Enhancing EEG-Based Emotion Recognition in Computer Games Using KNN Optimized by the iHOW Optimization Algorithm

Abdelhameed; Christos; El-Sayed M.; Marwa M.

doi:https://doi.org/10.54216/FPA.210108

Prospects for Applied Mathematics and Data Analysis | 2024

Stability Solution of Fractional Randomly System

In this paper, we study stability Solution of Fractional Randomly System. Two methods are provided to check the stability of such system in mean sense. The first method is based on integral inequalities. The second method is based on Lyapunov function. Stable in mean sense, asymptotically stable in mean sense are shown by using generalized Gromwell inequality. Stable in mean sense, asymptotically stable in mean sense, Mittag-Leffler stable in mean sense are shown by using generalized Lyapunov method.

groups

Eman Ahmad Hussen mail -

Sameh ALargeh mail

link https://doi.org/10.54216/PAMDA.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

An Introduction to Symbolic Neutrosophic Algebras

The objective of this paper is to study for the first time the concept of symbolic neutrosophic algebra defined over a neutrosophic ring R(I), where we derive a strict definition of this concept as an expansion of neutrosophic modules. In addition, we study some of its elementary properties such as neutrosophic subalgebras, neutrosophic homomorphisms and kernels through many theorems and mathematical proofs.

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Nader Taffach mail -

Mohammad Al-Shiekh mail

link https://doi.org/10.54216/GJMSA.0120105

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Identify Type of Squint of Human's Eye through Deep Network EfficientNet-B0 with Grad-CAM

Finding and treating different types of strabismus, which is when the eyes do not line up properly, can be challenging. This study introduces a deep learning system that automatically identifies five types of strabismus: esotropia, exotropia, hypertropia, hypotropia, and normal eye alignment. It combines EfficientNet-B0 with Grad-CAM to improve how the system recognizes and classifies these conditions accurately. These help EfficientNet-B0 improve how it picks out important features using squeeze-and-excitation blocks, which capture key details needed for accurate classification. Grad-CAM further refines this process and localizes the critical regions in the feature maps more effectively to improve interpretability. We trained the model on a dataset of 10,000 balanced images across the five classes, achieving a classification accuracy of 99.43% and 96.33% for training and testing data, respectively. The model's focus-based architecture ensures that clinicians' set goals are met in terms of the model's efficiency and reliability for predictions.

groups

Wafaa H. Alwan mail -

Sabah M. Imran mail

link https://doi.org/10.54216/FPA.210101

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Development of a Real-Tıme IoT-Based Portable Partıculate Matter Monıtorıng Devıce Usıng PMS5003 Sensor

Particulate Matter (PM) concentration significantly affects public health, exacerbating respiratory conditions and contributing to environmental challenges. This study presents a real-time Internet of Things (IoT)-based portable particulate matter monitoring device utilizing the PMS5003 sensor. The device measures PM1.0, PM2.5, and PM10 concentrations and uploads the data to the cloud at 15-second intervals for real-time visualization. A two-week observational study in South Tangerang, Indonesia, revealed peak PM2.5 and PM10 levels of 218 µg/m³ and 232 µg/m³, respectively, on weekdays, compared to a weekend low of 19.76 µg/m³ for PM2.5. Variations were influenced by anthropogenic factors, including vehicular and industrial activity. Data analysis showed a 78% reduction in PM2.5 levels during weekends, highlighting the impact of human activity on air quality. These findings underscore the impact of anthropogenic activities on air quality and demonstrate the effectiveness of IoT-based systems in environmental monitoring. The study highlights the potential for such technology to support data-driven strategies for pollution management and public health improvement.

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Lina Warlina mail -

Sri Listyarini mail -

Mohamad Afendee Mohamed mail -

Wan Suryani Wan Awang mail -

Roslan Umar mail -

Aceng Sambas mail

link https://doi.org/10.54216/FPA.210102

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Optimizing Performance in Modern Web Systems and Applications: An Analysis of Caching and Load Balancing Techniques

To increase scalability, response speed, and fault tolerance, modern web systems must have load balancing and caching solutions. Better resource allocation and traffic management control help to prevent system overload. This is essential to satisfy the growing need for perfect digital experiences. This work intends to demonstrate an adaptive load balancing system using real-time job scheduling, predictive analytics, and multi-layer caching, integrating artificial intelligence technology. Our hybrid deep learning and storage systems lower data retrieval time and estimate traffic. This approach tremendously increases the efficiency of online systems. Unlike conventional load balancing systems, which rely on either static or rule-based traffic distribution, our approach employs artificial intelligence-based dynamic allocation to real-time resource adjustment. Our solution forecasts workload surges and pre-allocated resources suitably using deep neural networks in conjunction with past traffic data. To hasten data retrieval, the multi-layer caching approach makes use of content delivery networks (CDNs) and cloud-based storage. This lessens the double effort required and helps one discover objects more easily. Among the several advantages, the new approach offers over the old ones are a 40% decrease in energy use, a 20% improvement in resource use, and a 50% improvement in reaction time. This approach has exceeded round robin and dynamic load balancing in actual AWS simulations. These findings highlight how incorporating predictive analytics driven by artificial intelligence might improve current site designs. For cloud platforms, IoT systems, and high-traffic online applications needing efficiency and fast adaption, this approach performs well.

groups

Ebtehal Akeel Hamed mail -

Ahmed Mahdi Abdulkadium mail -

Enas Faris Yahya mail

link https://doi.org/10.54216/FPA.210104

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Comparative Analysis of Fuzzy Time Series Methods for Predicting Indonesia's Export Performance

This study aims to forecast the export volumes of oil and gas and non-oil and gas sectors in Indonesia, as export volumes reflect the economic condition of a country. The research utilizes data from BPS, spanning from January 2018 to December 2023, and employs the Fuzzy Time Series (FTS) methodology. Six different methods are applied: First-Order FTS Chen, First-Order FTS Cheng, Second-Order FTS Chen, Second-Order FTS Cheng, Markov Chain FTS, and Time-Invariant FTS. FTS is a predictive technique based on fundamental logic and various concepts and rules within fuzzy sets. The prediction accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). The MAPE values for these six methods are compared to determine the most suitable method for this case study. The findings reveal that First-Order FTS Chen achieves an accuracy of 4.07%, First-Order FTS Cheng 4%, Second-Order FTS Chen 1.61%, Second-Order FTS Cheng 1.58%, Markov Chain 3.96%, and Time-Invariant 8.88%. The results indicate that Second-Order FTS Cheng provides the highest accuracy and is effective for predicting the export volumes of oil and gas and non-oil and gas sectors in Indonesia.    

groups

Lintang Patria mail -

Zahratul Amani Zakaria mail

link https://doi.org/10.54216/FPA.210103

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Systematic Review of Blockchain and Metaheuristic Algorithms for Secure and Scalable Healthcare Systems

The integration of blockchain technology and metaheuristic optimization has transformed healthcare systems by improving security, scalability, and data interoperability. Blockchain ensures decentralization, immutability, and privacy, making it a viable solution for electronic medical records (EMRs) and secure healthcare data management. Meanwhile, metaheuristic algorithms optimize blockchain networks by enhancing transaction efficiency, consensus mechanisms, and real-time medical data processing. This paper systematically reviews recent advancements in blockchain and metaheuristics for healthcare applications. We discuss existing privacy-preserving models, AI-driven optimization techniques, and hybrid consensus mechanisms, addressing their strengths and limitations. Through a structured methodology, we analyze research trends, security challenges, and computational bottlenecks. This study encompassed 300 research articles from nine global databases. Then, inclusion and exclusion criteria were applied, leading to the exclusion of 144 studies and the retention of 156 studies. Subsequently, quality assessments were conducted, resulting in the final inclusion of only 8 studies for data extraction. A three-phase methodology was followed: planning, conducting, and reporting. The studies covered the period from January 2020 to January 2025, and 10 evaluation questions were used to assess the quality of the studies. Our findings reveal that while blockchain enhances data security and interoperability, metaheuristic-driven AI further optimizes system efficiency. However, challenges such as scalability constraints, energy consumption, regulatory compliance, and AI-based cyber threats remain significant. Future research should focus on developing lightweight blockchain architectures, quantum- resistant cryptographic models, and federated AI-enhanced security frameworks to address these issues. By leveraging advanced blockchain and AI-driven metaheuristics, healthcare systems can achieve greater resilience, efficiency, and adaptive security.

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Karam Hatem Alkhater mail -

Mohana Shanmugam mail -

Pritheega Magalingam mail

link https://doi.org/10.54216/FPA.210105

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Novel Features Selection Method for Misuse Intrusion Detection System based on RNA Encoding and Raita Algorithm

The significance of the Intrusion Detection System (IDS) is due to its capability in detecting attacks over the network. The current paper proposes a new feature selection method for misuse intrusion detection systems based on RNA encoding, where the proposed method includes five steps. Firstly, the KDD-Cup99 dataset is used and then select random records are used for both training and testing. Secondly, RNA encoding to encode each possible value in the dataset into RNA characters. Thirdly, the keys and their locations are extracted by dividing the achieved RNA sequences from previous steps into blocks with different sizes, then finding the most repeated blocks, choosing them as keys, and storing their location. The next step is the proposed feature selection method based on the extracted keys and their locations, depending on the place of the key within the feature number. Finally, the Raita algorithm for matching to search for keys before and after the applied features selection method. In terms of IDS performance evaluation, experimental outcomes of the proposed feature selection method show the capability of optimizing the time complexity and metrics.  

groups

Dunia Alawi Jarwan mail -

Omar Fitian Rashid mail -

M. Jasim Mohammed mail -

Shaymaa E. Sarhan mail -

Hind Moutaz Al-Dabbas mail -

Maythem K. Abbas mail

link https://doi.org/10.54216/FPA.210106

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Hybrid AI-Based Approach for Early and Accurate Rice Disease Detection

Rice plant disease detection is crucial in agriculture to prevent crop loss and enhance productivity. Traditional manual inspection methods often lead to inaccuracies, delays in diagnosis, and excessive pesticide use. To address these challenges, this study proposes an Artificial Layered Fuzzy Neural Network-based African Vulture Optimization (ALFNN-AVO) algorithm for early and accurate detection of rice plant diseases. The proposed framework integrates multiple advanced techniques, including Cross Fusion former (CF former) for feature extraction, Squeeze Excitation (SE) fusion for enhancing feature representation, and Spatial Fuzzy C-Means (SPFCM) for precise segmentation of affected plant regions. Furthermore, an Artificial Layered Depth Separable Neural Network (ALDSNN) is employed for multi-class classification of rice plant diseases. The Differential Bitwise African Vultures Optimization Algorithm (DBAVOA) is introduced to optimize the hyperparameters, ensuring improved convergence and classification performance. Experimental results validate the efficiency of the proposed model, achieving an accuracy of 98.87% and an execution time of 0.09 minutes, outperforming existing methodologies. The findings demonstrate that the proposed framework offers a reliable and computationally efficient solution for real-time rice plant disease detection, contributing to sustainable agricultural practices.  

groups

Noorishta Hashmi mail -

Mohammad Haroon mail

link https://doi.org/10.54216/FPA.210107

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Enhancing EEG-Based Emotion Recognition in Computer Games Using KNN Optimized by the iHOW Optimization Algorithm

Emotion recognition using electroencephalogram (EEG) signals has become a pivotal area in affective computing, particularly within the context of human–computer interaction and game-based environments. This study aims to enhance the accuracy and robustness of EEG-based emotion classification by introducing a hybrid framework that combines the k-Nearest Neighbors (KNN) classifier with advanced metaheuristic feature selection techniques. Using the publicly available GAMEEMO dataset, which includes EEG recordings from 28 subjects engaged in four emotionally distinct computer games (boring, calm, horror, and funny), EEG data were acquired through a 14-channel Emotiv Epoc+ device and labeled using the Self-Assessment Manikin (SAM) scale. Baseline machine learning models including Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), and KNN were evaluated, with KNN achieving the highest base line performance. The KNN classifier was further optimized using several metaheuristic algorithms—namely WAO, BBO, GWO, GA, FA, PSO—and the proposed Improved Human Optimization Algorithm (iHOW). Experimental results show that the iHOW+KNN model achieved the best overall performance with an accuracy of 96.85%, sensitivity of 95.50%, specificity of 95.82%, and F1-score of 95.54%. Visual assessments using heatmaps, radar plots, and confidence intervals further validated the model’s reliability. These findings demonstrate the effectiveness of the iHOW+KNN framework in addressing the challenges of high-dimensional EEG data and highlight the potential of wearable EEG devices for real-time emotion recognition in affective computing applications into user experiences within the gaming environment.

groups

Abdelhameed Ibrahim mail -

Christos Gatzoulis mail -

El-Sayed M. El-kenawy mail -

Marwa M. Eid mail

link https://doi.org/10.54216/FPA.210108

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new