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A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven classifiers. A hybrid supervised learning system that takes advantage of rich intermediate features extracted from deep learning compared to traditional feature extraction to boost classification accuracy and parameters is suggested. They provide the same set of characteristics to discover and verify which classifier yields the best classification with our new proposed approach of “hybrid learning.” To achieve this, the performance of classifiers was assessed depending on a genuine dataset that was taken by our camera system. The simulation results show that the support vector machine (SVM) has a mean square error of 0.011, a total accuracy ratio of 98.80%, and an F1 score of 0.99. Moreover, the results show that the LR classifier has a mean square error of 0.035 and a total ratio of 96.42%, and an F1 score of 0.96 comes in the second place. The ANN classifier has a mean square error of 0.047 and a total ratio of 95.23%, and an F1 score of 0.94 comes in the third place. Furthermore, RF, WKNN, DT, and NB with a mean square error and an F1 score advance to the next stage with accuracy ratios of 91.66%, 90.47%, 79.76%, and 75%, respectively. As a result, the main contribution is the enhancement of the classification performance parameters with images of varying brightness and clarity using the proposed hybrid learning approach.

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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Deep Learning-Driven Decision Fusion: Spatio-Spectrogram Features for Inner Speech Recognition From Electroencephalogram Signals
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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Deep Learning-Driven Decision Fusion: Spatio-Spectrogram Features for Inner Speech Recognition From Electroencephalogram Signals
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Publication Date
Thu May 06 2010
Journal Name
Journal Of Engineering
A PROPOSED PROCEDURE FOR ANALYSING REINFORCED EMBANKMENTS
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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
A Proposed Agent System for Network Monitoring
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The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presente

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Publication Date
Mon Nov 19 2018
Journal Name
Al–bahith Al–a'alami
A Proposed Vision for Editing News Forms
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This research deals with a very important subject as it tries to change the theoretical and scientific heritage and some professional rules adopted in the newsroom. Most media students have difficulties in writing news for press correctly. The researcher tries to identify the compatibility of what is published in local news agencies with professional and academic standards.
The research finds detailed editorial rules for a number of news formats which will play an important role in writing news for press easily, especially for the beginners and newcomers. Also, it discovers a new fact denying the beliefs of some researchers and writers in not having news conclusion in news edited according to the inverted pyramid pattern.
The re

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Publication Date
Thu Nov 01 2012
Journal Name
Ijcsi International Journal Of Computer Science
Implementing a novel approach an convert audio compression to text coding via hybrid technique
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Compression is the reduction in size of data in order to save space or transmission time. For data transmission, compression can be performed on just the data content or on the entire transmission unit (including header data) depending on a number of factors. In this study, we considered the application of an audio compression method by using text coding where audio compression represented via convert audio file to text file for reducing the time to data transfer by communication channel. Approach: we proposed two coding methods are applied to optimizing the solution by using CFG. Results: we test our application by using 4-bit coding algorithm the results of this method show not satisfy then we proposed a new approach to compress audio fil

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Publication Date
Fri Apr 24 2026
Journal Name
F1000research
Machine Learning Assisted Hybrid Cuckoo Search for Predictive Optimization in Renewable Energy Systems
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Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
New Approach in Detection MAC Spoofing in a WiFi LAN
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Medium Access Control (MAC) spoofing attacks relate to an attacker altering the manufacturer assigned MAC address to any other value. MAC spoofing attacks in Wireless Fidelity (WiFi) network are simple because of the ease of access to the tools of the MAC fraud on the Internet like MAC Makeup, and in addition to that the MAC address can be changed manually without software. MAC spoofing attacks are considered one of the most intensive attacks in the WiFi network; as result for that, many MAC spoofing detection systems were built, each of which comes with its strength and weak points. This paper logically identifies and recognizes the weak points
and masquerading paths that penetrate the up-to-date existing detection systems. Then the

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Publication Date
Sun Mar 01 2026
Journal Name
Journal Of Information Hiding And Multimedia Signal Processing
Designing a New Text Encryption Approach Based on Genetic Algorithm
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Today, data security is a major problem concerning organizations and indi- viduals. The confidentiality of information is associated with using reliable and robust encryption algorithms in systems. Cyber-attacks on data and systems have become prevalent and sophisticated, and are increasing rapidly; hence, the need for developing robust encryption algorithms is crucial nowadays. This paper proposes a new encryption algorithm using dynamic symmetric key cryptography to encrypt text files. It utilizes a secret key for encryption and decryption processes, where the key’s length is varied depending on the text size. This presented approach gives a trade-off between speed and security, making it suitable for various applications, such as secur

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Scopus
Publication Date
Thu Jan 30 2025
Journal Name
Iraqi Journal Of Science
Development of A New Colorimetric-Flow System Approach for The Determination of Cefotaxime Sodium in Pharmaceutical Formulations
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     A new colorimetric-flow injection method has been developed and validated for the detection of Cefotaxime sodium in pharmaceutical formulations. This method stands out for its rapid and sensitive nature. The formation of a brown-colored complex between Cefotaxime sodium and the Biuret reagent in a highly alkaline environment serves as the basis for the detection. The intensity of this colored complex is measured using a custom-built Continuous Flow Injection Analyzer, enabling accurate quantification of Cefotaxime sodium. Optimization studies of the chemical and physical parameters such as dilution of Biuret reagent, effect of the medium basicity, flow rate, sample loop and others have been investigated. The calibration gra

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