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

Rania

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

International Journal of BIM and Engineering Science | 2025

Development Knowledge Graphs for Intelligent Curriculum Design in Education with Artificial Intelligence

Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insights, KGCD offers educators a powerful tool for designing adaptive, personalized curricula that align with student needs and educational goals. The system provides real-time suggestions for curriculum adjustments, ensuring the inclusion of relevant content and logical sequencing of topics. Initial pilot studies demonstrate KGCD’s potential to improve curriculum coherence and student learning outcomes by providing data-driven support for curriculum development and revision.

groups

S. Sakena Benazer mail -

Haritima Mishra mail -

A. Babiyola mail

link https://doi.org/10.54216/IJBES.100101

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Dynamic Learning-Driven Software Ecosystems: Revolutionizing Healthcare Solutions through Real-Time Adaptation

The increasing demand for personalized, efficient, and adaptive healthcare solutions has catalyzed the development of dynamic, learning-driven software ecosystems. This paper introduces a novel framework that leverages real-time data and machine learning algorithms to revolutionize healthcare services. The proposed system integrates continuous learning capabilities to enhance decision-making, optimize resource allocation, and enable precise diagnostics and treatment plans. By incorporating real-time data from patient monitoring systems, electronic health records, and IoT-enabled devices, the ecosystem offers adaptable healthcare solutions that evolve based on new data insights. The adaptability and scalability of the proposed framework ensure that healthcare providers can offer timely and personalized interventions while minimizing operational costs. Key features include dynamic learning models, predictive analytics, and seamless integration with existing healthcare infrastructures. Through extensive case studies, the paper demonstrates how these innovations can transform patient care, improve outcomes, and support proactive healthcare management.

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Jacinth salome mail -

Kowsalyadevi Krishnaraj mail -

Chandra Sekar P. mail -

Tatiraju V. Rajani Kanth mail

link https://doi.org/10.54216/IJBES.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Machine Learning-Enhanced Wireless Sensor Networks for Real-Time Environmental Monitoring

Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events, improving data reliability and reducing network load. Experimental evaluations in a simulated environment show a 40% improvement in anomaly detection accuracy and a 35% reduction in data redundancy. Furthermore, this framework achieved a 25% increase in energy efficiency, enhancing network longevity. This machine learning-optimized WSN framework provides an effective solution for continuous environmental monitoring in applications such as wildlife tracking, pollution control, and smart agriculture.

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Tatiraju V. Rajani Kanth mail -

K. Dhineshkumar mail -

Haritima Mishra mail -

Chandra Sekar P. mail

link https://doi.org/10.54216/IJBES.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

IoT-Based Smart Agricultural Monitoring Using WSN and Predictive Analytics with Artificial Intelligence (AI)

Smart agriculture leverages Internet of Things (IoT) technology to improve crop yield, resource efficiency, and environmental sustainability. This study presents an IoT-based smart agricultural monitoring system that integrates Wireless Sensor Networks (WSNs) with predictive analytics to monitor key environmental parameters, such as soil moisture, temperature, humidity, and light intensity, in real-time. The system utilizes WSNs to gather data from distributed sensor nodes and employs machine learning models for predictive analytics, enabling proactive decision-making for optimized irrigation, fertilization, and pest control. Experimental results demonstrate that the proposed system enhances resource usage by 40% and increases crop yield by 30% compared to traditional farming methods with Artificial Intelligence (AI). Additionally, the predictive analytics component achieves an accuracy of 92% in forecasting environmental conditions, aiding in timely interventions and minimizing crop stress. This IoT-based solution supports sustainable farming practices and offers scalability for various agricultural applications, including precision farming and greenhouse monitoring.

groups

K. Dhineshkumar mail -

Tatiraju V. Rajani Kanth mail -

A. Babiyola mail -

Haritima Mishra mail

link https://doi.org/10.54216/IJBES.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Design Change Management using BIM and Autodesk Construction Cloud

Efficient change order management is crucial in construction, particularly as project requirements evolve over time. In Syria's traditional construction process, lengthy gaps between planning, design, and execution significantly increase the likelihood of changes. This paper introduces a methodology that leverages Building Information Modeling (BIM) and cloud computing to enhance change management. A detailed case study of the Al-Eddekhar Housing project in Tartous was conducted, where Revit was employed for 3D modeling and Primavera for scheduling and cost estimation. Changes were meticulously analyzed using Revit's Model Compare tool, tracked through Primavera, and managed using Autodesk Construction Cloud for seamless document exchange and version control. The integration of BIM and cloud computing facilitates real-time collaboration between teams, significantly reducing errors, minimizing delays, and boosting overall project efficiency. The platform also preserves a historical record of project versions, enables visual comparisons of 3D models, and streamlines the approval process for change orders.

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Hiba Rai mail -

Lama Saoud mail

link https://doi.org/10.54216/IJBES.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

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