Neutrosophic–Fuzzy Multi-View Imputation for Incomplete Multivariate Measurement Data

Ahmed

doi:https://doi.org/10.54216/JNFS.100201

Journal of Intelligent Systems and Internet of Things | 2025

MFWX: Multi-Scale CNN with Multi-Frequency Channel Attention and Weighted Particle Swarm Optimization for Enhanced Brain Tumor Segmentation and Classification

The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis and/or treatment planning. The existing techniques suffer the effects of scale variation, redundant features, and the high dimensionality that causes ambiguous findings. We suggest the model named MFWX, which unites Multi-Scale CNN, Multi-Frequency Channel Attention (MFCA), Weighted Particle Swarm Optimization (WPSO) to identify features and XGBoost methods to classify them. The Multi-Scale CNN will capture the structure of the tumor at multiple resolutions, MFCA adjusts the features by zeroing in on significant frequency zones and WPSO eliminates redundancy to heavy-hit the strong forecasts of XGBoost. However, MFWX attained 94.2 accuracy and 92.5 Dice on the BraTS-2020 dataset surpassing ResNet50, EfficientNet-B7, and U-Net. It achieved an accuracy of 96.7%, and Dice of 95.1% on BraTS-2018, and performed well on classes of tumors. Ablation experiments proved the necessity of every part. In general, MFWX presents an efficient, clinically meaningful, scalable solution that outsmarts the current segmentation techniques.

groups

Mohammed Nazneen Fathima mail -

Prabhjot Singh1 mail -

Simrandeep Singh mail

link https://doi.org/10.54216/JISIoT.170221

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

A Real-Time Sign Language Recognition Framework Using Deep Learning and Internet of Things

  Sign language is a vital communication mean for hearing-impaired individuals, combining manual gestures with non-manual signs like facial expressions and body movements, often requiring both hands and sequential actions. Recently, an automatic Sign Language Recognition (SLR) has gained increasing attention, with Machine Learning and Deep Learning systems achieving competitive performance. While convolutional neural network has been widely employed owing to their effectiveness in image-based recognition tasks, existing methods, however, often struggle with efficiency, adaptability, and real-time deployment. This paper proposes an Internet of Things-Integrated Deep Learning Model for Real-Time SLR to enhance the communication among individuals with hearing-impairment and non-signers. The framework employs IoT-based wearable sensors for capturing hand and finger movements, followed by Sobel filtering for noise reduction. MobileNetV3 is applied for lightweight feature extraction, while a Variational AutoEncoder enables robust sign detection. To further improve performance, an Improved Sparrow Search Algorithm is introduced for hyperparameter tuning, constituting the novelty of this work. Experimental results show that the proposed framework achieves an outstanding accuracy of 99.05% when compared to state-of-the-art systems, validating its robustness and effectiveness for real-time SLR applications

groups

Lama Al Khuzayem mail -

Soukeina Elhassen mail

link https://doi.org/10.54216/FPA.210225

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

An Efficient Hybrid Approach Model for SARS-CoV-2 Prediction Using an Optimized Deep Learning Recurrent Neural Network and Fuzzy inference

SARS-CoV2 virus has affected the peoples in worldwide with several issues, like health and economy. Moreover, mathematical definition of fractal dimension affords a method for calculating the non-linear dynamic behaviour difficulty revealed through time series of countries. The fuzzy logic model illustrates and manages the characteristic uncertainty of classification issue. In this paper, an effectual SARS-CoV2model is developed using optimized Deep learning model through time series data. The derived features are derived from the input sequential data for disease forecasting. Moreover, over sampling scheme is exploited for data augmentation, which enhances the prediction process. Fuzzy systems and various distance measures are calculated for choosing most significant features. The Deep Recurrent Neural network (DRNN) is applied for performing SARS-CoV2prediction, in which DRNN is trained through designed Fractional Water Poor and Rich Optimization (FrWPRO) method. Meanwhile, the training process of DRNN using hybrid optimization model from scratch proves that, the designed SARS-CoV2prediction method accomplishes better performance compared to other existing approaches with Mean Square Error (MSE), Root MSE (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.1425, and 0.3775, and 0.3467 respectively.

groups

Zaid Derea mail -

Ammar Kazm mail -

Jasim Mohammed mail -

Oday Ali Hassen mail -

Esraa Saleh Alomari mail

link https://doi.org/10.54216/FPA.210227

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Secure and Efficient Novel Keystream Generator for Stream Ciphers

The Internet of Things real-time communications depend on a secure stream of data. For the secure communications, a stream cipher with the features of ease and speediness is appropriate. The development and testing of a novel cryptographic algorithm with the goal of enhancing encryption performance. This paper introduces novel A matrix-based nonlinear pseudorandom key stream generation method inspired by the principles of fundamental recursive relationship of Reinforcement Learning, aiming to enhance diffusion and randomness in stream ciphers. We also incorporate the encryption approach based on the Counter based transformation of keystream generation (CBTKSG) method to enhance the speed, which is particularly well-suited for efficiently handling large file sizes since it delivers fast throughput. The technique was thoroughly bench marked and compared to other well-known encryption schemes. Performance has significantly improved without sacrificing security, according to the data. The keystream output was placed through the NIST SP 800-22 statistical test suite to verify its cryptographic strength. It passed every test with high p-values, indicating high randomness quality. The cipher has a strong avalanche effect, meets standard security criteria like IND-CPA and IND-CCA, and resists common cryptanalysis methods including related-key, differential, and linear attacks.

groups

Chaithanya S. mail -

Siddesh G. K. mail

link https://doi.org/10.54216/JISIoT.170222

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhancing Breast Cancer Detection in CESM Mammograms: Impact of Data Augmentation on U-NET Segmentation Performance

When using mammography to diagnose breast cancer, segmenting medical scans is a crucial step.  Accurate segmentation facilitates early diagnosis, which in turn makes it possible to administer individualized treatment plans, ultimately improving patient outcomes. However, for these Deep Learning (DL) models to be trained efficiently and perform optimally, they require access to large datasets.   The lack of sufficient photographs in many publicly available datasets to adequately train deep learning models is a common flaw.   Therefore, this work aims to examine the effects of various affine data augmentations on the Dice Score of a U-NET model utilizing a recently released public dataset of Contrast-Enhanced Spectral Mammography (CESM) images.  The collection consists of 1003 CESM images and matching segmentation masks made by a certified radiologist.   Modifying certain model parameters on the CESM dataset and investigating the impact of single and combination data augmentations on the model's overall performance are the objectives of the study. Images that were moved in the x-direction and sheared vertically were used to train the best-performing model.  On the test set, the model's Dice Score was 56.6%, which was 9% better than the baseline result and showed how crucial data augmentation is when working with small datasets.

groups

Taha Y. Abdulqader mail -

Kifaa Hadi Thanoon mail -

Shatha A. Baker mail

link https://doi.org/10.54216/JISIoT.170223

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

Phishing attacks remain a persistent and ever-evolving threat to both networked systems and their users' privacy. In response to this formidable challenge, our research delves into an innovative approach designed to enhance the precision of phishing Uniform Resource Locator (URL) detection within the dynamic and programmable realm of Software-Defined Networks (SDNs). By harnessing feature selection capabilities and adaptive machine learning techniques, our proposed framework aims to fortify security measures in SDNs against these malicious campaigns. Our methodology's core is the deliberate selection of discriminative features from the extensive network data attributes. This feature selection process is meticulously designed to identify the most relevant characteristics associated with phishing URLs, thereby enabling the extraction of invaluable insights for more precise detection. These carefully chosen features then serve as inputs for a dynamic machine-learning model, trained to adapt and evolve alongside the constantly changing landscape of phishing attacks. Within the SDN environment, our framework optimizes utilizing network resources and controller processing power. It achieves this by reducing the dimensionality of input data, resulting in improved detection accuracy and a decrease in false positives. The adaptive nature of our machine-learning model ensures rapid recognition of emerging phishing tactics, thereby reducing the risk of succumbing to novel and sophisticated attacks. To validate the effectiveness of our approach, we conducted extensive experiments and evaluations within an SDN testbed, utilizing real-world phishing URL datasets. The results consistently demonstrate that our framework surpasses conventional methods, achieving higher detection accuracy and adaptability to evolving threats. In summary, our research represents a significant stride in the ongoing battle against phishing attacks by leveraging the dynamic capabilities of SDNs. The synergy between feature selection and adaptive machine learning techniques empowers SDNs to sustain accurate and effective phishing URL detection, ultimately reinforcing network security and safeguarding user privacy in an ever-evolving threat landscape.

groups

A. Usha Ruby mail -

George Chellin Chandran J. mail

link https://doi.org/10.54216/JCIM.170216

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

A MLOps Framework for Early Detection and Adjustment of Learner Behaviors in Fashion Manufacturing Technology Education

MLOps, short for Machine Learning Operations, is a practice that aims to streamline and automate the process of deploying, monitoring, and managing machine learning models in production. In the context of educational technology, MLOps can help optimize the performance of learning algorithms, ensure scalability and reliability. By implementing MLOps, educators can utilize real-time data to identify patterns of behavior that may indicate a student is struggling. This proactive approach allows timely interventions to be put in place, addressing issues before they escalate and potentially lead to academic failure. Additionally, MLOps can also help educators personalize learning experiences for students, catering to their individual needs and preferences. The participants were 60 learners enrolled in the Ready-Made Garment Manufacturing Technologies course, part of the Fashion Manufacturing Technology specialization in the Faculty of Human Sciences and Design at King Abdulaziz University. The findings of research found that integration of MLOps in educational technology has the potential to support and guide students in their learning through detecting undesirable student behaviors and adjusting early.

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Ramy Samir Mohammed ALSeragy mail -

Shadia Salah Salem mail -

Reham Mohamed Al-Ghoul mail

link https://doi.org/10.54216/FPA.210226

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Modeling Investor Trust in Supply Chain Finance: A Three-Staged MCDM Model-Based Neutrosophic Sets

Assessing investor trust is inherently complex, involving multiple interrelated factors and expert opinions that are often uncertain or inconsistent. Traditional Multi-Criteria Decision-Making (MCDM) methods face limitations in addressing such ambiguity, whereas Neutrosophic Sets provide a more robust alternative by separately modeling truth, indeterminacy, and falsity. This study proposes a three-stage Neutrosophic MCDM approach, consisting of NS-Delphi to consolidate expert input, NS-DEMATEL to analyze causal relationships, and NS-COCOSO to rank trust-related criteria, aimed at evaluating the determinants of investor trust in Vietnam’s supply chain finance (SCF) ecosystem. A case study demonstrates how this integrated model effectively captures expert hesitancy and causal interdependence. The findings highlight transparency, regulatory reliability, technological adoption, and ethical conduct as the most influential drivers of trust. Building on these insights, the study recommends several practical and policy-oriented strategies to enhance investor confidence: advancing digital transparency through blockchain and traceability systems, establishing legal safeguards to prevent financial fraud and protect investors, and promoting diversification in logistics investments to attract long-term capital and mitigate systemic risks. These implications provide a structured roadmap for policymakers, financial institutions, and SCF stakeholders seeking to foster a resilient and investor-friendly supply chain finance environment in Vietnam.

groups

Phi-Hung Nguyen mail -

Lan-Anh Thi Nguyen mail -

Thi-Lien Nguyen mail -

Anh-Phuong Danh Nguyen mail -

Hong-Nhung Thi Luong mail -

Bao-Giang Nguyen mail -

Thu-Huong Vu mail

link https://doi.org/10.54216/IJNS.270125

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2025

A Framework for Fuzzy Education Process and Neutrosophic Education Process

Numerous frameworks have been developed to address uncertainty in various domains. Among the most prominent are Fuzzy Sets, Rough Sets, Hyperrough Sets, Vague Sets, Intuitionistic Fuzzy Sets, Neutrosophic Sets, Plithogenic Sets, as well as other emerging theories that continue to be actively explored. These concepts for handling uncertainty have also been studied in the context of educational applications. In this paper, we provide formal mathematical definitions for the Fuzzy Education Process and the Neutrosophic Education Process. These educational process frameworks are applicable in a wide range of contexts, including secondary education, corporate training programs, and beyond.

groups

Takaaki Fujita mail -

Arif Mehmood mail

link https://doi.org/10.54216/JNFS.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2025

Neutrosophic–Fuzzy Multi-View Imputation for Incomplete Multivariate Measurement Data

Three reconstructions of the same missing cell can each be defensible and still disagree materially. Let e=(eN,eS,eR) denote contextual-neighborhood, low-rank, and cross-variable ridge estimates. The central question is therefore not only which value should replace xi j, but how much coherent evidence supports that replacement. Neutrosophic–Fuzzy Multi-View Imputation (NFMVI) addresses this question by assigning each source a fuzzy reliability μs ∈ (0,1] and a Gaussian concordance as = exp[−(es−c)2/(2γ2)] around a reliability-weighted center c. These quantities generate the source state (Ts, Is,Fs) = (μsas,1−as,1− μs), from which ws = Ts/Σr Tr yields the convex reconstruction bx = Σswses. The same state produces a cell-level unresolved-evidence fraction U = 1−Σs Ts/Σs(Ts+Is+Fs), so reconstruction and uncertainty are generated by one mechanism rather than by separate post-processing. Evaluation uses two real multivariate datasets, MCAR, value-dependent MAR, and structured channel deletion at 10–30%, giving 144 common deterministic masks. The evidence is deliberately mixed. On the macroeconomic panel, NFMVI attains standardized RMSE 0.3425, below KNN (0.3776), Bayesian-ridge chained imputation (0.3794), and iterative SVD (0.4987); on the diabetes covariates, NFMVI (0.7641) and chained imputation (0.7659) are nearly indistinguishable overall, with chained imputation remaining better under MAR. The uncertainty signal is more distinctive: macro error-screening AUC averages 0.8156, and RMSE rises from 0.1717 in the lowest-U quartile to 0.6202 in the highest. Paired bootstrap contrasts, correlation-structure error, ablation, and parameter sensitivity therefore support a narrower conclusion than universal accuracy dominance: NFMVI provides an interpretable fuzzy–neutrosophic rule for reconciling heterogeneous imputers while retaining cell-level evidence conflict for downstream scrutiny.

groups

Ahmed Hatip mail

link https://doi.org/10.54216/JNFS.100201

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new