ASPG Menu
search

American Scientific Publishing Group

Research Feed

Found 3841 matches for "All Articles"
Journal of Intelligent Systems and Internet of Things | 2025

Internet of Medical Things Powered by Machine Learning for Real-Time Diabetes Prediction

Diabetes is a common chronic illness that requires ongoing patient monitoring to diagnose the condition in a timely manner. With the significant advancements of the Internet of Medical Things (IoMT) sector in recent years, it is feasible now to monitor the patient's information continuously. There are many studies that used IoMT and machine learning (ML) techniques to diagnose diabetes but so far, the accuracy of the performance is still below the required level. Therefore, this study proposes a common framework for IoMT, cloud, and ML techniques to diagnose diabetes in real-time. IoMT devices continuously collect vital information of diabetic patients such as glucose and insulin levels. Then, this data is transmitted using various communication technologies to be stored in the cloud for diagnosis. Finally, to improve diagnostic accuracy, voting ensemble strategy-based method has been proposed that combines predictions from three base ML techniques (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)). The proposed voting model achieved promising results in diagnosing diabetes with an accurate rate of up to 98.0%, outperforming the base classifiers in this and previous studies.

groups
Qusay Saihood mail -
Inas H Kareem mail -
Omar Ayad Ismael mail -
Saad I. Mohammed mail -
Ahmed NO Algburi mail
link https://doi.org/10.54216/JISIoT.170108

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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

Brain Tumor Diagnosis Using Pre-Trained Conventional Neural Network Model

Diagnosis of brain tumors from MRI scans is a vital concern in medical imaging that contributes to the need for fast and accurate deep learning models. In this study, it is proposed a Hybrid CNN-ViT Feature Extraction framework that utilizes the local spatial feature extraction capability of Convolutional Neural Networks (CNNs) and long-range dependency capturing ability of Vision Transformers (ViTs). The method starts with a set of advanced preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and data augmentation based on generative adversarial networks (GAN) to help increase image quality and balance the dataset. First, trained by a CNN-based backbone is EfficientNet to obtain low- and mid-level spatial features, the hybrid model is proposed. These feature maps are further converted into patches and input to a Vision Transformer  (ViT) encoder, where self-attention functions to refine global feature representations. The proposed method utilized concatenation and attention-based mechanism for feature fusion, which ensured the discriminative classification of features from both CNN and ViT. Finally, a fully connected layer with the softmax classifier predicts the presence of tumor and its kind. Extensive experiments have been conducted on benchmark brain MRI datasets, which show that the Hybrid CNN-ViT model significantly outperforms traditional CNN-based models and achieves higher accuracy, precision, recall, and F1-score. The study demonstrates the successful application of hybrid deep learning techniques for robust and generalizable brain tumor classification. The novelty of this research lies in integrating spatial information with context attention in enhancing AI-based medical diagnostics.

groups
Shokhan M. Al-Barzinji mail -
Mohammed Q. Jawad mail -
Othman Mohammed Jasim mail -
Zaid Sami Mohsen mail -
Omar Falah Al-Jumaili mail
link https://doi.org/10.54216/JISIoT.170109

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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

SecureRS-CBIR: A Privacy-Preserving Deep Learning Framework for Content-Based Remote Sensing Image Retrieval

Recent advancements in Remote Sensing (RS) have created challenges in data storage, retrieval, and privacy. Existing Content-Based Image Retrieval (CBIR) systems are useful but often face limitations related to hypersensitivity towards remote sensing data in the cloud, scalability, and security. This article presents SecureRS-CBIR, a privacy-preserving framework for remote sensing image retrieval combining deep learning with multi-level encryption. The system uses three CNN models (VGG16, ResNet50, and DenseNet121) for feature extraction and implements encryption through image division, texture extraction, subblock shuffling, and color encryption. Experiments on the Aerial Image Dataset show VGG16 achieving 96% validation accuracy, with ResNet50 and DenseNet121 at 95% and 94% respectively. DenseNet121 excelled at DenseResidential classification (41/42 correct) with minor confusion between Beach and Desert categories. The framework successfully balances security with retrieval efficiency, maintaining privacy through robust encryption while enabling accurate content-based searches, providing a scalable solution for secure image retrieval in cloud environments. This work offers a new approach for remote sensing image retrieval by enabling efficient searching in large-scale datasets while addressing privacy concerns in cloud environments, thereby contributing to the relevant literature.

groups
Ahmed Sabah Ahmed AL-Jumaili mail -
Huda Kadhim Tayyeh mail
link https://doi.org/10.54216/JISIoT.170110

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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

VSG parallel power distribution control strategy by adaptive virtual impedance

As electric power develops, stable distribution of output power has become a key issue, and more and more power distribution strategies have been proposed. However, most of them are single distribution strategies with large errors and low credibility, which makes it difficult to maintain the stability of motor output distribution power in the actual situation. Therefore, by characteristics of adaptive virtual impedance to reduce small signals influence in the circuit and parallel power stability of virtual synchronous machine virtual synchronous generator control strategy, this research establishes a parallel power model of virtual synchronous generator, selects the changes of voltage and current as the measurement standard of the system, and sets up simulation experiments to determine whether to add adaptive virtual impedance to design a control strategy that can stably distribute output power. Results showed that it can keep output ratio of active power and reactive power within range of 2:1, and voltage difference at the output terminal is 0, and the current is 0.8A, which meets the requirements of circulating current. In a word, the control strategy of virtual synchronous generator designed in this research has high accuracy and strong stability. Compared with previous control strategies, the control strategy of parallel power distribution can ensure the stability of output power in the actual situation. This achievement has certain application prospects in the field of motor power distribution.

groups
Jianfeng Wang mail -
Nurulazlina Ramli mail -
Noor Hafizah Abdul Aziz mail
link https://doi.org/10.54216/JISIoT.170111

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2025

Application of Neutrosophic Stratified Ranked Set Sampling: An Efficient Sampling Technique in the Estimation of Average Relative Humidity in USA

The study examined the shortcomings of conventional statistical techniques in managing unclear or ambiguous data and emphasized the necessity of implementing neutrosophic statistical techniques as a more enhanced remedy. Advanced techniques like neutrosophic statistics (NS) were developed since traditional statistical methods are unable to handle the uncertainty present in ambiguous data. In order to tackle this problem, the study suggested an innovative and novel sampling method called "neutrosophic stratified ranked set sampling (NSRSS)" in addition to specialized neutrosophic estimators for precisely predicting the population mean in the proximity of uncertainty. This novel strategy adjusted ranked set sampling (RSS) techniques to allow the special features of neutrosophic data. Furthermore, the study improved the precision of estimating the population mean in uncertain situations by introducing neutrosophic estimators that use subsidiary information inside the structure of stratified ranked set sampling (SRSS). The work provided theoretical insights into the performance of these estimators by presenting comprehensive formulations of bias and mean squared error (MSE). To illustrate the efficacy of the suggested techniques, the study includes simulation studies, numerical examples conducted using the computer language R. Evaluations utilizing MSE, and percentage relative efficiency (PRE) demonstrated the higher accuracy of the suggested estimators over conventional alternatives. The findings demonstrated the NSRSS's applicability, particularly for predicting population means in situations where heterogeneity and uncertainty are prevalent. Furthermore, it was demonstrated that the estimators and technique produced interval-based findings, which provided a more accurate depiction of the uncertainty related to population parameters. The reliability of the estimators in estimating population means was greatly improved by this interval estimation in combination with a lower MSE. A significant vacuum in the field of statistical research is filled by the study's introduction of estimators and a customized sampling approach made especially for neutrosophic data. This research significantly advances statistical theory and practice by extending traditional statistical approaches to efficiently handle ambiguous data, especially for applications where exact data is few, heterogeneous, or uncertain. The empirical validation through numerical illustrations and simulations conducted in R further solidifies the practicality and robustness of the proposed techniques, reinforcing their applicability to real-world scenarios.

groups
Vishwajeet Singh mail -
Rajesh Singh mail -
Anamika Kumari mail
link https://doi.org/10.54216/IJNS.260312

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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

Cloud Enabled Blockchain and IoT Integrated Assisted Living System Supporting Quality of Life and Privacy in 6G Networks

Due to emerging disruptive technologies, the Internet of Things (IoT) is essential in innovative living domains, such as elderly and disabled healthcare services, home security and safety monitoring, and computerization control services. The IoT can improve inhabitants' quality of life and the quality of life of smart ambient assisted living (AAL) environment users. The sixth (6G) network will enable a completely linked world with terrestrial wireless communications. Blockchain-based approaches offer decentralized privacy and security, yet they include vital delay, computational, and energy overhead inappropriate for most resource-reserved IoT devices. Hence, this study proposes a Blockchain and IoT-based Assisted Living System (BIoT-ALS) using 6G communication. The nodes in our proposed paradigm use smart contracts to specify norms of interaction while working together to provide storage and computing resources. Our suggested approach has encouraged confidence-free interaction and boosted user privacy through the blockchain approach. This paper aims to explain the sensor layer, a distributed signal processing system in vast, physically connected, wirelessly networked, and energy-restricted networks of sensor items. A comprehensive experimental test series shows each sensor type's accuracy and probable usage. The numerical results show that the suggested BIoT-ALS model improves the performance ratio of 99.1%, accuracy ratio of 98.8%, reliability ratio of 94.8%, an efficiency ratio of 93.6%, the throughput rate of 97.6%, and reduces the network delay of 19.2%, latency ratio 10.2%, and execution time of 20.4% compared to other popular models.

groups
Mohanaprakash T. A. mail -
Nagalingam Mythili mail -
V. Pandarinathan mail -
Santhi Karuppiah mail -
R. Saravanan mail -
P. Sangeetha mail
link https://doi.org/10.54216/JISIoT.170112

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2025

Multiple Opinions in a Fuzzy Soft Expert Set and Their Application to Decision-Making Issues

Decision-making procedures frequently encounter disputes or contradictions between expert perspectives, and thus we need ways to resolve them. When working with many viewpoints in a decision-making environment, this method enables dynamic, changing input from different experts, which may be particularly helpful in real- world situations like expert systems and group decision-making. In this work, we provide multiple opinions in the fuzzy soft expert set and their application to decision-making issues (MO-FSES), which is an extension of the fuzzy soft set, and multiple opinions in time fuzzy soft expert set. The characteristics of its primary operations complement, union intersection, AND, and OR will also be defined and examined by me. Lastly, we will use this strategy for decision-making challenges.

groups
Ayman.A Hazaymeh mail -
Anwar Bataihah mail
link https://doi.org/10.54216/IJNS.260313

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

On Soft Locally Closed Sets and Soft Submaximal Spaces

This work adds to the burgeoning knowledge of soft topology. First, we continue the study of soft locally closed sets. We present several characterizations of soft locally closed sets. Also, we investigate their behaviors using specialized soft topologies as product and subspace soft topologies. Then, we define and investigate the concept of soft dense-in-itself spaces. In particular, we characterize soft dense-in-itself subspaces in terms of locally closed sets. Given a soft topological space pN, ρ, Mq, the collection of soft locally closed sets of pN, ρ, Mq forms a soft topology on N relative to M which is denoted by ρl. We obtain several symmetries between the pN, ρ, Mq and pN, ρl, Mq. In particular, we show that pN, ρ, Mq is soft T0 (resp. soft TD, soft indiscrete) iff pN, ρl, Mq is soft T0 (resp. soft discrete, soft connected). Moreover, we show that if pN, ρl, Mq is soft T1 (resp. soft Alexandroff), then pN, ρl, Mq is soft discrete (resp. soft Alexandroff) but not conversely. In addition to these, we obtain several characterizations and relationships of both soft locally indiscrete spaces and soft submaximal spaces. In particular, we show that pN, ρ, Mq is soft locally indiscrete if and only if ρ “ ρl. In the last section, via the soft locally closed sets, we define and investigate soft lc-regularity as a stronger form of soft regularity. Finally, the paper deals with the correspondence between some concepts in soft topology and their analog concepts in classical topology.

groups
Samer Al-Ghour mail -
Dina Abuzaid mail
link https://doi.org/10.54216/IJNS.260314

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

W- Hausdorff Separation Axiom in Second Order Interval Valued Fuzzy Topological Spaces

We studied and introduced a concept SIVFT then present the concept of SIVF subspace and SIVF product topology in SIVF topological spaces. W-Hausdorff Separation Axiom in SIVF topological spaces and its basics are studied.

groups
Bhavani Gokila D. mail -
Vijayalakshmi V. M. mail
link https://doi.org/10.54216/IJNS.260315

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

A Nonagonal Single-Valued Neutrosophic Soft Set Analysis of Issues Faced by Female Employees across Industries

This paper presents a novel approach for ranking the issues experienced by female employees across various industries using the nonagonal single-valued neutrosophic soft set framework. By leveraging an extensive database of multi-observer data, we evaluated the challenges faced by women in diverse work environments. The Neutrosophic Soft Set proved to be a robust tool for addressing decision-making complexities within the neutrosophic domain, facilitating a comprehensive understanding of these issues. We established a comparative table to categorize the identified problems, enabling effective organization based on attributes, capabilities, and outcomes. Our findings underscore the utility of advanced mathematical frameworks in analyzing gender-specific workplace challenges, providing valuable insights for developing targeted interventions. This research contributes to the ongoing discourse on gender equity in the workplace and lays the groundwork for future studies aimed at enhancing the experiences of female employees across sectors.

groups
John Jayaraj J. mail -
N. Jose Parvin Praveena mail -
I. Paulraj Jayasimman mail -
broumi said mail
link https://doi.org/10.54216/IJNS.260316

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new