Machine Learning Models with Statistical Analysis Techniques for ForecastingWind Turbines Scada Systems Measurement

Mona Ahmed; El-Sayed M.; Mohamed Gamal; Islam; Hossam El.Deen Salah

doi:https://doi.org/10.54216/FPA.190205

Journal of Intelligent Systems and Internet of Things | 2025

Cat-Feed-Nets: A Novel Cat Evoked Deep Feedforward Networks for Detection of Dos Attacks in IoT-Cloud Environment

Internet of things (IoT) is an intelligent combination of embedded systems, cloud computing and wireless communications. However, the data privacy and leakage problems are considered as the major deadlocks for deploying the IoT devices in the real time fields. Nevertheless, the complication of Distributed Denial of Service (DDoS) hazard on the IoT devices recent surge has seen an uptick, making it prone to numerous threat complications. For this reason, prompt detection of these attacks plays a pivotal role to safeguard the user’s data. The AI methodology of Machine and Deep Learning Models engaged for the designing the intelligent systems to provide the secured environment to safeguard the network against the various attacks. However, the computational overhead of deep learning model handicaps to deploy it in the IoT-Cloud environment. To tackle this issue, the present article suggests the novel hybrid learning based detection system called CAT-FEED-NETS that incorporates the Deep feed forward neural networks (DFFNN) where the hyper parameters are tuned by the Cat Swarm Intelligence Algorithms. Comprehensive trials and analysis are performed using NSL-KDD and UNSW datasets and criteria to assess the efficacy of quality measurements such as accuracy, precision recall, F1-score and model building time (MBT) is evaluated and analysed. Evaluation results are weighted against the various DL algorithms with the suggested model exhibiting better results than the other models by producing 0.96 accuracy, 0.956 precision, 0.955 recall and 0.9834s of MBT respectively. The proposed framework had proved its superiority in predicting the cloud attacks than the other existing frameworks.

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P. Jagdish Kumar mail -

S. Neduncheliyan mail

link https://doi.org/10.54216/JISIoT.160117

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

On Developing a Temporally Ordered Energy Efficient Routing Model (TO-EER) using Bio-Inspired Optimization for MANET

Mobile Ad-hoc Network is a structure of dynamic cellular network devices with no fixed architecture. Due to the network's constantly changing environment, characterized by frequent changes in its topology routing becomes a major challenge in MANET, which can reduce the overall network efficacy. As routing protocol plays a vital role in MANET, the energy-efficient routing model can enhance network longevity with a minimal rate of energy consumption. This paper uses a Temporally Ordered Routing Algorithm (TORA) to attain a higher scalability rate and an Elephant Herding Optimization (EHO) model to employ energy-efficient routing protocol features. The computations of the proposed model include the length of the route (LR) in optimal route selection and the energy level of routes (ER). It devises the routing problem as an optimization issue and further incorporates EHO for route selection, enhancing the weighted rate of LR and ER. The experimentations are carried out using the NS-3 simulation tool and factors such as latency, packet success rate, throughput, reliability, and energy depletion rate. Through a comparative analysis of the results with the previous works, the effectiveness of the proposed model is demonstrated.

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Hemalatha M. mail -

M. Mohanraj mail

link https://doi.org/10.54216/JISIoT.160118

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

A Study of Some Important Algorithms Used in the Process of Generating Random Numbers

The efforts of many researchers and scholars have focused on providing appropriate algorithms for generating random numbers and developing them in a manner that suits the need for them, but these algorithms still have advantages and disadvantages, so they are suitable for a specific study and not suitable for another study. The reason for the interest of researchers and scholars in the process of generating random numbers is that random numbers have many scientific and technical applications, starting with generating a series of semi-random numbers, starting from computer simulation to encryption, games of chance, and random samples for statistics and security. In simulation, which is one of the important methods provided by the new science of operations research, the primary reliance is on generating a series of random numbers that follow the regular distribution in the range [0,1], and then converting these random numbers into random variables that follow the probability distribution according to which the system to be simulated works, as the accuracy of the results we obtain from the simulation process depends on the numbers we generate using one of the algorithms. In other words, the appropriate algorithm for the field of study must be chosen from among the algorithms used, which prompted us to prepare this research, through which we will present a reference study of some of the algorithms used to generate random numbers. Where we will highlight the advantages and disadvantages of these algorithms and the most important areas of their use. Then we will calculate the number of these algorithms and compare them. The algorithms that we will discuss in this research are: ➢Middle Square Method. ➢Middle multi-Method. ➢Fibonacci Methods. ➢Linear congruential Methods.

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Sawsan Rateb almokabaa mail -

Maissam Ahamad Jdid mail

link https://doi.org/10.54216/PAMDA.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Optimized Convolutional Neural Network for Alzheimer’s disease Detection

Alzheimer’s disease (AD) is a serious diseases distressing society. AD is a complex disease associated with many risk factors, such as aging, genetics, head trauma, and vascular disease. AD is also influenced by environmental factors such as heavy metals and trace metals. The pathology of AD, including amyloid-peptide (Aβ) protein, neurofibrillary tangles (NFTs), and synaptic loss, is still unknown. There are many explanations for the causes of AD. Cholinergic dysfunction is a main danger factor for Alzheimer's disease, whereas others believe that abnormalities in the production and treating of Aβ protein are the primary cause. However, there is currently no accepted hypothesis explaining the pathogenesis of AD. Magnetic resonance imaging is used to diagnose Alzheimer's disease. Our new AD pathogenesis showed 99.77% accuracy with 0.2% efficiency loss and outperformed VGG16, MobileNet2, and Inception V3 without the Adam optimizer and folder hierarchy.

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Amena Mahmoud mail -

Abdulaziz Shehab mail -

A. S. Abohamama mail -

Esraa Al-Ezaly mail

link https://doi.org/10.54216/JISIoT.160119

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Integrating Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis

Pancreatic cancer (PC) is an extremely malignant cancer type with a maximum rate of mortality. It remains a challenging form of tumor to treat due to its late analysis and aggressive nature, which drastically decreases the survival rate. Early analysis of PC is vital for enhancing the probabilities of treatment and survival. PC analysis was initially dependent upon imaging, and then the recent imaging offered a worse prognosis, restraining clinicians’ treatment choices. PC detection utilizing deep learning (DL) contains the application of advanced computational methods for analyzing medical image data like CT scans or MRI images, for the early and correct detection of PCs. DL approaches, particularly convolutional neural networks (CNNs), are trained on huge databases for diagnosing forms and anomalies indicative of PC. Therefore, this study presents a novel Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis (COADL-MIAPCD) technique. The main objective of the COADL-MIAPCD approach is to proficiently examine the medical images for the detection of PC. The COADL-MIAPCD technique primarily applies a median filtering (MF) for image pre-processing. In addition, the COADL-MIAPCD approach allowed using of an improved SE-ResNet. Moreover, the COA has been utilized for the optimum parameter choice of the improved SE-ResNet. At last, the extreme learning machine (ELM) has been used for the recognition and classification of PCs. The simulation outcomes of the COADL-MIAPCD technique has been validated utilizing a medical image database. The obtained experimental values stated that COADL-MIAPCD technique achieves better performance than other models.

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Eiman Talal Alharby mail

link https://doi.org/10.54216/FPA.190119

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel Smart Cities Framework for GCC Countries

There is a need to create and develop smart cities that could help improve the quality of life in global countries. The goal of this paper is to develop a novel smart city framework for the GCC countries. This study presents a comprehensive analysis of smart city features across multiple cities worldwide, leveraging data from a reliable world cities database. Through exploratory data analysis and visualization techniques, we examined various aspects of smart city development, including mobility, environment, government, economy, people, and living standards. It turned out from the literature that globally, there is a focus on some of the dimensions of smart cities while others did not receive much attention. Smart economy and smart environment were not receiving much attention globally. A framework was developed for the GCC countries that focuses on all the dimensions of the smart cities, but most of the attention is on smart governance and smart economy since these two dimensions help improve the quality of life and diversify the sources of the economy in the country. This framework is useful for GCC countries as it would have great implications on the desired outcomes of smart cities and link with the strategic development goals that most GCC countries have, whether it is the 2030, 2035, or even the 2040 vision.

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Khawla Alhasan mail

link https://doi.org/10.54216/FPA.190120

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Natural Language Processing Driven Applied Linguistics for Sarcasm Detection Using Artificial Hummingbird Algorithm with Deep Learning

Natural Language Processing (NLP)-driven applied linguistics for sarcasm detection includes computational models to understand and identify sarcastic expressions within text. This interdisciplinary method integrates linguistics principles with advanced NLP techniques to identify subtle and nuanced cues indicative of sarcasm correctly. It includes computational approaches like linguistic feature extraction, machine learning models, and sentiment analysis. Furthermore, deep learning (DL) algorithms, including transformers and recurrent neural networks (RNNs), hold significant potential in capturing complex linguistic nuances inherent in sarcastic expression. These approaches can learn the hierarchical representation of text, which enables capturing context dependency, which is crucial for accurately detecting sarcasm. The applications of NLP-driven applied linguistics for sarcasm detection show great potential in various domains namely social media analysis, online content moderation, and customer feedback interpretation. By automating sarcasm detection, this system can enhance communication understanding, improve sentiment analysis accuracy, and contribute to better decision-making processes in various contexts. This study develops automated Sarcasm Detection using the Artificial Hummingbird Algorithm with Deep Learning (ASD-AHADL) technique. The ASD-AHADL technique applies the optimal DL model for detecting sarcastic content. To achieve this, the ASD-AHADL technique undergoes data preprocessing and the BERT-based word embedding process at the initial stage. Followed by the ASD-AHADL technique uses attention-gated recurrent unit long short-term memory (AGRU-LSTM) for the sarcasm detection process. At last, the AHA-based parameter tuning process is involved to fine-tune the parameters based on the DL algorithm. The experimental study of the ASD-AHADL technique has been tested under a social media dataset. The outcomes indicated that the solution of the ASD-AHADL technique was significant compared to others.

groups

Maryam Alsolami mail

link https://doi.org/10.54216/JISIoT.160120

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Innovations in Health Anomaly Detection: A Comparative Review of Machine Learning and Statistical Approaches

One of the significant challenges in modern healthcare is the early and accurate detection of health anomalies, especially in the case of life-threatening diseases such as breast cancer. This paper investigates the comparative efficacy of ML models and statistical methods for the classification of breast tumors as benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Dataset. The dataset, comprising various tumor cell attributes, was preprocessed with Principal Component Analysis (PCA) to enhance model training efficiency. The first 11 principal components retained 95% of the total variance, ensuring minimal information loss while reducing dimensionality. We compared the performance of several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Decision Trees (DT), Random Forests (RF), Naïve Bayes (NB), and K-Nearest Neighbors (KNN). Among them, Logistic Regression, SVM, and ANN achieved near-perfect classification accuracy with balanced precision-recall metrics, where the accuracy rates were all more than 98%. XGBoost and Random Forest were also very impressive as advanced models, while simple models like Decision Trees and Naïve Bayes proved to be less potent and were unable to manage class imbalances and complex data patterns. Our main findings are essentially reflective of the transformative role machine learning would play in healthcare; for instance, enhancing the accuracy of diagnosis, optimizing clinical workflow, and promoting decision-making. These insights are made actionable for practitioners in healthcare to promote the adoption of reliable ML solutions for breast cancer detection. In the future, real-time data integration, external validation, and hybrid modeling approaches must be considered to further enhance the practical utility of these findings.

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Nada M. Sallam mail -

Eman Ben Salah mail

link https://doi.org/10.54216/FPA.190203

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Hybrid chaotic bat artificial bee colony algorithm assisted hybrid machine learning based intrusion detection system

Network intrusions are becoming more common, resulting in significant privacy violations, financial losses, and the illegal transfer of sensitive information. Numerous intrusion strategies pose a threat to data, computer resources, and networks. While hackers may focus on obtaining trade secrets, private information, or confidential data that can then be disclosed for illegal purposes, each type of intrusion aims to achieve a distinct objective. False attack detection by security systems and changing threat environments create challenges such as delayed identification of true attacks and long-term financial harm. This paper presents a novel hybrid optimization algorithm-assisted deep learning model for accurately identifying intrusion types and enhancing network security. Initially, input information is composed from openly obtainable datasets. The input data is cleaned, normalized, and standardized to produce accurate results. An improved synthetic minority oversampling technique (ISMOTE) for data balance reduces the method's overfitting problem. Then, the Chaotic Bat Artificial Bee Colony optimization algorithm (CBABCOA) is used to identify critical features and reduce feature dimensionality issues. HSVM-XGBoost (Hybrid Kernel Support Vector Machine-Extreme Gradient Boosting) is used for intrusion detection and classification. The Chaotic Binary Horse Optimization Algorithm (CBHOA) is used for hyper parameter tuning. This method makes use of the CIC UNSW-NB15 Augmented dataset, the CICIDS 2019 data set, and the NSL-KDD information set. The proposed method achieves better than the other method.

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Vasanth Nayak mail -

Sumathi Pawar mail -

Sunil Kumar B. L. mail

link https://doi.org/10.54216/FPA.190204

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning Models with Statistical Analysis Techniques for ForecastingWind Turbines Scada Systems Measurement

Wind energy is one of the fastest-growing sustainable, clean, and renewable sources, attracting significant attention and investment from many countries. However, given the substantial capital investment required for wind power plants, understanding the proposed plants’ performance becomes critical before implementation. This assessment is most effectively conducted using refined wind power predictability models and precise wind velocity data. Accurate wind forecasts are essential for informed decision-making and effective wind energy utilization. In this study, three advanced Machine Learning (ML) regression methods were applied to the TNWind dataset to predict the power output of wind turbines. The dataset variables included date and time (measured at 10-minute intervals), low-voltage active power (in kW), wind speed (in m/s), the theoretical wind power curve (in kWh), and wind direction. To predict wind power output, six supervised ML models were trained, including Random Forest Regressor (RF), Extreme Gradient Boosting Regressor (XGB), Gradient Boosting Regressor (GB), Support Vector Machine Regressor (SVR), K-Neighbors Regressor (KN), and Linear Regressor. The analysis revealed that the Random Forest model outperformed the others, achieving exceptional performance metrics: an R2 value of 0.97, an MAE of 0.17 and an MSE of 0.07. The analysis to identify the outcomes for wind power generation from machine learning proves that renewable energies are more capable and are a lucrative investment.

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Mona Ahmed Yassen mail -

El-Sayed M. El-Kenawy mail -

Mohamed Gamal Abdel-Fattah mail -

Islam Ismael mail -

Hossam El.Deen Salah Mostafa mail

link https://doi.org/10.54216/FPA.190205

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new