Multi-criteria group decision making method in Pythagorean interval-valued neutrosophic fuzzy soft soft using VIKOR approach

M.; Aiyared; Said; Lejo J; K.

doi:https://doi.org/10.54216/IJNS.220108

Journal of Intelligent Systems and Internet of Things | 2023

Application of Edge Computing-Based Information-Centric Networking in Smart Cities

 Many data resources and network availability are needed for the smart city applications to execute at their highest efficiency level Many interconnected devices in a smart city produce vast quantities of data and will likely have new uses in the near future. In smart cities, the Internet of Things (IoT) and 5G beyond networks provide dependable, large-scale data exchange and communication. A new intelligent ecosystem is the goal of 5G, and the technology that will make it possible is the next-gen networking technologies. The drawback of smart devices is their limited computational capability. Adding in-network caching into information-centric edge networks allows them to overcome this obstacle. Hence, this study suggests an Adaptive Information-Centric Network based on Edge Computing Framework (AICN-ECF) to reduce data traffic and latency with high security in smart cities. Integrating EC and ICN allows content distribution to be handled quickly, improving user experience. This study provides an ICN-based edge caching system with four cache attributes for managing large multimedia data traffic in smart cities built on the Internet of Things. At the base station (BS) application layer, there is support for ICN and device-to-device (D2D) communication, which allows for caching of requested material at the network's edge. This layered design is the first step in the process. Secondly, to facilitate efficient caching, a selection has been offered to cache contents at network nodes in a layered network design, considering a variety of centrality indicators. Finally, this study provides a method for caching material close to the delivery path in ICN network layers, allowing for rapid content distribution by using near-path caching. The experimental findings demonstrate that the suggested AICN-ECF model increases the cache hit ratio of 98.7%, content retrieval time of 97.8%, data security ratio of 96.5%, data transmission ratio of 95.6% and delay ratio of 11.2% compared to other popular models.

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Munqith Saleem mail -

Hanan Burhan Saadon mail -

Marwa S. Mahdi Hussin mail -

Tamarah Alaa Diame mail -

Raaid Alubady mail -

Mohd K. Abd Ghani mail -

Hatıra Günerhan mail

link https://doi.org/10.54216/JISIoT.080208

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Blockchain-based e-Medical Record and Data Security Service Management based on IoMT resource

The confidentiality of electronic medical records (E-Medical records) is of the utmost importance. Consequently, healthcare companies are responsible for ensuring their patients' medical records' privacy, security, and service management. Innovative agreements will ensure patient satisfaction for management. The study's primary goals are to enhance data security service management and reduce the amount of external involvement with healthcare data. This study explores a novel approach to improve the security and confidentiality of e-medical information by examining the feasibility of utilizing the blockchain system within the context of the IoMT (Internet of Medical Things). The medical care management platform uses blockchain technology to manage e-health records effectively. This paper presents a paradigm for e-medical record services based on IoMT resources, which integrates blockchain technology with Secure Federated Learning (BT-SFL-IoMT). The data is stored on blockchain, and predictions and analyses are made using secure federated learning. Hyper ledger Analyzer is used to assess the latency and speed of blockchain transactions and capture access activity and authorization events. As verified by the results, the functionality is resistant to unauthorized retrievals and fits the needs of real-world settings while securing e-medical records. Many metrics, including testing accuracy of federated learning, Convergence speed, and Performance analysis of the proposed model, demonstrate its efficient use in secure databases.

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Raaid Alubady mail -

Rawan A.shlaka mail -

Hussein Alaa Diame mail -

Sarah Ali Abdulkareem mail -

Ragheed Hussam mail -

Sahar Yassine mail -

Venkatesan Rajinikanth mail

link https://doi.org/10.54216/JISIoT.080207

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Predictive Maintenance in IoT: Early Fault Detection and Failure Prediction in Industrial Equipment

The Industrial Internet of Things (IoT) has ushered in a new era of predictive maintenance, revolutionizing the way industries manage and maintain their critical equipment. This paper presents a comprehensive exploration of predictive maintenance strategies, with a primary focus on early fault detection and classification in industrial equipment. We introduce the "Triplet Fault Injection Algorithm," capable of injecting three distinct fault types—spike, bias, and stuck—into sensor data for realistic and rigorous testing. Leveraging this algorithm, we employ the powerful Extreme Gradient Boosting (XGBoost) machine learning approach to detect and classify these faults. Our experimental results showcase the superiority of XGBoost over baseline machine learning methods, across various data types commonly found in industrial equipment. The consistent higher accuracy and F1 scores obtained with XGBoost underscore its effectiveness in minimizing false alarms and enhancing the reliability of early fault detection. Moreover, we discuss the transformative role of IoT in predictive maintenance, highlighting its potential to optimize equipment performance and reduce downtime in the industry 4.0 landscape. This paper contributes valuable insights and empirical evidence to the domain of predictive maintenance in IoT-enabled industries, emphasizing the significance of early fault detection for efficient and cost-effective maintenance practices.

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Reem Atassi mail -

Fuad Alhosban mail

link https://doi.org/10.54216/JISIoT.090217

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2023

Hybrid Alpha Power Marshall Olkin G Class of Distributions

This research presents a new class of probability distributions derived as a hybrid class between alpha power transformation class and Marshall Olkin G class and we call it the hybrid alpha power Marshall Olkin G class of distributions (HAPMOG). Characteristics properties of this new class were derived including moments, moments generating function, characteristic function, reliability and hazard functions, and its probability density function was presented in linear combination. Also, many generated distributions depending on this new class was presented and well-studied including HAPMOG-Exponential, HAPMOG-Weibull, HAPMOG-Freshet. This new class of distributions helps in modelling new forms of data, which has important applications in engineering, communication systems, networks modeling, etc.

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Fatimah Taher mail -

Mohamed Bisher Zeina mail -

Moustafa Mazhar Ranneh mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset

Increase in network activity of transferring information online allows network breeches where intruders easily avail the most important information or data. The growth of online functioning and many other governmental data over the internet without security has caused data vulnerability; attackers can easily detect the data and misuse them. Network Intrusion Detection System (NIDS) has allowed this whole process of online data transfer to occur safely and secured transactions. Due to the cloud usage in network the huge amount of traffic is created as well as number of attacks are increased day by day. To prevent the vulnerability and its types are social, environmental, cognitive, military attacks in the network are classified using CRNN model.  We used ensemble learning methods in machine learning algorithms are used to detect and prevent the malicious packets in the network. Our model detects the unauthorized users intruding into any network and alerts the organization regarding the same. When a typical firewall is unable to effectively stop certain sorts of attacks on computer system usage and network communications, a network intrusion detection system may be used. First, we are classifying the unauthorized packets using machine learning algorithm. Using our concept, we have used neural networks in this paper to detect any such attack. For the Network Security Laboratory - Knowledge Discovery in Databases data set using CNN and RNN algorithms, we also applied a few well-known techniques as boosting and pasting methods. In this CRNN approach, we demonstrate that neural networks are more effective than other methods at detecting attacks.

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Manjunath H. mail -

Saravana Kumar mail

link https://doi.org/10.54216/FPA.130109

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Privacy-Enhanced Digital twin Framework for Smart Livestock Management: A Federated Learning Approach with Privacy-Preserving Hybrid Aggregation (PPHA)

Through its integration with the Federated Learning (FL) and Digital Twin (DT) technology, Internet of Things (IoT) based smart livestock farming is revolutionized toward real-time health monitoring and predictive analytics combined with secure decision-making. Privacy risks, inefficient models, large computational overheads, and heterogeneous data remain prominent in existing frameworks. This work introduces a “Privacy-Enhanced Digital Twin Livestock Optimization (PEDLO)” system, combining several adaptive and AI-driven components, including IntelliSense-Livestock Monitoring Framework (ISLMF) for multi-sensor data fusion, Privacy-Preserving Hybrid Aggregation (PPHA) Algorithm for secure federated learning, and Digital Twin-Augmented Reinforcement Learning (DTARL) for simulation-based decision-making. The PEDLO system optimizes disease prediction and anomaly detection, aims to reduce false alarms, and ensures data privacy for enhanced livestock welfare. Experimental results show 0.94 of accuracy, 0.93 of anomaly detection sensitivity, and a 40-second model convergence time, which outperform state-of-the-art techniques by a wide margin. The proposed system will enable scalable, efficient, and secure livestock management, marking a transformative shift toward sustainable precision farming.

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Adel A. Alyoubi mail

link https://doi.org/10.54216/JISIoT.160101

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection

The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation. However, this interconnected landscape also presents unique security challenges, necessitating robust intrusion detection mechanisms. In this research, we present a comprehensive study of anomaly detection in IoT networks, leveraging advanced machine learning techniques. Specifically, we employ the Gated Recurrent Unit (GRU) architecture as the backbone network to capture temporal dependencies within IoT traffic. Furthermore, our approach embraces hierarchical federated training to ensure scalability and privacy preservation across distributed IoT devices. Our experimental design encompasses public IoT datasets, facilitating rigorous evaluation of the model's performance and adaptability. Results indicate that our GRU-based model excels in identifying a spectrum of attacks, from Distributed Denial of Service (DDoS) incursions to SQL injection attempts. Visualizations of learning curves, Receiver Operating Characteristic (ROC) curves, and confusion matrices offer insights into the model's learning process, discriminatory power, and classification performance. Our findings contribute to the evolving landscape of IoT security, offering a roadmap for enhancing the resilience of interconnected systems in an era of increasing connectivity.

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Reem Atassi mail

link https://doi.org/10.54216/FPA.130110

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Test Design Optimisation of factors and levels by Covering double and triple mode Combinations using Orthogonal Array test strategies and Random Forest Algorithm

Testing is a process of trying to find out every believable fault or weakness in a project. In today’s world, software products and components play a vital part in our life. Software testing is a world, it contains its own life cycle consists of the following stages – Requirements, Test Plan, Test Design, Test Execution, Defect reporting/tracking. The core of software testing lies in writing test cases based on specifications. Software testers play a vital role writing the test cases during test design phase of software testing life cycle. Research have proved that writing test cases is the most time killing and challenging activity among other testing life cycle phases. It is very crucial to sequence and write optimized test cases to increase the rate of fault identification during test design phase as early as possible. There are various proven test design techniques available which focuses on optimizing test cases in different test stages. Our key focus in this paper is to identify the optimized test cases minimizing the actual number of test cases with minimal effort using OATS (Orthogonal Array Test Strategy) techniques covering double mode and triple mode test combinations and Random Forest algorithm.

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S. Malathi mail -

M. Sangeetha mail -

Faiyaz Ahmad mail -

Saravanan M. S. mail -

T. Kalachelvi mail

link https://doi.org/10.54216/FPA.130111

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2023

The Role of Internet of Things in Smart City Environmental Monitoring: A Pollution Detection System

Smart cities represent a transformative vision of urban living, where technology seamlessly integrates with the urban landscape to enhance sustainability and quality of life. Central to this vision is the effective management of environmental factors, particularly air quality and temperature. This paper presents a comprehensive study on real-time environmental pollution detection within smart cities, utilizing Internet of Things (IoT) sensors. We explore the intricate relationships between air pollutant indicators (o3_AQI, no2_AQI, co_AQI, and pm25_AQI) and temperature, shedding light on the dynamic interactions that underlie urban atmospheric conditions. Our research employs a robust dataset and employs statistical analysis, including Ordinary Least Squares (OLS) regression, to uncover significant correlations between key environmental variables. These insights not only contribute to a deeper understanding of urban pollution dynamics but also enable the development of predictive models for temperature fluctuations based on pollutant levels. Such models hold promise for proactive environmental management and public health interventions. Furthermore, our study highlights the pivotal role of IoT sensors in revolutionizing smart city governance, offering real-time data-driven solutions for sustainable urban living. As cities worldwide strive to enhance their environmental resilience, this research provides valuable insights and tools for harnessing the potential of IoT technologies in the pursuit of cleaner and more livable urban environments.

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Rasha Almajed mail -

Abedallah Zaid Abualkishik mail -

Laiali Almazaydeh mail -

Sameh Ghwanmeh mail

link https://doi.org/10.54216/JISIoT.090218

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2023

Multi-criteria group decision making method in Pythagorean interval-valued neutrosophic fuzzy soft soft using VIKOR approach

In contrast to the Pythagorean interval valued fuzzy soft set and the neutrosophic interval valued fuzzy soft set, the Pythagorean neutrosophic interval valued fuzzy soft set is a generalization of these sets. We discuss aggregating PyNIVFS decision matrixes by using aggregated operations. The VIKOR method, which is an extension of neutrosophic fuzzy soft sets, is a powerful method for evaluating multi-criteria group decision making. The score function in this approach is based on the aggregation of the VIKOR method to a PyNIVFSpositive and negative solution. Optimal alternatives are introduced under closeness. An investment company plans to purchase some shares of the top five companies in the stock exchange to invest some money on. In order to minimize this factor, they decided to invest some of their cash in percentages of 30 dollars, 25 dollars, 20 dollars, 15 dollars and

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M. Palanikumar mail -

Aiyared Iampan mail -

Said Broumi mail -

Lejo J Manavalan mail -

K. Sundareswari mail

link https://doi.org/10.54216/IJNS.220108

Volume & Issue

Vol. Volume 22 / Iss. Issue 1

Details open_in_new