A Fuzzy Approach for Congestion Avoidance in FANET and IoT

Mahendra; Priti; Vinay Kumar

doi:https://doi.org/10.54216/JISIoT.140115

Journal of Artificial Intelligence and Metaheuristics | 2024

Predictive Modeling of Global Educational Outcomes: A Comparative Analysis Using Machine Learning Regression Techniques

Education contributes a crucial portion to the world’s development; thus, it is crucial to focus on education enrollment and quality education. It is essential not only that children enroll in school but also that they receive proper education to improve individuals and, consequently, society. This paper aims to use machine learning to predict educational outcomes based on the World Educational Data obtained from Kaggle to analyze the data, preprocess it, and evaluate the performances of the different regression models. The following models consist of Support Vector Regression (SVR), CatBoost, RandomForestRegressor, ExtraTreesRegressor, GBoost, MLPRegressor, GradientBoosting Regressor, DecisionTreeRegressor, KNeighborsRegressor, LinearRegression, and Pipeline. Evaluation measures used included MSE, RMSE, MAE, MBE, r, R2, NSE, and WI. Analyzing the performance comparison, the best accuracy was associated with CatBoost with an r value equal to 0.999996 and an R2 value of 0. 999993; The MSE score was 0.04024. The outcomes of the present paper demonstrate that the application of advanced machine learning algorithms can be used effectively to predict educational outcomes, thus enabling policymakers and educational planners to use them for designing effective educational policies and overcoming existing global challenges in the sphere of education.

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Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Ehab M. Almetwally mail

link https://doi.org/10.54216/JAIM.070206

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Securing DNS over HTTPS: A Machine Learning Study on Traffic Classification Using DoHBrw-2020

This paper provides a detailed review of related works for classifying secure DNS traffic, with emphasis on the identification of threats relating to DoH using machine learning algorithms. In the present study, with the help of DoHBrw-2020 dataset consisting the network traffic data of DoH protocol during its testing phase, we compare the performance of various machine learning algorithms: Decision Tree, SVM, KNN, Na¨ıve Bayes, Neural Network (MLP), Gradient Boosting, and SVM with RBF kernel. As for each model, we have Accuracy, Sensitivity, Specificity, Positive Predicted Value, Negative Predicted Value, and F Score. They reveal the fact that the chosen Decision Tree model produces the highest accuracy and equals to 99. 65% and all the criteria of the assessment should be well managed. It is important that the various machine learning methods contribute to the study’s discovery of high potential in improving DNS traffic security and offers an understanding on the best models to use for real-time detection of DoH threats. From these outcomes, it can draw many perspectives to the further creation and implementation of safer DNS solutions within contemporary information security paradigms.

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Al-Seyday.T. Qenawy mail -

Hussein Alkattan mail -

Amany Khaled mail

link https://doi.org/10.54216/JAIM.070207

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Numerical Proceduers for Computing the Exact Solutions to Systems of Ordinary Differential Equations

This paper presents a modified homotopy perturbation method (HPM), which aimed at solving systems of ordinary differential equations (ODEs). The MHPM, which combines the HPM, Laplace transform, and Padé approximants, offers an alternative approach to address the challenges associated with solving such problems. By employing this method, it becomes feasible to overcome these challenges and obtain a dependable approximation for the exact solution. The effectiveness and applicability of the proposed scheme are demonstrated through preliminary results derived from illustrative examples, all of which correspond to exact solutions.

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Nidal Anakira mail -

Osama Oqilat mail -

Adel Almalki mail -

Irianto Irianto mail -

Saad Meqdad mail -

Ala Amourah mail

link https://doi.org/10.54216/IJNS.250214

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Evaluating the Effectiveness of Physical Rehabilitation Exercises through RehabNet++ and Hybrid Optimization Techniques

This article focuses on improving the accuracy and efficiency of multimodal human motion analysis using advanced techniques. Initially, Generative Adversarial Networks (GANs) were used for skeletal enhancement, and then Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied on the enhanced images to check the quality Joint-level. Limb-level, Temporal, Statistical Features are effectively recovered from contrast enhancing images. Furthermore, with the selected optimal features acquired from PutterFish Customized Serval Optimizer (PFCSO), the RehabNet++ architecture that makes the human movement assessment has been trained. This PFCSO model has been developed based on the inspiration acquired from the Pufferfish Optimization Algorithm (POA) and the Serval Optimization algorithm (SOA), respectively. The RehabNet++ architecture includes an optimized Multilayer Perceptron (O-MLP), STR-ResNet architecture, Attention-based Convolutional Neural Networks and Transfer Learning. The O-MLP model has been formulated by optimizing the hidden layers of MLP using the PFCSO model. In addition, Grad-CAM visualization is included to provide a graphical description for model selection. A comparative study has been conducted to test the proposed deep learning algorithm against the original methods using the Kimore dataset. This analysis is implemented in PYTHON and is dedicated to multimodal human motion analysis.

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Osamah A. Altammami mail

link https://doi.org/10.54216/FPA.170112

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Biometrics Applied to Forensics Exploring New Frontiers in Criminal Identification

Different biological data may be used to identify people in this investigation. The system uses complex multimodal fusion, feature extraction, classification, template matching, adjustable thresholding, and more. A trustworthy multimodal feature vector (B) is created using the Multimodal Fusion Algorithm from voice, face, and fingerprint data. The key objectives are weighing, normalizing, and extracting characteristics. Complex feature extraction algorithms improve this vector and ensure its accuracy and reliability. Hamming distance is utilized in template matching for accuracy. Support vector machines to ensure classification accuracy. The adaptive threshold technique adjusts option limits based on the biology score mean and standard deviation when external conditions change. A thorough look at the research shows how algorithms operate together and how vital each aspect is for locating criminals. Change the multimodal fusion weights for optimum results. Thorough research using tables and photographs revealed that the fingerprint approach is optimal. Fast, simple, and precise technologies may enable new unlawful recognition tools. The adaptive thresholding algorithm's multiple adaptation steps allow the system to adjust to diverse study circumstances. The Multimodal Biometric Identification System is a cutting-edge leader in its area and provides a trustworthy, practical, and customizable research choice. This novel strategy is at the forefront of criminal recognition technology and has been supported by ablation research. It affects reliability, accuracy, and adaptability.

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Ajay Kushwaha mail -

Tushar Kumar Pandey mail -

B. Laxmi Kantha mail -

Prashant Kumar Shukla mail -

Sheo Kumar mail -

Rajesh Tiwari mail

link https://doi.org/10.54216/JCIM.150122

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

The Challenge of Adversarial Attacks on AI-Driven Cybersecurity Systems

As AI is deployed increasingly in defensive systems, hostile assaults have increased. AI-driven defensive systems are vulnerable to attacks that exploit flaws. This article examines the approaches used to resist AI-based cybersecurity systems and their effects on security. This paper examines existing literature and case studies to demonstrate how attackers modify AI models. These include avoidance, poisoning, and data-driven assaults. It also considers data breaches, system failures, and unauthorized access if a hostile effort succeeds. The report recommends adversarial training, model testing, and input sanitization to address these issues. It also stresses the need for monitoring and updating AI algorithms to adapt to changing opponent tactics. This paper emphasizes the need to limit hostile strike threats using real-life examples and statistics. To defend AI-driven cybersecurity systems from complex threats, cybersecurity specialists, AI researchers, and policymakers must collaborate across domains. This article provides full guidance for cybersecurity and AI professionals. It describes the complex issues adversarial assaults create and proposes a flexible and robust architecture to safeguard AI-driven cybersecurity systems from emerging threats.

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M. N. V Kiranbabu mail -

A. Jeraldine Viji mail -

Amit Kumar Chandanan mail -

Vijay Birchha mail -

Tushar Kumar Pandey mail -

Sumit Kumar Sar mail

link https://doi.org/10.54216/JCIM.150123

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Hybrid Temporal Lambda Layer Embedded in Autoencoder Neural Network for Fake News Detection

Many social media applications use different animated or morphed images to make fake news viral. Recognition of text from images for their classification as real or fake requires a neural network. BERT (Bidirectional Encoder Representation Transformer) or MLP-based (Multi-Layer Perceptron) algorithms are successful when working with textual data alone. However, the system needs to extract the sequential text from the images to identify the semantic meaning of the content before the classification process. The dataset utilized was acquired from The Indian Fake News Dataset (IFND) contains text and visual data from 2013 to 2021. The data includes both visual and textual information, as well as 126k data points obtained from millions of users. In the proposed model, a squeezed lambda is implemented to process the data in the three forms of verbal tenses, i.e., past to future and future to past. In the lambda layer, temporal classification is performed by applying two bidirectional LSTM (Long Short Term Memory) layers based on the retuning sequences of the character list available in the dataset. It also computes the batch cost of every iteration and reduces them based on the ratio of prediction and input class labels available. To ensure that the suggested technique is more accurate than the current approach, a validation was undertaken, resulting in a +0.5 increase in accuracy over the BERT (Bidirectional Encoder Representation Transformer) model. Hence, the proposed method has achieved higher accuracy than existing algorithms. Than existing algorithms.

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T. V. Divya mail -

Figlu Mohanty mail

link https://doi.org/10.54216/JCIM.150124

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Predictive Modeling of Muscular Performance and Fitness Progression using Artificial Intelligence

This study presents a novel approach to predictive modeling of muscular performance and fitness progression using artificial intelligence techniques. Leveraging advanced machine learning algorithms, including artificial neural networks (ANN), support vector machines (SVM), and gradient boosting machines (GBM), we develop a comprehensive model capable of accurately forecasting key metrics related to muscular strength, endurance, and overall fitness. Extensive experimentation and evaluation demonstrate the superiority of the proposed method over existing algorithms across a range of performance metrics, including accuracy, precision, recall, F1-score, and error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our findings highlight the importance of feature selection techniques and model hyperparameter optimization in driving predictive performance, underscoring the need for careful model development and tuning. The practical implications of our research extend to sports science and athletic training, where the proposed method can inform personalized training strategies tailored to individual athletes' needs and goals. Moving forward, further research is needed to validate the robustness and generalizability of the proposed method across different populations and athletic disciplines, as well as to explore its integration with real-time data sources for more dynamic and responsive training programs.

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Manshuralhudlori mail -

Agus Kristiyanto mail -

Rony Syaifullah mail -

Febriani Fajar Ekawati mail -

Slamet Riyadi mail -

Fadilah Umar mail

link https://doi.org/10.54216/FPA.170113

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Enhancing Financial Fraud Detection using Temporal Patter Mining Technique

Examining the temporal behavior of common patterns, obtaining appropriate clusters, and reducing the size of discovered patterns are three significant challenges in temporal data mining. Among the available methods, the constraint-based pattern mining approach has achieved remarkable progress in this domain. Apriori and Interleaved algorithms, which are both slow and outdated, are nonetheless used by present time-granularity pattern exploration approaches. To address these issues, we propose the Frequent Pattern Growth method with Special Constraints. The system incorporates a method for generating patterns on a regular basis. It mandates that transactional datasets adhere to complete and partial cyclic criteria. To locate all possible periodic patterns within the Spatio temporal database, we redefine the task as periodic pattern mining in this thesis. The proposed method makes use of a periodic pattern tree miner. To begin, the clustering method uses an innovative global pollination artificial fish swarm technique to create the most effective dense clusters.

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Ahmed Aziz mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJAACI.060206

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Fuzzy Approach for Congestion Avoidance in FANET and IoT

In the recent era of communication technology, flying ad hoc networks are gaining popularity because of their flexibility and broad area of application to gather data from environmental sources with limited infrastructure. FANET nodes, or unmanned aerial vehicles (UAVs), are heterogeneous devices, and coordination between the UAVs is an important part of communication with limited battery power sources. In ad hoc networks, devices have limited battery power, so proper battery utilization is critical to maintaining network connectivity. In order to establish a network without congestion, it is vital to have inter-UAV and IoT wireless communication for cooperation and collaboration among many UAVs. UAV connections may experience frequent disconnections. Another obstacle is the limited distance allowed between the stations. The routing algorithm selects only the nodes that are specifically requested by the source node based on its requirements and maintains the source node no longer needs the route until it. IoT devices have limited processing capability and memory. A single mobile device controls the IoT devices, or users can use the concept of automation to control the functioning of smart IoT devices. This research proposes a fuzzy-based congestion control scheme (MCPFB) to control the congestion between UAVs and IoT devices. UAVs are faster, and IoT devices can collect information from UAVs and forward it to other devices. The UAV’s can store limited and sufficient types of information, but during routing, only a single path is available, which causes congestion in the FANET-IoT network. The fuzzy based load prediction and balancing routing is able to handle the problem of congestion in FANET-IoT. In order to overcome the problem of congestion with improper energy utilisation, this paper presents fuzzy rule-based congestion control techniques for a flying ad hoc network. We focus on the efforts to reduce congestion in the FANET-IoT network. Routing is a critical issue in FANET-IoT and hence the focus of this research is on the performance improvement of routing in FANET-IoT. Packets dropping on the nodes show congestion occurrence in the network, and the possibility of lost connectivity with other nodes is high. Unlike the aforementioned works, the proposed MCPFB routing shows better performance compared to the conventional BARS scheme in FANET-IoT.

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Mahendra Sahare mail -

Priti Maheshwary mail -

Vinay Kumar Dwivedi mail

link https://doi.org/10.54216/JISIoT.140115

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new