Efficient Data Fusion Framework for Real-Time Monitoring of Air Pressure Systems in Scania Trucks

Durdona

doi:https://doi.org/10.54216/NIF.020203

Galoitica: Journal of Mathematical Structures and Applications | 2023

(σ, τ)-Derivations on Prime Inverse Gamma Semi-Ring

The concept of inverse Γ-semiring   M is a generalization of inverse semiring. This paper investigates the concept (σ, τ)- derivation on inverse Γ-semiring and extend a few results of this map on prime inverse Γ- semiring that acts as a homomorphism or as an anti- homomorphism, where σ, τ are automorphisms on M.

groups

Sundus taha kathem mail -

Abdulrahman Hameed Majeed mail

link https://doi.org/10.54216/GJMSA.0100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

A novel multivariate copula of Raftery type with multiple dependence parameters and its neutrosophic application in finance

This paper introduces an innovative multivariate exponential distribution, specifically of Raftery type, characterized by heterogeneous dependence parameters. Various properties of this distribution family are thoroughly investigated, with particular emphasis placed on the copula derived from this model. Notably, this copula is non-exchangeable and demonstrates multiple dependence parameters. Different properties associated with this novel copula, including the examination of estimation parameters, have been thoroughly investigated. The efficacy of the proposed copula is demonstrated through its successful application in modeling a real neutrosophic dataset associated with the New York and American Stock Exchanges.

groups

Tariq Saali mail -

Mhamed Mesfioui mail -

Ani Shabri mail

link https://doi.org/10.54216/IJNS.230224

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Enhancing Security and Privacy in IoT-Based Learning with Homomorphic Encryption

The security and privacy of data in an IoT-driven intelligence landscape is a major concern. This research examines the integration of Paillier homomorphic encryption into Federated Learning to enhance security while maintaining individual data privacy in such environments. The interconnectedness of devices in IoT frameworks poses a challenge in maintaining the confidentiality of sensitive information. By using Paillier encryption within Federated Learning, this problem is solved by securing learning parameters while still keeping data private. This approach demonstrates promising improvements without violating privacy through extensive simulations and comparative analyses across different model architectures. The results of this study highlight the potential effectiveness of this method for enhancing security measures in interconnected IoT environments.

groups

Ahmed Hatip mail -

Karla Zayood mail -

Rabah Scharif mail

link https://doi.org/10.54216/IJWAC.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Ransomware Threats in Industrial Internet of Things Networks: A Detection Approach

The Industrial Internet of Things (IIoT) is a challenging environment for ransomware threats, and it requires robust detection mechanisms to protect critical infrastructures. This study explores the complex landscape of ransomware attacks in IIoT and suggests proactive detection strategies. To develop an advanced detection model, this research uses the CATBoost algorithm that can handle categorical features by leveraging a comprehensive dataset that captures various attributes of ransomware incidents. The study also enhances the interpretability of the model by incorporating SHAP (SHapley Additive exPlanations) which explains how individual features affect ransomware identification in IIoT environments. Empirical evaluation demonstrates that the model can accurately classify ransomware instances with high precision and recall rates. Moreover, SHAP explanation reveals important features that influence the decisions made by the model thereby improving its interpretability and trustworthiness. The experimental results indicate that customized detection approaches are important and highlight the effectiveness of CATBoost algorithm in strengthening IIoT systems against ransomware attacks.

groups

Ahmed Aziz mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJWAC.080102

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Crafting Resilient Consensus Mechanisms for The Web3.0 Network Through Edge Intelligence

The era of independent, secure, and scalable networks and applications that Web3.0 promised has arrived. The resilience and reliability of the network are directly tied to the architecture of the consensus mechanisms used in this context. In the paper "Crafting resilient consensus mechanisms for the Web3.0 network through edge intelligence," the authors describe a novel approach to strengthening consensus protocols by leveraging edge computing and artificial intelligence. The primary purpose of this project is to improve Web 3.0 security by implementing consensus methods based on edge intelligence. The goal of this attempt is to reduce the inefficiencies, scalability challenges, and environmental concerns associated with more conventional approaches such as proof-of-work and proof-of-stake. The proposed method combines real-time network research with local transaction verification. This eventually leads to more scalable, secure, and effective consensus procedures, which increases the resilience and greatly decreases the cost of Web3.0 networks.The proposed method recognizes the inefficiencies, lack of scalability, and environmental unfriendliness of standard consensus procedures like the Proof of Work (PoW) and Proof of Stake (PoS) consensus processes. This approach makes use of edge intelligence in real time to assess the state of the network and make appropriate adjustments in response. What emerges is a consensus process that is greener, more scalable, and more successful overall. In addition, we provide the local transaction verification (LTV) technique, which allows edge nodes to validate transactions locally, therefore reducing latency and maximizing transaction efficiency. Our findings demonstrate how edge intelligence might improve Web3.0 consensus processes. Extensive simulations and tests show that the suggested approaches outperform conventional consensus mechanisms in terms of efficiency, security, and scalability. Cost reductions for Web3.0 network operators are also emphasized to emphasize the value of our strategy. Consensus procedures for Web3.0 networks that include edge intelligence provide a viable path toward attaining the required resilience, efficiency, and scalability. This study lays the way for a new age of distributed systems, guaranteeing the resiliency and flexibility essential to the success of Web3.0.  

groups

Mustafa El-Taie mail -

Aaras Y. Kraidi mail

link https://doi.org/10.54216/IJWAC.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Threat Detection and Mitigation in the Realm of Connected Vehicle Systems

Connected Vehicle Systems (CVS) are a combination of transportation and digital technologies that have the potential to revolutionize road safety and efficiency. However, this interconnectivity exposes them to various evolving cyber threats that require proactive detection and mitigation strategies. This study examines the security threat landscape in CVS, focusing on the challenges posed by malicious intrusions, unauthorized access, and vulnerabilities within vehicular networks. By using Deep Neural Networks (DNNs) and conducting an extensive literature review on cybersecurity frameworks, autonomous vehicles, and network vulnerabilities, this research provides a robust methodology for detecting and mitigating attacks in vehicular networks. The results show that the proposed approach is effective with improved predictive capabilities as well as the ability to detect abnormal behaviors. The findings highlight the need for standardized cybersecurity frameworks, cooperation among stakeholders, and continuous improvement of security protocols to ensure safe interconnected vehicular networks in a rapidly changing technological environment.

groups

Harith Yas mail -

Manal M. Nasir mail

link https://doi.org/10.54216/IJWAC.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

The Emerging Role of Wearable Health Technologies in Proactive Disease Prevention

The study gives a complete plan for lowering disease through the use of ICT in personal healthcare. The Health Pattern Recognition (HPR), Dynamic Risk Assessment (DRA), and Personalized Intervention Strategy (PIS) formulas are all parts of this method. They are used to collect, prepare, and use data. This research focuses on cybersecurity using health pattern recognition (HPR), dynamic risk assessment (DRA), and personalized intervention strategies (PIS). PIS offers a comprehensive disease prevention approach in personal healthcare that takes advantage of technological advancements. Because they integrate secure data processing with privacy-preserving machine learning, these aspects assure the safety and validity of health data collected from wearable devices. This option allows for the assessment of medical records. It may be helpful to analyze the technique's accuracy and adherence to established security standards in order to evaluate its application for disease prediction and preventive health management. The HPR program looks at each person's health information to find trends in diseases and other results using machine learning. This helps with early evaluation and healthcare management that avoids problems. DRA keeps a person's risk rating up to date so that it takes into account any changes in their health. After that, people are given choices based on the results and risks that PIS has predicted. Some of the tests that were used to compare the suggested method to industry standards were accuracy, sensitivity, specificity, precision, and the Matthews Correlation Coefficient. The suggested way seems to work because it has better predicting power, fewer fake positives, and more users who are involved in preventive health management.

groups

Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail -

Yahia B. Hassan mail

link https://doi.org/10.54216/IJWAC.080105

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Neutrosophic and Information Fusion | 2023

Efficient Information Fusion from Environmental Sensors for Near Real-Time IoT Analytics

This research paper explores the world of nearly real time analytics by focusing on methods of combining information obtained from Environmental Sensor data. The study utilized a customized setup consisting of three arrays of sensors connected to Raspberry Pi devices. It. Analyzed a dataset that encompassed various environmental conditions. By utilizing the Random Forest algorithm this research investigated how sensor readings, including temperature, humidity, LPG concentrations, smoke, light intensity and motion detection can be fused together. The methodology used cross-validation to ensure model training while visually presenting the intricate relationships, between environmental parameters. The results demonstrated the performance of the Random Forest model through visualizations showing Out of Bag (OOB) error rates and a comparative analysis of machine learning classifiers. The findings shed light on the potential of combining information from sensors to enable reliable predictions using Environmental Sensor data. This provides a foundation for advancements in analytics and applications related to environmental monitoring.

groups

Maha Ibrahim mail

link https://doi.org/10.54216/NIF.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2023

Multi-Dimensional Sensor Fusion for Proactive Maintenance in Pump Systems

This study focuses on the task of maintenance, in pump systems by utilizing a combination of multi dimensional sensor fusion and advanced machine learning techniques. Pump systems play a role in settings but unexpected failures can lead to significant disruptions and operational inefficiencies. The goal of this research is to predict and prevent these failures effectively. To achieve this we analyzed a dataset consisting of 52 sensor units and over 220,000 readings. By applying Principal Component Analysis (PCA) we were able to extract information and reduce complexity gaining an understanding of how the pump system behaves. We then utilized Long Short Term Memory (LSTM) networks to learn from the combined sensor data enabling predictions and early detection of faults that're vital for proactive maintenance strategies. Our findings demonstrate the potential of these methodologies. The integration of sensor data sources and the use of PCA for dimensionality reduction allowed us to obtain a view while LSTM networks effectively captured the temporal dynamics present, in the sensor data leading to precise predictions regarding system behavior.

groups

Samandarboy Sulaymanov mail

link https://doi.org/10.54216/NIF.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2023

Efficient Data Fusion Framework for Real-Time Monitoring of Air Pressure Systems in Scania Trucks

Monitoring air pressure systems in heavy-duty vehicles such as Scania trucks is a key driver for operational safety and efficiency in the automotive industry. However, the complex interaction of sensors and data sources makes it difficult to quickly detect potential system failures. This problem is solved in our paper where we present a special-purpose data fusion framework for real-time monitoring of Scania trucks’ air pressure systems. To achieve this, PCA is used to reduce the size of the dataset followed by a voting classifier which combines diverse models such as Decision Trees, Random Forests, Naive Bayes, and Linear Regression using ensemble learning. In particular, our comparative analysis shows that the Voting Classifier outperforms other ML methods in terms of prediction accuracy. These findings suggest that our fusion framework can be utilized for the early detection of air pressure anomalies in heavy-duty vehicles enhancing their safety record.

groups

Durdona Bakhodirova mail

link https://doi.org/10.54216/NIF.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new