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

Adel A. Alyoubi

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

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent system for Distributed Quality Monitoring of Sewage Management based on Wastewater Treatment Procedure and Data Mining

Wastewater treatment procedures (WWTP) rely heavily on accurate forecasting of treatment results to keep oxygenation levels under control. Conventional biochemical mechanism-driven approaches provide poor results, mainly due to complicated and redundant system factors. As sewage treatment operations expand fast, automated operational solutions are needed to achieve this goal. In the research, data mining was used to model the WWTP to predict the outcomes based on input circumstances and the amount of oxygenation provided to the system. Combined Sustainability Research for Wastewater Treatment procedures (CSR-WWTP) is proposed in this research. Data-driven approaches to modeling WWTP have already been developed but do not consider long-term treatment procedures and structure features. Forecasting and management for the WWTP are described in this article using a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). The first stage utilizes the CNN structure to dynamically learn and encrypt the local features of each WWTP timestamp in the first phase. The RNN model is applied to the WWTP to express global sequence characteristics using local feature encryption. For this purpose, it conducts a huge number of tests to assess the performance and accuracy of the proposed forecasting framework.

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Shaid Sheel mail -

Sarmad Jaafar Naser mail -

Hussein Alaa Diame mail -

Noor Baqir Hassan mail -

Naseer Ali Hussien mail -

Seifedine Kadry mail

link https://doi.org/10.54216/JISIoT.090215

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Data Management and Decision-Making Process Using Machine Learning Approach for Enterprises

Currently, Machine Learning (ML) seems very attractive since it may speed up business functions in enterprises, lower costs for supplying goods and services, and manage information to promote enterprise efficiency. Essential technological domains nowadays are the explosive period of growth in enterprise solutions, which are progressively used in almost all business platforms. The ML sessions will receive a thorough summary, and the relevant organizations will be shown procedures for relevant business processes. The data management unit is already been striving to solve related issues in ML applications for more than a generation, creating numerous customized analytical techniques. The approach described in the study uses a weighted directed graph displayed in an industrial environment to identify the core part of the neural network structure and then trains them using the relevant data source. The article proposed ML-assisted Enterprise Data Management (ML-EDM) for identifying the trade-off between ML growth in the financial sector and its consequences in precision and interpretability. According to the experimental findings, the ratio of AI for decision-making is 84.25%, the Speed and Agility proportion is 92.70%, the result of Earlier Prediction Management is 93.80%, the  Infrastructure Development is 85.46%, with Data Efficiency is 84.5% and Performance efficiency of the system is 90.14%.

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Tamarah Alaa Diame mail -

M. Abdul Jaleel M. mail -

Sajad Ali Ettyem mail -

Raaid Alubady mail -

Mohaned Adile mail -

Mohd K. Abd Ghani mail -

Hatıra Günerhan mail

link https://doi.org/10.54216/JISIoT.080107

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2023

Chili Leaf Disease Detection Using Deep Feature Extraction

Diseases in crops lead to decreased production, which can be addressed through consistent surveillance. Manual surveillance of crop diseases is both arduous and prone to mistakes. The timely identification of crop leaf diseases using Computer Vision and Artificial Intelligence can aid in minimizing the negative impact of diseases and address the limitations of continuous human surveillance.  To classify chili crop diseases, this research paper introduces a new deep feature extraction model based on Transfer Learning using ResNet50, MobileNet, EfficientNetB0, and multiple classifiers. On Plant Village dataset related to the diseases of the chili crop and Private data set, the proposed method was trained and tested. And also analyzed the results by comparing the performance of the pre-trained deep learning models on original data and data filtered through the Image filtering mechanisms and proposed method on the plant village dataset and private dataset, the highest performance accuracy is 99.6% with ResNet50 and the faster CPU time for feature extraction is 29.3 seconds using MobileNet. Comparing the suggested model to the most advanced deep learning models reveals greater accuracy with fewer computational resources.

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Pallepati Vasavi mail -

A. Punitha mail -

T. VenkatNarayana Rao mail

link https://doi.org/10.54216/JISIoT.090216

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices

Today, every nation strives for international recognition in a variety of sports. Governments invest in games and sports to raise the performance of their teams and athletes to get notoriety. Numerous people are involved in sports execution, including team management, coaches, and biomechanists who monitor athlete fitness and work to achieve remarkable results. Performance analysis is greatly aided by technological integration in sports management. The performance analysis of athletes is evaluated in this research using an upgraded machine learning approach on Improved Machine Learning approach on Wearable Devices (IMLA-WD). This design strategy utilizes wearable devices to collect health data, which is then fed into a machine-learning model to monitor athletes' progress. The athletes' performance is evaluated using standard machine learning methods, and the deep neural network monitors their health status. With a health prediction accuracy of 98.65%, the statistical findings of the proposed model demonstrate the highest performance compared to existing methodologies.

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Maryam Ghassan Majeed mail -

Hawraa Ali Sabah mail -

Mustafa Nazar Dawood mail -

Mohaned Adile mail -

Noor Hanoon Haroon mail -

Mariok Jojoal mail -

Ahmed Mollah Khan mail

link https://doi.org/10.54216/JISIoT.080108

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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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

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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

Details open_in_new