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Strengthening cloud data protection based on a novel cyber security framework
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Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the IRF algorithm. It optimizes feature importance weights during training and improves the model's ability to detect intrusions in cloud environments accurately. The proposed detection model is implemented in Python software. In the findings assessment phase, we effectively assessed the performance of our proposed DB-IRF in detecting earthquake incidents across multiple evaluation metrics such as Accuracy (97.5%), Precision (97.96%), F1 Score (98.48%) and Recall (97.85%). We also conducted a comparison analysis with other conventional methodologies. Our experimental results demonstrate the capability and reliability of the recommended framework.

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Publication Date
Tue Jun 14 2022
Journal Name
Iraqi Journal Of Industrial Research
Biofuel Production and Its Impact on Global Food Security: A Review Article
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The aim of this study is to shed light on the importance of biofuels as an alternative to conventional energy, in addition to the importance of preserving agricultural crops, which are the main source of this fuel, to maintain food security, especially in developing countries. The increase in global oil prices, in addition to the fear of global warming, are among the main factors that draw the world’s attention to searching for alternative sources of traditional energy, which are sustainable on the one hand, and on the other hand reduce carbon emissions. Therefore, the volume of global investment in renewable energy in general, and in liquid biofuels and biomass in particular, has increased. Global fears emerged that the excessive

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Publication Date
Sat Nov 05 2022
Journal Name
Sensors
Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption
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Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using si

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Publication Date
Tue Sep 30 2025
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Geomechanical properties evaluation of Mauddud formation based on experimental measurements and well log data
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    Mauddud formation is one of the most prominent formations in Northeastern Iraq due to its significant hydrocarbon reserves, making accurate geomechanical characterization essential for safe drilling operations and informed development planning. This study constructs a calibrated post-drill one dimensional mechanical earth model (1D-MEM) for selected wells, levering Techlog software to integrate rock mechanical data, image logs, multi-arm caliper measurements, conventional well logs, drilling reports, and core analyses. The methodology provides a detailed workflow for estimating geomechanical properties from log and image analysis to model calibration. Validation of the 1-D MEM performed through cross-comparison with direct me

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computing
Twitter Location-Based Data: Evaluating the Methods of Data Collection Provided by Twitter Api
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Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based dat

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Publication Date
Wed May 01 2024
Journal Name
Journal Of Drug Delivery Science And Technology
Antibacterial and wound healing performance of a novel electrospun nanofibers based on polymethyl-methacrylate/gelatin impregnated with different content of propolis
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Publication Date
Thu Feb 01 2024
Journal Name
International Journal Of Biological Macromolecules
A novel designed nanofibrous mat based on hydroxypropyl methyl cellulose incorporating mango peel extract for potential use in wound care system
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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of Electronic System Based on Cloud Computing to Develop Electronic Tasks for Students of the University of Mosul
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The present research aims to design an electronic system based on cloud computing to develop electronic tasks for students of the University of Mosul. Achieving this goal required designing an electronic system that includes all theoretical information, applied procedures, instructions, orders for computer programs, and identifying its effectiveness in developing Electronic tasks for students of the University of Mosul. Accordingly, the researchers formulated three hypotheses related to the cognitive and performance aspects of the electronic tasks. To verify the research hypotheses, a sample of (91) students is intentionally chosen from the research community, represented by the students of the college of education for humanities and col

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Publication Date
Sat Jun 01 2024
Journal Name
Iaes International Journal Of Artificial Intelligence (ij-ai)
A novel fusion-based approach for the classification of packets in wireless body area networks
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This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

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Publication Date
Tue Jul 24 2018
Journal Name
Sensors
Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees
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Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa

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