Neutrosophic Divisor Point of A Straight Line Segment With A Given Ratio

Malath Fared; Rasha

doi:https://doi.org/10.54216/PMTCS.020102

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.

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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

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.

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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

On Weak Fuzzy Complex Pythagoras Quadruples

In this work, we study the generating of Pythagoras quadruples in the sets of weak fuzzy complex integers and anti-weak fuzzy complex integers, where we present sufficient and necessary conditions for generating Pythagoras quadruples in the mentioned sets. Also, we present many examples to clarify our work's validity and novelty.

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Fredy Canizares Galarza mail -

Marcos Lalama Flores mail -

Diego Palma Rivero mail -

Mohammad Abobala mail

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

Volume & Issue

Vol. Volume 22 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Internet of Things Enabled Disease Outbreak Detection: A Predictive Modeling System

Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support Vector Machine (SVM) for disease outbreak prediction, showcasing its exceptional accuracy and performance. Through extensive experimentation and comparative analysis, we demonstrate that the SVM, when embedded in our IoT ecosystem, excels in predicting disease outbreaks, outperforming other machine learning models. This approach not only enhances the timeliness and precision of outbreak detection but also facilitates informed decision-making and resource allocation. Furthermore, our system model's integration with blockchain technology ensures the secure storage and validation of prediction results, bolstering the trustworthiness of collected data. This research represents a significant leap forward in proactive disease management and public health, offering a blueprint for future endeavors in epidemiology and healthcare. It underscores the transformative potential of IoT-driven predictive analytics in safeguarding global health and well-being.

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Ehsaneh khodadadi mail -

S. K. Towfek mail

link https://doi.org/10.54216/JISIoT.100107

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

MACBETH-MAIRCA Plithogenic Decision-Making on Feasible Strategies of Extended Producer's Responsibility towards Environmental Sustainability

The environmental disarray caused by plastic usage alarms the world leaders to join hands in making coordinated efforts in creating a green globe to support environment for the future generation. The responsibility of promoting environmental sustainability is not confined to government alone as all the producer companies have equal share in mitigating the plastic waste. One of the ideal strategies of environmental conservation practiced by many of the nations at recent times is extended producer’s responsibility (EPR). This research work purposes in building a multi-criteria decision-making model integrating the methods of MACBETH (Measuring attractiveness through a categorical-based evaluation technique) and MAIRCA (Multi Attributive Ideal-Real Comparative Analysis) in plithogenic environment to make optimal decisions on the plastic recycling methods subjected to four core criteria in EPR context. The efficiency of the newly developed integrated model is determined by comparing the model with other models integrating MAIRCA with the methods of CRITIC (CRiteria Importance Through Intercriteria Correlation) & FUCOM (Full Consistency Method). The sensitivity analysis helps in listing the merits and limitations of the proposed integrated model and also the consistency of the criterion weights and the ranking of the alternatives are checked.

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S. Sudha mail -

Nivetha Martin mail -

M. Clement Joe Anand mail -

P. G. Palanimani mail -

T. Thirunamakkani mail -

B. Ranjitha mail

link https://doi.org/10.54216/IJNS.220210

Volume & Issue

Vol. Volume 22 / Iss. Issue 2

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2023

A View Through Artificial Intelligence and Its Relationships with Machine Learning and Deep Learning

This paper presents a comparison between artificial intelligence, machine learning and deep learning. From artificial intelligence and its types of systems, through machine learning and its stages to deep learning, the most important features that belong to each of them are identified, illustrated by simple examples that help to understand the difference.

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Sandy Montajab Hazzouri mail

link https://doi.org/10.54216/PMTCS.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2023