive behaviors to children was medium, reaching (69.20%). Moreover, the results also indi

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Publication Date
Tue Jan 08 2019
Journal Name
Lubricants
Influence of Sample Mixing Techniques on Engine Oil Contamination Analysis by Infrared Spectroscopy
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For the most reliable and reproducible results for calibration or general testing purposes of two immiscible liquids, such as water in engine oil, good emulsification is vital. This study explores the impact of emulsion quality on the Fourier transform infrared (FT-IR) spectroscopy calibration standards for measuring water contamination in used or in-service engine oil, in an attempt to strengthen the specific guidelines of ASTM International standards for sample preparation. By using different emulsification techniques and readily available laboratory equipment, this work is an attempt to establish the ideal sample preparation technique for reliability, repeatability, and reproducibility for FT-IR analysis while still considering t

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Publication Date
Tue Sep 27 2022
Journal Name
Al–bahith Al–a'alami
POLITICAL DEBATES AND THEIR ARTISTIC CONSTRUCTION IN IRAQI SATELLITE CHANNELS ( AN ANALYTICAL STUDY ) : (A research drawn from a Master Thesis)
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The problem of research on the study of political debate programs in the Iraqi satellite channels, in the "People decide" program by Afaq channel and " electoral competition " by Fallujah channel), and its importance for the community and researchers in the scientific field, as new programs to enter the Iraqi media after we have been the world media a lot in this area at the academic and practical levels (The field), and seeks to find out what the technical construction of the programs of political debates in Iraqi satellite channels and methods of construction and methods of employment used by the technical elements in the presentation of the programs and The study adopted the surve

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Publication Date
Tue Jun 30 2015
Journal Name
International Journal Of Computer Techniques
Multifractal-Based Features for Medical Images Classification
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This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

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Publication Date
Thu Dec 16 2021
Journal Name
Translational Vision Science & Technology
A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps
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Publication Date
Fri Sep 27 2024
Journal Name
Journal Of Applied Mathematics And Computational Mechanics
Fruit classification by assessing slice hardness based on RGB imaging. Case study: apple slices
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Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

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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
Mon Dec 18 2017
Journal Name
Al-khwarizmi Engineering Journal
Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm
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This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algori

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Publication Date
Sun Dec 28 2025
Journal Name
Al–bahith Al–a'alami
TOPICS OF PEACEFUL COEXISTENCE IN FOREIGN SATELLITE CHANNELS DIRECTED IN THE ARABIC LANGUAGE: (A Research Drawn from Master Thesis)
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This research deals with issues of peaceful coexistence in foreign satellite channels directed in the Arabic language, trying to get acquainted with the most prominent of these topics dealt with the programs subject to analysis and the method of dealing with them and the most used journalistic arts in that.

The research adopted the descriptive approach and the method of content analysis for the purpose of studying the research community represented by the program «Shabab Talk» “Youth Talk” in the German channel Deutsche Welle (DW) and the program «Beina Sam wa Amar» “between Sam and Ammar” in the American free channel, by designing the content analysis form to subject

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