Security Inspection for Data Computing Networks Using Deep Learning Techniques

Alaa Q.

doi:https://doi.org/10.54216/JISIoT.140206

Fusion: Practice and Applications | 2025

EEG-based Epileptic Seizure Detection Using DconvNET

Epilepsy is a neural condition that is rather prevalent and affects a sizeable portion of the average population all over the world. Throughout its history, the illness has constantly be located of significant status in the pitch of biomedicine due to the dangers it poses to people's health. Electroencephalogram (EEG) recordings are a method that may be utilized to evaluate epilepsy, which is defined by the occurrence of seizures that occur repeatedly and without any apparent cause. Electroencephalography, often known as EEG, is a method that is utilized to assess the electric movement located within the brain. The examination of electroencephalogram data is an essential component in the field of epilepsy research, since it allows for the early detection of epileptic episodes. On the other hand, the generation of models that are independent of individual characteristics is a significant challenge. Extensive efforts have been directed to the creation of classifiers that are tailored to specific patients. In this thesis, the cross-patient viewpoint is the primary focus of investigation; nevertheless, the heterogeneity of EEG patterns among people presents a challenge to this investigation. An examination of the similarities and differences of the pattern recognition algorithms that are applied for the diagnosis of epileptic episodes based on EEG data was taken. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) were the approaches that were under consideration for evaluation. According to the findings of our analysis, the two approaches exhibit comparable levels of performance; however, KNN attained a slightly greater level of accuracy in some situations on occasion.

groups

Suresh Nalla mail -

Seetharam Khetavath mail

link https://doi.org/10.54216/FPA.170217

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Social Media Data Analysis for Enhancing Student Evaluation of Teaching Styles

In the realm of education, understanding the impact of different teaching styles on student engagement and satisfaction is essential. Recent advancements in sentiment analysis provide new avenues for evaluating student feedback, particularly through informal channels such as social media. While formal student evaluations offer structured feedback on teaching styles, they may not fully capture the nuanced opinions and sentiments expressed by students in informal settings, such as social media. This research aims to address the gap by integrating sentiment analysis of social media data to evaluate teaching effectiveness across various styles and comparing it with formal evaluation results. This study employs sentiment analysis using the VADER (Valence Aware Dictionary and sEntiment Reasoner) tool to analyze student posts on social media platforms. The analysis includes the extraction of sentiment distributions, identification of common keywords, and tracking of sentiment trends over time. Additionally, formal student evaluations (Likert scale) are collected to offer a direct comparison. The teaching styles analyzed include lecture-based teaching, project-based learning, flipped classrooms, online learning, hybrid learning, and traditional exam-based learning. The findings demonstrate that student sentiment varies significantly across teaching styles. Flipped classrooms and project-based learning received the highest positive sentiment scores, while traditional exam-based teaching showed the most negative sentiment. Social media feedback tended to align with formal evaluations for certain teaching styles, such as the flipped classroom and hybrid learning but showed divergence in others, like online learning, which received higher sentiment in social media feedback. Trends over time reveal evolving sentiments, with fluctuating satisfaction as the academic semester progressed. The integration of social media sentiment analysis provides a more dynamic and real-time understanding of student experiences, offering deeper insights into teaching style effectiveness.

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Walaa Fouda mail -

Najla M. Alnaqbi mail -

Sanjar Mirzaliev mail -

Dina Sabry Said mail

link https://doi.org/10.54216/FPA.170218

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Implementation of the Neutrosophic Sets in Measurable Space with Respect to Neutrosophic Ring

The generalization for interval fuzzy set name as neutrosophic set employed to construct a measurable space in this work. The measurable space with respect to a ring of sets that is closed under difference and union, is studied. The objective of this study is to extend the notion of a ring of sets by using neutrosophic sets. Neutrosophic set concept has gained popularity in various fields of mathematics, probability, and other sciences due to its many uses, especially when dealing with uncertainties. Several different properties of neutrosophic ring are studied. Examples and characterizations to the proposed extension are given.

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Ibrahim S. Ahmed mail -

Ali Al-Fayadh mail -

Hassan H. Ebrahim mail -

Luma S. Abdalbaqi mail

link https://doi.org/10.54216/IJNS.250317

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Weather Prediction: Predicting Rain Using Weather Conditions

Weather forecasting is a major discipline that plays an important role in fields such as agriculture, transport, and emergency management, and it largely depends on accurate forecasts. Concerning this problem, this work aimed to analyze the effectiveness of recurrent neural networks, particularly the Long Short-Term Memory (LSTM), for estimating rainfall depending on precipitation, maximum temperature, minimum temperature, and wind speed. We will therefore use a large database containing recorded weather data obtained over several years to calibrate accurate predictive models designed to distinguish between drizzle, rain, sun, snow, and fog. The main idea of the work is to teach LSTM models that are capable of revealing temporal relations and patterns in sequential data, which makes them suitable to work on various time series forecasting such as weather prediction. The data is preprocessed effectively to clean it and make it ideal for our analysis to accurately compare the performance of one model against the others, we have divided the data into training, validation, and testing sets. The concurrency of the proposed LSTM model is then evaluated with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to measure the forecasting accuracy. The findings show a better predictive performance uplift whereby the best-performing LSTM model has an MSE of 8.74, RMSE of 2.96, MAE of 2.35, and R² of 0.83. Such metrics represent logical dependence between the predicted and actual weather conditions, proving thus the efficiency of the model. Also, the evaluation shows how hyper parameters’ optimization, features’ selection, and normalization, make a huge difference in the model’s performance and indicate that the precise management of weather parameters can result in better forecasts. However, the contributors of this research are not recluded to theoretical perspective; the present study can be useful for various subjects since the dependability of weather forecasts can be improved. They will be advantaged to have more precise weather data for crop growing, road networks, and other transport systems to prepare for the worst conditions, and emergency, rescue operations to be in a better position to handle certain disasters. Consequently, this study improves the academic literature on weather peculiarities with unforeseen downpours through a demonstration and explanation of the potential of LSTM networks to analyze key meteorological characteristics for rainfall prediction. Possible future study directions are outlined, proposing the expansion of features beyond those analyzed in the existing study to improve the predictive models, the usage of continuous rather than weekly data, as well as considering the mixed-ingredients approach for increasing the prediction accuracy. This inclusive strategy seeks to enhance the realistic stages in the phased meteorological prognosis and also timely resource allocation and management tactics within climate volatility.

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Khaled Sh. Gaber mail -

Mohamed Abd Elmonem Elsebaey mail -

Ahmed Al-Sayed Ibrahim mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Capsule Networks for Rice Leaf Disease Classification

Deep Learning is a high-performance machine learning approach that combines supervised machine learning and feature learning. It is built of a sophisticated models with numerous hidden layers and neurons to create advanced image processing models. DL has proven its effectiveness and resilient in different fields including big data, computer vision, image processing, and many others. In agriculture, rice leaf infections are a frequent and pervasive issue that lower crop and output. This research proposed a reduced form of Capsule Network (Caps NET), a form convolutional neural network, for the classification of rice leaf disease. The goal of the suggested Caps NET model was to assess the suitability of various feature learning models and enhance deep learning models' capacity to learn about rice leaf disease classification. Caps NET was fed images of both healthy and infected leaves. High classification performance was obtained with the ideal configuration (FC1 (960), FC2 (768), and FC3 (4096)), which had 96.66% accuracy, 97.25% sensitivity, and 97.49% specificity.

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Eman Turki Mahdi mail -

Wijdan Jaber AL-kubaisy mail -

Maha Mahmood mail

link https://doi.org/10.54216/JISIoT.140201

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation

This study presents a new adaptive routing protocol for fire emergencies, leveraging a newly created dataset and a hybrid deep learning approach to optimize decision-making and data routing strategies. The developed protocol integrates a hybrid of Convolutional Neural Networks (CNNs) with Bi-Directional Long Short-Term Memory (BiLSTMs) deep learning models to predict fires at early stages, effectively managing the dynamic and unpredictable nature of fire emergencies to prevent data loss and ensure packet delivery to the base station. Exhaustive validation was conducted utilizing the standard protocol to ensure the reliability and effectiveness of the proposed approach. Experimental results demonstrate the superior performance of the proposed hybrid-deep learning model and the significant enhancements in routing efficiency and monitored data preservation for the developed protocol compared to the standard protocol. The findings are useful in providing a reliable solution for adaptive routing during emergencies.

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Ola Khudhair Abbas mail -

Fairuz Abdullah mail -

Nurul Asyikin Mohamed Radzi mail -

Aymen Dawood Salman mail

link https://doi.org/10.54216/JISIoT.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Intrusion Detection System in Wireless Sensor Networks Using Machine Learning

Current industrial control systems are increasingly integrating with corporate Internet technology networks in order to fully utilize the abundant resources available on the Internet. The growing connection between industrial control systems and the internet has made them a desirable choice. Industrial control systems are in need of significant protection due to being a common target for a range of cyber-attacks. The use of the Internet of Things is currently increasing across industries due to its efficiency, and the Internet of Things is facing a security challenge. This document gives an overview of the intrusion detection system and the methods of the intrusion detection system. The purpose of this document is to examine intrusion detection methods and present the best method based on studies. Experimental results show that this system uses a combination of machine learning methods for high performance.

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Zainab S. Idan mail -

Ahmed Al-Fatlawi mail -

Hussein Akeel Hussein Alaasam mail -

Sajjad H. Hasan mail -

Ahmed Ali Talib Al Khazaali mail

link https://doi.org/10.54216/JISIoT.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Reduce Energy Consumption and Increase Lifetime via Genetic Algorithm over Wireless Communication Networks

Wireless sensor networks have been identified as one of the most important technologies. A vast amount of research and development has been devoted to this area in the past decade. Nowadays, they have been applied in various fields including environment monitoring, smart building, medical care, and etc. With the advances in electronics, wireless communications, and sensor technology, more and more new opportunities have been created for the research in wireless sensor networks. However, the successful implementation of WSN faces many challenges, such as limited power, limited memory, and limited computing capability. Among them, limited power is the most critical restriction because it is usually impossible for the battery-powered sensor nodes to be recharged. Therefore, one of the main areas of interest for wireless sensor network research is how to reduce power consumption. The proposed system classifies sensor nodes into two operational modes, optimizes node deployment, and finds optimal node placements using a genetic algorithm (GA) to minimize the energy consumption of the WSN. The system's successful testing on a simulated WSN meant for radiation site identification revealed its potential for practical real-world applications.

groups

Mohammed Arif Nadhom Obaid Al-agar mail -

Zaynab Saeed Hameed mail -

Israa Ali Al-Neami mail -

Sergey Drominko mail -

Erina Kovachiskaya mail

link https://doi.org/10.54216/JISIoT.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Smart Home Cloud Monitoring Design and Investigation Using Artificial Intelligence Strategies

Artificial intelligence (computer-based intelligence) is advancing significantly in all areas and applications of life at a high speed. The use of modern technologies has become a necessity in daily life, and smart systems have entered daily life, especially in the design of smart homes. Smart homes linked to man-made intelligence mimic the way residents live and facilitate many activities and services. Although some studies have shown how smart homes use computer-based intelligence, few applications have been reported for integrating smart technologies into installation and use of the Internet of Things. In this research, the basic problems in adaptive smart home systems, such as the development of the smart home and its synchronization with the Internet of Things, and “what is the relationship between analysis and adaptation in smart homes with simulation of intelligence algorithms” were addressed to be the focal point of this paper. Moreover, this study aims to depict the capabilities and elements of artificial intelligence in improving the performance of smart homes. In order to understand how to use artificial intelligence to build smart homes, the precise situation of applying artificial intelligence in smart home elements and the way applications are used in homes was determined. We simulated a multi-service smart home environment by designing an efficient, multi-purpose artificial intelligence algorithm to improve the control level and enhance the performance of smart home services.

groups

Hiba A.Tarish mail

link https://doi.org/10.54216/JISIoT.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Security Inspection for Data Computing Networks Using Deep Learning Techniques

Deep learning offers practical answers for neural network models when applied to cloud registering security. Via robotization Distinguish dangers, decrease manual checking, and further develop in general security adequacy. Deep learning network models assume a pivotal part in security errands like interruption discovery, malware identification, anomaly recognition, and log examination. requires Deep Learning mix in cloud security cautiously assesses existing frameworks, characterizes goals, chooses dataset with arrangement, model tuning and last changes for consistence. Moreover, applying deep learning methods in cloud security requires thought of variables, for example, computational assets, information assortment, arrangement costs, model turn of events, mix endeavors, and continuous observing and support. This study proposes an artificial neural network (ANN) model portrayal in the cloud to track down cloud security parts and recreate security techniques and researches the essential moves toward coordinate these models in the cloud. Regarding that the adequacy of the ANN scheme relies upon cloud parameters like the nature of the preparation information and the network architecture Also, weight change calculations. The review emploies a dataset from Kaggle.com to approve the recreation and blueprints the means Partake in preparing and assessment of the ANN structure.

groups

Alaa Q. Raheema mail

link https://doi.org/10.54216/JISIoT.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new