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All Journal Bulletin of Electrical Engineering and Informatics Journal La Multiapp
Mohialden, Yasmin Makki
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering

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Journal : Journal La Multiapp

 1 
Automated Chemical Equation Balancing Using the Apriori Algorithm Mohialden, Yasmin Makki; Hussien, Nadia Mahmood; Al-Rada, Walaa A Abd
Journal La Multiapp Vol. 4 No. 3 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i3.852

Abstract

Chemical equations must be balanced to maintain mass conservation. Traditional chemists employed manual processes with meticulous investigation and trial-and-error iterations. Automating and enhancing this difficult process is becoming more popular as machine learning (ML) progresses. We provide a novel Apriori algorithm-based chemical equation balancing method in this paper. Our solution uses the Apriori algorithm to find common itemsets of balanced reactions and translates unbalanced equations into machine-readable language. After that, it reconstructs balanced equations, automating a tedious task.
Enhancing User Authentication with Facial Recognition and Feature-Based Credentials Mohialden, Yasmin Makki; Hussien, Nadia Mahmood; Ali, Doaa Muhsin Abd
Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i6.903

Abstract

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition.
Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration Al Marjani, Mohammed F.; Mohammed, Rana K.; Ahmed, Ziad O.; Mohialden, Yasmin Makki
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.952

Abstract

The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.
Enhancing the Security of Information Systems Using Iot Technology Gaata, Methaq Talib; Mohialden, Yasmin Makki; Mahmood Hussien , Nadia
Journal La Multiapp Vol. 5 No. 4 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i4.1308

Abstract

Psychiatric patient information system that is used in most mental health clinics is very important in dealing with patient records. However, the safety of such systems is a major issue since information being processed in such systems is often sensitive. This paper offers a new way of boosting the security of the Mentcare information system via the incorporation of IoT technology. The following figure demonstrates the components of the proposed security framework of the system which uses highly secure password generation algorithms that enable the system to generate passwords of different levels of complexities depending on the user’s preference. Such improvements guarantee exclusive safeguard mechanisms against illegitimate access since IoT provides a way of passing secure passwords to the right individuals in real-time. That has resulted in the overall decreases in hacking attempts by the unauthorized access and enhanced the encryptions that meet GDPR and HIPAA standards and practices fully integrated with IoT technology. Also, general enhancements have been made on Mentcare system with regard to the ease and speed in generating password, system response time and user satisfaction. In light of these findings, this study reaffirms the need to have IoT-advanced security protocols for medical information systems especially in mental health care where patients’ information needs to be well protected. The conclusions prove that in addition to increasing security, the proposed system optimizes the process of its functioning, which confirms that it is necessary to apply it to protect the health care information.
Co-Authors Ahmed, Ziad O. Akawee, Mustafa Mahmood Al Marjani, Mohammed F. Al-Rada, Walaa A Abd Ali, Doaa Muhsin Abd Gaata, Methaq Talib Hussien, Nadia Mahmood Mahmood Hussien , Nadia Mohammed, Mostafa Abdulghafoor Mohammed, Rana K.