Characteristics Neutrosophic Homomorphism for Neutrosophic Rings: On Review

Shawqi; Adel

doi:https://doi.org/10.54216/PAMDA.030105

International Journal of BIM and Engineering Science | 2025

Adopting the HBIM system as a basis for preserving the architectural heritage in the city of Aleppo (AL-Matbakh al-Ajami building as a case study)

This study examines the role of Historic Building Information Modelling (HBIM) in preserving the architectural heritage of the Old City of Aleppo, focusing on a case study of the Al-Matbakh al-Ajami building. The study aims to provide an integrated framework for using HBIM for documenting and managing historical buildings. This is done through multiple stages and working according to the levels of detail by developing the 3D model from LOD200 to LOD500, which contributes to improving restoration and maintenance processes.

groups

Samah zeitouni mail -

Hala Asslan mail

link https://doi.org/10.54216/IJBES.100106

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Advanced Threat Detection in Cyber-Physical Systems using Lemurs Optimization Algorithm with Deep Learning

Cyber-physical systems (CPS) are significant to main organizations like Smart Grids and water conduct and are gradually helpless to an extensive range of developing threats. Identifying threats to CPS is of greatest significance, owing to their progressive frequent usage in numerous critical assets. Traditional safety devices like firewalls and encryption are frequently insufficient for CPS designs; the execution of Intrusion Detection Systems (IDSs) personalized for CPS is a crucial plan for safeguarding them. Artificial intelligence (AI) techniques have shown abundant probability in numerous areas of network security, mainly in network traffic observation and in the recognition of unauthorized access, misuse, or denial of network resources. IDS in CPSs and other fields namely the Internet of Things, is regularly considered through deep learning (DL) and machine learning (ML). This manuscript offers the design of an Advanced Threat Detection utilizing the Lemurs Optimization Algorithm with Deep Learning (ATD-LOADL) methodology in the CPS platform. The primary of the ATD-LOADL methodology is to focus on the recognition and classification of cyber threats in CPS. In the preliminary phase, the pre-processing of the CPS data takes place using a min-max scaler. To select an optimum set of features, the ATD-LOADL technique uses LOA as a feature selection approach. For threat detection, the ATD-LOADL algorithm uses a multi-head attention-based long short-term memory (MHA-LSTM) classifier. At last, the detection results of the MHA-LSTM method are boosted by the use of the shuffled frog leap algorithm (SFLA). The experimentation outcomes of the ATD-LOADL approach can be widely investigated on a benchmark CPS dataset. An experimentation outcome stated the enhanced threat detection results of the ATD-LOADL technique over other existing approaches

groups

Omar Ahmed Abdulkader mail -

Muhammad Jawad Ikram mail

link https://doi.org/10.54216/JCIM.150208

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Blockchain Assisted Al‐Biruni Earth Radius Optimization with Deep Learning Model for Sustainable Healthcare Disease Detection and

The cybersecurity and sustainability concepts involve safeguarding and analyzing sustainable systems, providing a versatile perspective. In the extensive data landscape of sustainable healthcare systems, ensuring diagnostic and security processes poses challenges. Healthcare disease detection using Blockchain (BC) employs BC technology to boost security and precision. This system securely shares and stores patient records through BC, fostering collaboration among researchers and healthcare providers to improve disease detection accuracy. This study designs a new BC-Assisted Al‐Biruni Earth Radius Optimization with Deep Learning Model for Sustainable Healthcare Disease Detection and Classification (BAERDL-SHDDC) technique. The BAERDL-SHDDC technique presented utilizes BC to securely store patient data and employs DL models to analyze the data for the disease detection process. For disease detection, the BAERDL-SHDDC technique involves a three-stage process namely Al‐Biruni Earth Radius (AER)-based feature selection, ensemble DL classification, and hyperparameter optimization. The hyperparameters of the ensemble DL models with fractals optimizations are optimally selected using an Adadelta optimizer. The stimulation result analysis of the BAERDL-SHDDC approach shows the guaranteeing performance of the BAERDL-SHDDC algorithm over other existing techniques with greater accuracy of 98.45%, 95.22%, and 96.49% under Heart Statlog, Pima Indian Diabetes, and EEG Eyestate databases respectively

groups

Omar Ahmed Abdulkader mail

link https://doi.org/10.54216/JCIM.150209

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Cybersecurity Attack Detection Using Multiplayer Battle Game Optimizer with Hybridization of Deep Learning Models

Cybersecurity is advancing and the rate of cybercrime, which is always rising. Advanced attacks are measured as the novel normal as they are one of the more normal and extensive. Cybersecurity threats have risen promptly in many areas like healthcare, smart homes, energy, automation, agriculture, and industrial processes. An intrusion detection system (IDS) discovers intrusions by analyzing attack designs or mining signatures from system packets. To assess an IDS model, use Machine Learning (ML) and deep learning (DL) approaches for recognizing data traffic into malicious and healthy. ML and DL techniques has earned an extensive interest on countless applications and domains of study, mostly in Cybersecurity. With computing power and hardware becoming more available, ML and DL systems can be employed in order to classify and analyze corrupt actors from a massive group of accessible data. This manuscript presents an Enhancing Detection of Cybersecurity Attack Using Multiplayer Battle Game Optimizer with Hybrid Deep Learning (EDCA-MBGOHDL) technique. The main intention of the EDCA-MBGOHDL technique is to provide a robust framework for cyberattack detection using deep learning integrated with a hyperparameter tuning approach. At first, the feature selection process is implemented by applying improved Harris hawk optimization (IHHO) algorithm for ensuring that only the most relevant features are fed into the model. Furthermore, the hybrid of convolutional neural network, bidirectional long short-term memory and attention mechanism (CNN-BiLSTM-AM) model is employed for the classification of cybersecurity threats. Eventually, the multiplayer battle game optimizer (MBGO) algorithm adjusts the hyperparameter values of the CNN-BiLSTM-AM classifier optimally and outcomes in greater classification performance. The wide range of analysis of the EDCA-MBGOHDL technique takes place using a benchmark dataset. The outcomes pointed out the superior performance of the EDCA-MBGOHDL system across existing models

groups

K. Anitha mail -

K. Rajiv Gandhi mail

link https://doi.org/10.54216/JCIM.150210

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Malicious User Recognition Using Coot Optimization Algorithm with Bayesian Belief Network for Cognitive Radio Networks

As a dynamic paradigm, Cognitive radio networks (CRNs) in wireless transmission enable devices to intelligently adapt their communication parameter based on real-world spectrum availability. Spectrum sensing lies at the core of CRNs, where nodes continue to monitor the spectrum for underutilized or unused band detection. However, the presence of malicious users (MUs) has a significant impact reliability and performance of the network. MUs detection is indispensable to prevent interference or unauthorized access and ensure network integrity. Advanced techniques combining game theory, machine learning, and signal processing are used for effectively identifying and mitigating malicious activities. CRNs can ensure efficient spectrum utilization and enhance security in heterogeneous and dynamic environments by incorporating robust MU detection systems into spectrum sensing protocols. This article presents a Malicious User Recognition using the Coot Optimization Algorithm with Bayesian Belief Network (MUR-COABBN) technique for CRN. The MUR-COABBN technique exploits metaheuristics with a Bayesian machine-learning method for the classification of the MUs in the CRN. In the MUR-COABBN technique, the COA is initially used to choose better feature subsets. Moreover, the detection of MUs can be performed by the use of BBN. Finally, the parameter tuning of the BBN model is carried out using an improved seeker optimization algorithm (ISOA). The experimental evaluation of the MUR-COABBN technique takes place with respect to distinct aspects. The experimentation outcomes implied the improved performance of the MUR-COABBN methodology with other methods under distinct measures. Therefore, the MUR-COABBN model can effectually and accurately improve security in the CRN.

groups

Rania Aboalela mail

link https://doi.org/10.54216/JCIM.150211

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic fuzzy metric spaces and fixed points results with integral contraction type

In this study, we introduce fixed point theorems related to integral type contractions, framed within the advanced context of neutrosophic fuzzy metric spaces. Additionally, we derive multiple fixed point results that are relevant to this particular setting.

groups

Anwar Bataihah mail -

Ayman A. Hazaymeh mail

link https://doi.org/10.54216/IJNS.250344

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

The Mathematical Formulas of 2-Cyclic Refined Duplets and Triplets

This work is dedicated to studying the problem of computing 2-cyclic refined neutrosophic duplets and triplets in the 2-cyclic refined neutrosophic ring of real numbers, where we present four different formulas that describe all possible duplets in this extended ring. Also, we present four different formulas for the computation of related triplets in the same ring.

groups

Josef Al Jumayel mail -

Ahmad Khaldi mail

link https://doi.org/10.54216/GJMSA.0110206

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

The Dominator Coloring of Some Graph Classes

A proper vertex coloring of a graph 𝐺(𝑉,𝐸) is an assignment of colors to the vertices of 𝐺 so that no two adjacent vertices have the same color. A dominator coloring of 𝐺 is a proper vertex coloring for which every vertex is adjacent to all the vertices of at least one color class. The minimum number of colors required to establish a proper dominator coloring on 𝐺 is called the dominator coloring number and is denoted by 𝜒𝑑(𝐺). In this paper, we determine the dominator coloring number of strong grid graphs 𝑃𝑚⊠𝑃𝑛 when 𝑚,𝑛≥3. We also determine the dominator coloring number of the Queen graph 𝑄2,𝑛 for 𝑛≥2.

groups

Ramazan Yasar mail

link https://doi.org/10.54216/GJMSA.0110207

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Irreversible k-Threshold Conversion Number of Strong Grids for k>3

An irreversible k-threshold conversion process on a graph 𝐺=(𝑉,𝐸) is a dynamic, iterative process which begins by choosing a set 𝑆0&sube;𝑉. For each step 𝑡(𝑡=1,2,…,), 𝑆𝑡 is obtained from 𝑆𝑡−1 by adjoining all vertices that have at least k neighbors in 𝑆𝑡−1. We call 𝑆0 the seed set of the k-threshold conversion process and if 𝑆𝑡=𝑉(𝐺) for some 𝑡≥0, then 𝑆0 is called an irreversible k-threshold conversion set (IkCS) of 𝐺. The k-threshold conversion number of 𝐺 (denoted by (𝐶𝑘(𝐺)) is the minimum cardinality of all the IkCSs of 𝐺. In this paper, we study Irreversible k-threshold conversion processes on strong grids 𝑃𝑚⊠𝑃𝑛. We determine 𝐶𝑘(𝑃3⊠𝑃𝑛) for 𝑘=5,6,7 and 𝐶𝑘(𝑃4⊠𝑃𝑛) for 𝑘=6,7. We also present upper bounds for 𝐶4(𝑃3⊠𝑃𝑛), 𝐶4(𝑃4⊠𝑃𝑛),𝐶5(𝑃3⊠𝑃𝑛), then we determine 𝐶8(𝑃𝑚⊠𝑃𝑛) for arbitrary 𝑚,𝑛.

groups

Ali Kassem mail -

Ramy Shaheen mail -

Suhail Mahfud mail

link https://doi.org/10.54216/GJMSA.0110208

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024