Models and Algorithms for Minimizing Errors in Managing the Consumption of Fuel and Energy Resources

Murodjon; Botirjon; Olimjon; Nodir

doi:https://doi.org/10.54216/FPA.210203

Journal of Cybersecurity and Information Management | 2026

Optimizing VANET Clustering Algorithms for 3D Urban Environments: Impact of Traffic Congestion and Driver Behavior on Network Performance

Vehicular Ad-hoc Networks (VANETs) play a crucial role in intelligent transportation systems, facilitating communication between vehicles and infrastructure in urban environments. Clustering algorithms are essential for managing network topology and enhancing communication efficiency in VANETs. The complex nature of three-dimensional (3D) urban environments, coupled with varying traffic conditions and driver behaviors, presents significant challenges for VANET clustering algorithms. Understanding these interactions is vital for developing robust and efficient VANETs. This study investigates how vehicle generation patterns, driving dynamics, and 3D road geometries influence the performance of VANET clustering algorithms in urban settings, focusing on network connectivity and stability. A comprehensive simulation framework was developed, incorporating a Traffic Generator model, a Mobility Model, and a Model of Road Curvature. The methodology evaluated clustering algorithm performance across three traffic congestion levels (low, medium, high) and three driver aggression levels for each congestion scenario. Data analysis, correlation studies, and sensitivity analysis were conducted to assess the impact of these factors on clustering efficiency. The study revealed significant correlations between traffic congestion levels, driver aggression, and clustering performance. Higher congestion levels led to more frequent cluster reconfigurations, while increased driver aggression affected the predictability of vehicle movements, affecting cluster stability. The 3D nature of urban environments introduced additional challenges, particularly in areas with elevation changes. The findings underscore the need for adaptive clustering algorithms capable of responding to dynamic urban traffic conditions. The research provides valuable insights for optimizing VANET clustering strategies in 3D urban environments, contributing to the development of more efficient and reliable vehicular communication networks for future smart cities.

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Ahmed Salih Al-Obaidi mail -

Ghaith J. Mohammed mail -

Waleed Khalid Alzubaidi mail

link https://doi.org/10.54216/JCIM.170207

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2026

Feature Selection Techniques in Intrusion Detection Systems: A Review

Intrusion detection has garnered significant attention as researchers strive to develop sophisticated models characterized by their high accuracy levels. However, the persistent challenge lies in creating reliable and effective intrusion detection systems capable of managing vast datasets under dynamic, real-time conditions. The effectiveness of such systems largely depends on the chosen detection methodologies, specifically the feature selection processes and the application of machine learning techniques. This paper offers a comprehensive review of feature selection methods employed in the realm of intrusion detection research. It examines various dimensionality reduction strategies, followed by a systematic classification of feature selection techniques to assess their impact on the training phase and subsequent detection efficacy. The focus was on the wrapper, filter feature selection methods, where the methods used were analysed, and their strengths and weaknesses were revealed. The identification and selection of the most pertinent features have been shown to significantly enhance the detection performance, not only in terms of accuracy but also in reducing computational demands, underscoring its critical importance in the architecture of intrusion detection systems.

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Ahmad Salim mail -

Obaid Salim mail -

Omar Muthanna Khudhur mail -

Shokhan M. Al-Barzinji mail -

Farah Maath Jasem mail

link https://doi.org/10.54216/JCIM.170208

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Advancing Cybersecurity in IoT: A Data-Driven Approach to Discovering Unknown Botnet Attacks

Over the years, exciting new technologies such as the Internet of Things (IoT) have changed many aspects of our lives, including smart homes. Unfortunately, this technology is vulnerable to cyber attacks owing to the lack of physical boundaries to ensure safety, privacy, and security. Botnet attacks are among the prominent cybersecurity threats because they can compromise the entire network with cyber attacks, such as distributed denial-of-service (DDoS) attacks. Hence, the intelligent discovery of new unknown botnet attacks remains a challenge, particularly in IoT environments, owing to the complex nature of the signatures of unknown botnet attacks. Through a systematic literature review, we provide a comprehensive review of current studies to determine the trends and challenges in the discovery of unknown botnet attacks. This study implemented a lightweight intelligent data-driven methodology called CySecML to discover unknown botnet attacks. The CySecML methodology differs from existing methods because of its unique data preparation and feature selection methods, specifically aimed at mitigating cyber attacks. The effectiveness of this methodology is demonstrated using state-of-the-art botnet attack data sets, where the self-training machine-learning algorithm achieved the best results with an F1-score of 94%.

groups

Innocent Mbona mail -

Jan H. P. Eloff mail

link https://doi.org/10.54216/JCIM.170209

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Assessing Quality Attributes of Microservices in Hadoop and Spark Clusters: A Performance Benchmarking Approach in Dockerized and Non-Dockerized Architectures

The rapid expansion of big data has accelerated the adoption of distributed computing frame- works such as Apache Hadoop and Apache Spark, enabling efficient large-scale data processing. While Spark’s in-memory computation model significantly enhances performance compared to Hadoop’s traditional MapReduce, the deployment architecture—whether Dockerized or non- Dockerized—plays a crucial role in affecting performance, scalability, and resource management. This study evaluates the impact of containerized and non-containerized multi-node cluster architectures on the performance of Hadoop and Spark, utilizing standardized workloads such as WordCount and TeraSort. Key performance metrics, including execution time, throughput, and resource utilization, are analyzed across various configurations with parameter tuning. Beyond pure performance benchmarking, the study also assesses the quality attributes of microservices in big data environments, focusing on scalability, maintainability, fault tolerance, and resource efficiency. The comparative analysis between monolithic and microservice-based architectures highlights the advantages of modularity and independent scaling inherent to microservices. Experimental findings indicate that Spark outperforms Hadoop on small to medium-scale workloads, while Hadoop exhibits superior robustness for processing extremely large datasets. Dockerized deployments offer better resource isolation and management flexibility, whereas non-Dockerized setups demonstrate reduced overhead under certain configurations. These insights contribute to optimizing deployment strategies and architectural decisions for microservices-based big data processing frameworks.

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Saad Hussein Abed Hamed mail -

Mondher Frikha mail -

Heni Bouhamed mail

link https://doi.org/10.54216/JISIoT.180216

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Extended EWMA Scheme for Enhanced Maxwell Process Monitoring: An Application to the Industrial Sector

The neutrosophic framework offers a promising direction for modeling data affected by uncertainty. Many quality characteristics in the production industry follow the asymmetric structure of the Maxwell distribution. The neutrosophic VSQ chart serves as a novel tool for monitoring parameters of the neutrosophic Maxwell distribution. However, the existing structure of the neutrosophic VSQ chart, based on the basic Shewhart model, is generally unable to detect small shifts in the production process. In this study, a new control chart designed following the structure of the EWMA chart is developed to efficiently monitor Maxwell-distributed neutrosophic data. The run length properties of the proposed scheme are studied, and Monte Carlo simulations are performed to investigate its statistical characteristics. Numerical results indicate that the proposed chart is effective in detecting small shifts in the process. The practical utility of the proposed chart is demonstrated through a real-world industrial dataset affected by uncertainty.

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Fuad S. Alduais mail

link https://doi.org/10.54216/IJNS.270215

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Exploring the Relationship between Social Network Structures and Emotional Contagion using NLP and Network Science

Natural Language Processing (NLP) and&ensp;Network Science were combined to study emotional contagion dynamics in social media networks. We simulated the&ensp;diffusion of emotions through users on a synthetic interaction network using sentiment-labeled Twitter data and a graph-based model. We explored the relationship between graph metrics, including centrality and clustering coefficient, on emotion&ensp;propagation and stability. The findings show that&ensp;emotion intensity converges through the network and that both weak coupling of central nodes and moderate cluster structures dampen the spread of emotion. A community-level analysis reveals more alternative&ensp;results, such as the fact that emotions differ in polarity between communities. Our work demonstrates a better understanding of how emotional behavior in online environments can be adjusted using semantic measures, which offer a&ensp;means to characterize the relevance of information online and the interconnected relationships among emotionality.

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Prapti Pandey mail -

Vivek Shukla mail -

Rohit Miri mail -

Praveen Chouksey mail -

Parul Dubey mail -

Rohit Raja mail

link https://doi.org/10.54216/JISIoT.180217

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A Two-Stage Hybrid AI Framework for Robust and Real-Time Driver Drowsiness Detection

Driver drowsiness detection is an important aspect of intelligent transportation systems that aim to reduce fatigue-related accidents. The existing schemes based on threshold-based method, or deep-learning based models often found to be associated with issues in terms of flexibility, computational efficiency, or capacity for real-time performance. This paper presents a development of two-stage hybrid framework for driver drowsy detection, where the first stage utilizes a fuzzy-logic based approach applied to physiological measures, facial feature, head position, blink duration, and eye movements to produce lightweight and adaptive analyses of sleepiness in drivers. The second stage consists of a hybrid quantum-classical neural network (HQCNN), in which convolutional neural networks (CNN) extract spatial features whereas quantum fully connected (QFC) components apply entanglement-based transformations to improve both feature characterization and classification accuracy. The experimental result validates effectiveness of the proposed hybrid method with 94% accuracy, and better than traditional CNNs with real-time capability. The proposed framework is developed to achieve a balance between computational efficiency and classification/decision quality thereby making it suitable for driver monitoring in real-time application.

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Gowrishankar Shiva Shankara Chari mail -

Jyothi Arcot Prashant mail

link https://doi.org/10.54216/JISIoT.180218

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Enhancing Osteoporosis Detection with Hybrid Fuzzy Logic Preprocessed Convolutional Neural Networks

  This paper applies deep learning techniques in classifying X-ray images to detect osteoporosis. Osteoporosis, a bone weakness condition, increases the risk of fractures; therefore, accurate early diagnosis is essential in management. We have designed a hybrid method called Fuzzy Logic Preprocessed Convolutional Neural Network, or FLPCNN, wherein fuzzy logic is used at the preprocessing step to handle uncertainty and imprecision of features extracted from X-ray images. This paper used a dataset of X-ray images, and the FLPCNN model was applied, classifying them into osteoporotic and non-osteoporotic with quite an accuracy of 100%. Fuzzy logic preprocessing combined with Convolutional Neural Networks (CNN) enhances the model’s classification accuracy and interpretable decisions. The proposed method would be a new way to cut down diagnostic errors and improve patient outcomes, opening ways for further research into deep learning techniques applied in healthcare.

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Murtadha M. Hamad mail -

Azmi Tawfeq Hussein Alrawi mail

link https://doi.org/10.54216/FPA.210201

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Barriers to E-Government Implementation in Developing Countries: A PEST Analysis of Citizens' Perceptions in Iraq

E-government implementation in developing countries faces obstacles and challenges far beyond being a simple technology., By interviewing citizens through its enhanced PEST (Political, Economic, Social, and Technological) analysis and artificial intelligence algorithms, this study systematically evaluates the experiments of Iraq to accommodate e-government service. 1,081 Iraqi citizens were surveyed using mixed methods to quantify their public acceptance and willingness of e-government services, as well as identifying the obstacles. Our investigation finds that data security (mean = 3.59-3.80), the political situation, economic distress, a lack of enthusiasm for change in society, and shortfalls of technological infrastructure are all serious challenges at present. The research used advanced statistical methods, including correlation analysis (0.634 technology-trust relationship), regression models (R ^ 2 = 0.542), factor analysis (KMO = 0.891), and Multi-Layer Perceptron (MLP) neural network algorithms achieved 89.8% prediction accuracy for e-government acceptance. The AI algorithm supported the conclusions drawn from statistical tests, with Technology Readiness and Security Perception rising up as two most significant predictors (23.4% importance for Technology Readiness and 19.8% importance for Security Perception). The findings also propose a novel methodological framework that integrates traditional statistical analysis with machine learning capabilities, rendering concrete recommendations to developing country policy makers. The study's findings imply that successful e-government implementation requires a holistic approach that factors in political, economic, social and technological aspects together. The composite PEST index score of 0.826 smells widespread resistance on the ground, although AI predictive model greatly facilitates forecasting for future e-government initiatives.

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Yaman Huthaifa Saeed Aldewachi mail

link https://doi.org/10.54216/FPA.210202

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Models and Algorithms for Minimizing Errors in Managing the Consumption of Fuel and Energy Resources

Managing fuel and energy resources (FER) efficiently is still a major challenge for energy-intensive industries like oil and gas. This paper presents a practical framework that combines mathematical models with easy-to-run algorithms to plan and control FER use in real time. Our twin goals are to cut costs and keep equipment dependable. We first outline the main parts of an energy-management system for an oil-and-gas operation, and then list the key tasks, factors, and decision criteria. The framework has two complementary paths: Path 1 relates FER use to production output via Lagrange optimization, while Path 2 fine-tunes forecasts with a simple least-squares correction based on metered data. Both paths are implemented as executable algorithms and tested on real electricity and fuel-gas datasets. The new method cuts monthly FER-planning errors by up to 80 %, reducing penalties and helping equipment last longer.

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Murodjon Sultanov mail -

Botirjon Karimov mail -

Olimjon Uralov mail -

Nodir Akbarov mail

link https://doi.org/10.54216/FPA.210203

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new