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International Journal of Neutrosophic Science | 2025

Neutrosophic EWMA and DEWMA control chart on Exponential and Transformed Exponential Distributions

The sophisticated statistical methods known as Bayesian EWMA and DEWMA control charts are intended to track process performance and identify changes in data over time. They improve the capacity to monitor minute changes in the process by combining conventional smoothing methods with Bayesian inference. By integrating the idea of neutrosophic approaches into Bayesian EWMA and DEWMA models, the suggested approach seeks to address and get beyond this restriction. In this study, neutrosophic approaches are utilized to provide the manufacturing process with two tolerance limits instead of a set value for upper and lower control limits, particularly when all observations are uncertain, imprecise, or fuzzy. By combining the Exponential, Inverse Rayleigh, and Weibull distributions, five symmetric loss functions are examined while taking uniform prior into account. Additionally, for mean, variance, and control limits of the proposed work have been derived. Simulation studies were conducted and compared with previous work as well as all projected works. This study significantly advances the subject of control chart technique, especially when it comes to managing hard, vast, and complicated information.

groups
Ishah Maria Mathew mail -
O. S. Deepa mail
link https://doi.org/10.54216/IJNS.260418

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

DenseNet-Based Deep Learning Driven Multi-Class Classification of Side-Scan Sonar Images for Marine Exploration

This paper discusses about implementing Machine Learning Models with the Marine_Pulse dataset. This is about side-scan sonar images that have four groups: Engineering Platform (EP), Pipeline/Cable (P/C), Sea Bed Surface (SBS), and Underwater Residual Mound (URM). This manuscript performed some difficult feature extraction and classification methods using the DenseNet-DNN framework. This paper delves deeply into the implementation of the DenseNet121 Dropout, DenseNet201 Dropout, DenseNet201 Enhanced Dropout, and DenseNet201 Transfer Learning models. It investigates how these models perform on feature extraction and classification using a DNN. We enhanced the performance and reduced overfitting by applying a dropout to DenseNet121 and DenseNet201 and by adding transfer learning (TL) to DenseNet201, respectively. The models were evaluated based on the accuracy, precision, recall, F1-score, specificity, and classification errors of the training and testing samples. We observed that DenseNet201 Enhanced Dropout outperformed the other models, achieving the highest accuracy of 95.79%. DenseNet201 Dropout followed this achievement with an accuracy of 94.74% and DenseNet121 Dropout with an accuracy of 92.11%. DenseNet201 Transfer Learning, on the other hand, had the worst accuracy (92.11%).  Specificity is a measure of how well the model represents negative examples correctly. The maximum specificity was observed in DenseNet201 Enhanced Dropout (98.38%). DenseNet201 Dropout follows it at 97.96% and DenseNet121 Dropout at 97.18%. The smallest specificity was reported on DenseNet201 TL with 96.60%. This result demonstrates that our keys can generalize well and that they maintain high classification accuracy on the test data.

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Maddukuri Srinadh mail -
J. B. Seventline mail
link https://doi.org/10.54216/JCIM.160213

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new
Journal of Sustainable Development and Green Technology | 2025

Enhancing Regional Growth by Overcoming Economic Integration Challenges

This study investigates the systemic barriers hindering regional economic integration and their implications for broader regional growth. Focusing on qualitative analysis of policy reports, trade data, and regional agreements, it identifies key challenges such as political instability, security concerns, inadequate infrastructure, restrictive trade policies, and weak financial systems that limit effective integration and regional development. The research highlights how insecurity and governance issues deter foreign investment and trade partnerships, while underdeveloped transport and energy networks obstruct connectivity among neighbouring countries. Furthermore, complex trade regulations and limited access to international finance restrict economic cooperation and growth potential. The findings underscore the critical importance of promoting political stability, simplifying trade regulations, and strengthening financial systems to enhance regional integration. Collaborative efforts in infrastructure development and transit facilitation, coupled with international support for institutional reforms, are essential to overcoming these barriers. Such measures will not only facilitate seamless economic integration but also contribute to sustainable regional growth and stability.

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Farkhod Abdurakhmonov mail -
Abdulxay Kholmuminov mail
link https://doi.org/10.54216/JSDGT.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Computational Artificial Neural Network Performances for the Fractional Order Lumpy Skin Disease Model

The motive of current investigations is to design a computational artificial neural network procedure for the numerical outputs of the fractional order (FO) lumpy skin disease model (LSDM), called as FO-LSDM. The stochastic performances using the optimization of scale conjugate gradient (SCGD) have been implemented to get the solutions of the FO-LSDM. The aim to implement the solutions of the FO is considered more reliable as compared to the integer order. The mathematical form of the LSDM is divided into two populations based on the cattle and vector using the population of susceptible and infected. A numerical Adam scheme is plagued to accomplish the dataset for reducing the mean square error by splitting the statics of endorsement, testing and training as 13%, 12% and 75%. The proposed stochastic neural network approach has a single layer, thirty numbers of neurons, sigmoid activation function, and optimization based SCGD procedure. The exactitude of the SCGD neural network is authenticated through the result comparisons and reducible absolute error around 10-06 to 10-08. Additionally, the correctness of the stochastic process based on the SCGD neural network is evaluated by applying the procedure of state transitions, correlation values, and best training.

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Saleh Ali Alomari mail
link https://doi.org/10.54216/JISIoT.170209

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new
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.

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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

Details open_in_new
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

Details open_in_new
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

Details open_in_new
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.

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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

Details open_in_new
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.    

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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