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All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering
Ali Salah, Hussein
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Distributed Denial-of-Service Attack Detection Using One-Dimensional Convolutional Neural Network in Airline Reservation Systems (ARS) Kareem Gharkan , Dhurgham; Kareem Mohammed, Bahaa; Ali Salah, Hussein; Mocanu, Mariana
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.202

Abstract

A prevalent and perilous in the contemporary are Distributed Denial of Service (DDoS) attacks. in which attackers attempted to prevent authorized users from accessing internet services by deploying many attack workstations. This research presents a detection approach based on One Dimension Convolutional Neural Networks, which has created an innovative approach for detecting DDoS attacks that addresses the limitations of conventional methods. The primary objective of this study was to analyze and detect DDoS attacks through the examination of a dataset about the booking of airline tickets. The present investigation utilized the APA-DDoS dataset, comprising two discrete categories: benign traffic and DDoS traffic. Wireshark was utilized to simulate airline data as well. Utilized as one-dimension convolutional neural network (1D CNN) technology, the model achieved an accuracy rating of 99.5%. The experimental outcomes demonstrated that the proposed model effectively and consistently identified DDoS attacks. Solid ability to differentiate between legitimate and malicious traffic has been exhibited by the system, thereby ensuring network security.
Enhancement Infrared, Visible, Manganic Resonance Imaging and Computed Tomography: A Comparative Study Jasim Alhamdane, Haider; Nickray, Mohsen; Ali Salah, Hussein
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3155

Abstract

Images are merged to produce a single image with increased image quality and the integrity of key characteristics while combining complementing multi-temporal, multi-view, and multi-sensor information. The goal of the study is to enhance the focus of three wavelet transform methods, namely image fusion based on discrete wavelet transform, stationary wavelet transform, and dual tree-complex wavelet transform, in order to improve the quality of medical images such as computed tomography images, magnetic resonance images, and the quality of merging visible images and infrared images using the technique of image fusion based on wavelet transform. The fuzzy histogram equalization method, the Lucy-Richardson algorithm, the recovery of the pictures prior to the fusion process, and the convolutional filters based on linear spatial filters were all utilized for the optimization process. Seven scales were employed in the study to evaluate the performance effectiveness of the suggested strategies and to contrast them with the conventional approaches. The results showed that, when compared to the other methods, image fusion based dual tree-complex wavelet transform and spatial filters produced the best results. This paper discusses numerous state-of-the-art image fusion techniques at various levels, along with their benefits and drawbacks, as well as various spatial and transform-based techniques with quality measures and their applications in many fields. This review has finished with a number of future directions for various image fusion applications.
Modeling, Implementing and Evaluation a Decision Support System Used for Choosing the Best HVAC System in The Buildings, Case Study in Iraq Ahmed, Ahmed Shihab; Ali Salah, Hussein
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1947

Abstract

The life cycle cost of a building is affected by the heating, ventilation, and air conditioning (HVAC) system chosen by the Life Cycle Costs (LCC). Quality, constructability, appearance of the structure's interior and exterior, HVAC size and weight, and LCC are some of the criteria influencing the choice. Methods: To monitor a project's progress based on energy savings, standard measures such as cost variance (CV) and schedule variation have used an idea when tracking the performance of intelligent buildings. Also, as described in the article, this research compared the decision-making limits of Building Information Modelling (BIM) and (MCDM). Analysis: The conventional approach cannot reveal information regarding divergence from the expected level of performance. Based on the outcomes of the construction cost variables, the key finding was the observation of 12 efficient elements. Finding and Novelty: According to the R, a building's most valuable features are its (Energy Saving Features, Warranties, Budget, Protect Your Unit, SEER Ratings, and Home Square Footage). The findings of Actual value (AV) and planned value (PV) were significantly different, as noted by the Multi-Criteria Decision Maker (MCDM). The new method also makes it possible to track project costs and timetables more accurately. The paper will characterize the HVAC Decision Support System's architecture (HVACDSS). Also, a case study of action modeling is provided, and the preliminary findings are addressed. Six criteria characteristics are used by the HVACDSS technique by an analysis of building construction conducted using the WEKA mining tool (decision tree).
Towards an Integrated Decision Support System for the Evaluation Data Mining Tools in Economic Intelligence System Ali Salah, Hussein; Shihab Ahmed, Ahmed; Bhar Layeb, Safa
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3635

Abstract

Information is very important in the economic world because it affects how industries make choices in today's fast-growing economy. It's essential to learn how to find valuable knowledge for the company's operations quickly. The goal of this work is to create a database of socioeconomic intelligence, operationalize it as a system, and use the study's results to make better decisions. Building economic understanding processes requires research into economic analysis algorithms and the development of computational representations for financial systems. This information is used to construct knowledge item architecture for financial reasoning systems. This study employs data mining methods to assess and extract relationships among dataset elements. Association rules and forecasting techniques are used to quickly and accurately retrieve relevant data for the financial intelligence sector. The research examines the application of financial intelligence mechanisms via data mining methodologies. The article discusses the dataset and reveals that the suggested algorithm's classification accuracy surpasses that of the Logistic Regression (LR) technique by 2.76%. This illustrates the efficacy of the devised system in obtaining and analyzing economic intelligence data. Research on sophisticated algorithms and their use in financial intelligence platforms could improve the precision and effectiveness of data collection and analysis. The results of this study provide a basis for enhancing financial decision-making and underscore the potential for further innovation in this field.
Co-Authors Ahmed, Ahmed Shihab Bhar Layeb, Safa Jasim Alhamdane, Haider Kareem Gharkan , Dhurgham Kareem Mohammed, Bahaa Mocanu, Mariana Nickray, Mohsen Shihab Ahmed, Ahmed