On Weak Fuzzy Complex Pythagoras Quadruples

Fredy Canizares Galarza; Marcos Lalama Flores; Diego Palma Rivero; Mohammad Abobala

doi:https://doi.org/10.54216/IJNS.220209

Fusion: Practice and Applications | 2023

Enhancement CNN based on LSTM for vital sign classification

Monitoring vital signs is essential for tracking patient health and detecting changes in their condition. However, in aging cultures with overburdened healthcare staff, accurately and efficiently monitoring vital signs poses a challenge. To address this issue, an autonomous system for vital sign control is proposed, offering improved accuracy, real-time monitoring, alert systems, remote monitoring, and reduced staff labor costs. This paper presents a deep learning architecture using a publicly accessible dataset of 25,494 patients and five numerical characteristics to classify vital signs. A CNN-LSTM model is introduced, outperforming a traditional CNN model in terms of performance, parameter efficiency, and training time. The CNN-LSTM model effectively captures both spatial and temporal features from the input data, resulting in superior representation and improved accuracy compared to the CNN model, which only extracts spatial data. The suggested model achieved a remarkable accuracy of 98%, surpassing previous models. The findings demonstrate the potential of the CNN-LSTM model for early identification of medical issues, enabling prompt actions and enhanced patient outcomes. Overall, this research highlights the significance of implementing an autonomous system for vital sign control in healthcare organizations, offering substantial benefits in patient care and healthcare management.

groups

Mina H. Madhi mail -

Abbas M. Al-Bakry mail -

Alaa Kadhim Farhan mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/FPA.130202

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Brain Tumor Classification Using Convolutional Neural Network and Feature Extraction

Convolutional Neural Networks (CNNs) are the most popular neural network model for the image classification problem, which has seen a surge in interest in recent years thanks to its potential to improve medical picture classification. CNN employs a number of building pieces, including convolution layers, pooling layers, and fully connected layers, to determine features in an adaptive manner via backpropagation. In this study, we aimed to create a CNN model that could identify and categorize brain cancers in T1-weighted contrast-enhanced MRI scans. There are two main phases to the proposed system. To identify images using CNN, first they must be preprocessed using a variety of image processing techniques. A total of 3064 photos of glioma, meningioma, and pituitary tumors are used in the investigation. Testing accuracy for our CNN model was 94.39%, precision was 93.33%, and recall was 93% on average. The suggested system outperformed numerous well-known current algorithms and demonstrated satisfactory accuracy on the dataset. We have performed several procedures on the data set to get it ready for usage, including standardizing the pixel sizes of the photos and dividing the dataset into 80% for train, 10% for test, and 10% for validation. The proposed classifier achieves a high level of accuracy of 95.3%.

groups

Ehsan khodadadi mail -

S. K. Towfek mail -

Hussein Alkattan mail

link https://doi.org/10.54216/FPA.130203

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2023

A Proposed Framework for the Integration of BIM Models in AEC Companies in Syria

Recently, AEC companies in Syria have realized the importance of adopting BIM in their system, especially in the next phase of reconstruction in Syria. However, due to the recent experience, the BIM process is still in its early stages and needs a lot of efforts to overcome the technical and administrative obstacles in front of it. The research methodology is based on analyzing models of organizational structures for AEC companies operating with BIM technology around the world, and studying their strengths and deficiencies in order to extract the most important factors for improving the performance of BIM in the Syrian construction industry companies. The study concluded with proposing a framework for the integration of BIM models in the structure of AEC companies, and companies that do not use BIM or that operate partially according to BIM can adopt it in developing their administrative structure in accordance with their own characteristics and the requirements of applying BIM in them.

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Rania Zakaria mail -

Sonia Ahmad mail -

Hamza Omran mail

link https://doi.org/10.54216/IJBES.070103

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals

Schizophrenia, a complex psychiatric disorder, presents a significant challenge in early diagnosis and intervention. In this study, we introduce an intelligent approach to schizophrenia detection based on the fusion of multivariate electroencephalography (EEG) signals. Our methodology encompasses the integration of EEG data from multiple electrodes into multivariate input segments, which are then passed into a LightGBM (Light Gradient Boosting Machine) classification model. We systematically explore the fusion process, leveraging the spatiotemporal information captured by EEG signals, and employ machine learning to discern subtle patterns indicative of schizophrenia. To evaluate the effectiveness of our approach, we compare our model against state-of-the-art machine learning algorithms. Our results demonstrate that our LightGBM-based model outperforms existing methods, achieving competitive performance in the accurate identification of individuals with schizophrenia.

groups

Elizabeth Mayorga Aldaz mail -

Roberto Aguilar Berrezueta mail -

Neyda Hernández Bandera mail

link https://doi.org/10.54216/FPA.130204

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

Intelligent Energy Management: A Comprehensive Review of Artificial Intelligence, Machine Learning, and Metaheuristic Optimization Approaches

Intelligent energy management is increasingly important for improving energy efficiency, reliability, economic performance, and sustainability across buildings, smart homes, industrial facilities, microgrids, and interconnected energy systems. This review examines recent advances integrating machine learning, deep learning, reinforcement learning, Internet of Things technologies, predictive analytics, feature selection, and metaheuristic optimization. These methods support energy-consumption forecasting, demand response, renewable-energy coordination, load scheduling, anomaly detection, building optimization, smart-home control, industrial energy management, and microgrid operation. The reviewed studies indicate that intelligent approaches can improve forecasting accuracy, reduce energy costs, optimize resource allocation, and enhance real-time decision-making under uncertain conditions. Metaheuristic algorithms further improve performance through parameter optimization, feature selection, and multiobjective scheduling. However, practical deployment remains challenged by data quality, computational complexity, cybersecurity, privacy, interoperability, generalizability, transparency, infrastructure cost, and communication reliability. Future research should prioritize explainable, scalable, secure, and adaptive intelligent energy-management frameworks for efficient, resilient, flexible, and low-carbon energy system operation.

groups

Faustino D. Reyes mail

link https://doi.org/10.54216/MOR.070203

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

Intelligent Network Management Systems: A Comprehensive Review of Artificial Intelligence, Machine Learning, and Metaheuristic Optimization Approaches

Modern communication networks have become increasingly heterogeneous, programmable, distributed, and servicedriven, creating management requirements that exceed the capabilities of purely manual or static rule-based operation. This review examines the evolution of intelligent Network Management Systems (NMSs) through the integration of machine learning, deep learning, predictive analytics, feature selection, metaheuristic optimization, network telemetry, software-defined control, and automated decision mechanisms. The reviewed methodological directions support traffic and performance prediction, anomaly and intrusion detection, fault localization, configuration optimization, resource allocation, routing assistance, quality-of-service management, capacity planning, and closedloop remediation. Intelligent approaches can strengthen situational awareness and reduce the time required to identify, diagnose, and respond to rapidly changing network conditions, while metaheuristic algorithms provide flexible mechanisms for feature-space reduction, model calibration, multi-objective resource optimization, and search within large configuration spaces. Practical deployment nevertheless remains constrained by telemetry quality, concept drift, computational overhead, interoperability, multi-vendor heterogeneity, security, privacy, explainability, scalability, and the risk of unsafe automated actions. Future research should therefore prioritize trustworthy, policy-aware, scalable, secure, and adaptive NMS architectures capable of combining continuous observability with verifiable closed-loop automation across enterprise, cloud, edge, Internet of Things, and multi-domain network environments.

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Amir Neamatallah mail -

Raneem Elagmy mail -

Ahmed Ibrahim mail -

Mohamed Ali mail

link https://doi.org/10.54216/MOR.070204

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Exploring the Fusion of Blockchain and AI for Enhanced Practices in IoT Ecosystems: Opportunities and Challenges

The rapid expansion of the Internet of Things (IoT) has ushered in an era of unprecedented data generation, offering transformative potential across industries. Yet, this vast data landscape brings forth challenges related to security, privacy, trust, and intelligent data analysis. In response to these challenges, the fusion of blockchain technology and artificial intelligence (AI) within IoT ecosystems has emerged as a promising solution. This paper embarks on a comprehensive exploration of this fusion, delving into its opportunities and challenges. We provide an overview of IoT's evolution, blockchain technology's fundamental principles, and the significance of AI in data analysis and decision-making. Our focus lies in elucidating how the integration of blockchain fortifies data security, trust, and transparency in IoT applications, while AI augments data analysis, predictive maintenance, and automation. Furthermore, we discuss the challenges and considerations that accompany the integration of AI and blockchain in IoT environments, including scalability, privacy concerns, interoperability, and ethical considerations. By examining the intricate interplay of these technologies, this paper contributes to a deeper understanding of how the fusion of blockchain and AI can usher in a new era of secure, intelligent, and efficient IoT practices.

groups

Fausto Vizcaino Naranjo mail -

Jorge L. Acosta Espinoza mail -

Silvio Machuca Vivar mail

link https://doi.org/10.54216/FPA.130205

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Multi-Sensor Data Fusion for Accurate Human Activity Recognition with Deep Learning

In the era of pervasive computing and wearable technology, the accurate recognition of human activities has gained paramount importance across a spectrum of applications, from healthcare monitoring to smart environments. This paper introduces a novel methodology that leverages the fusion of multi-sensor data with deep learning techniques to enhance the precision and robustness of human activity recognition. Our approach commences with the transformation of accelerometer and gyroscope time-series data into recurrence plots, facilitating the distillation of temporal patterns and dependencies. Subsequently, a dual-path convolutional network framework is employed to extract intricate sensory patterns independently, followed by an attention module that fuses these features, capturing their nuanced interactions. Rigorous experimental evaluations, including comparative analyses against traditional machine learning baselines, validate the superior performance of our methodology. The results demonstrate remarkable classification performance, underscoring the efficacy of our approach in recognizing a diverse range of human activities. Our research not only advances the state-of-the-art in activity recognition but also highlights the potential of deep learning and multi-sensor data fusion in enabling context-aware systems for the benefit of society.

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Edmundo Jalon Arias mail -

Luz M. Aguirre Paz mail -

Luis Molina Chalacan mail

link https://doi.org/10.54216/FPA.130206

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

On The Symbolic n-Plithogenic Square Real Matrices For 13≤n≤14 and Their Elementary Algebraic Properties

The main goal of this paper is to study the algebraic properties of the symbolic n-plithogenic matrices in two different special cases (for n=13, n=14). We present many theorems that describe the algebraic behavior of these matrices, where an algorithm for computing determinants, inverses, and eigenvalues will be provided. On the other hand, the relationships between symbolic 13-plithogenic/14-plithogenic matrices and their classical components will be derived.

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Luis llerena Ocana mail -

Dionisio Ponce Ruiz mail -

Maria Pico Pico mail

link https://doi.org/10.54216/IJNS.220208

Volume & Issue

Vol. Volume 22 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023