SCNN-UNet: A Novel Deep Learning Approach for Pulmonary Embolism Detection in COVID-19 Patients Using Super Pixel Segmentation

Sukhwinder; Vijay

doi:https://doi.org/10.54216/JISIoT.160214

International Journal of Neutrosophic Science | 2025

Neutrosophic Analysis for the Future of Artificial Intelligence in Language Education

The neutrosophic set, a mathematical framework that accounts for truth, indeterminacy, and falsity, plays a crucial role in enhancing artificial intelligence (AI)-driven language education. By integrating neutrosophic logic, AI systems can better handle linguistic ambiguities, dynamically adapt learning materials, and offer more precise and personalized feedback. This paper explores the application of neutrosophic theory in intelligent tutoring systems (ITS), natural language processing (NLP), and AI-assisted feedback mechanisms, all within an uncertainty-based framework. Through the incorporation of neutrosophic models, AI can more effectively assess learner responses by considering elements of truth, uncertainty, and falsehood, leading to more adaptive and context-aware language instruction. Furthermore, the study highlights how AI, powered by neutrosophic logic, contributes to breaking language barriers, increasing accessibility, and fostering inclusive learning environments. Ethical concerns, bias mitigation, and data privacy challenges in AI-driven language learning are also addressed, emphasizing the need for responsible AI implementation. Finally, the paper underscores the synergistic balance between AI and human educators, advocating for adaptive AI frameworks that enhance linguistic comprehension while ensuring pedagogical integrity. Future research directions focus on leveraging neutrosophic logic to further improve AI's reliability, adaptability, and overall effectiveness in personalized language education.

groups

Hilal Abdul-Raziq Sadiq mail -

Shakirova Zulfiya Normahamatovna mail -

Mullasadikova Nigora Muramanovna mail -

Madayeva Mu‘tabarxon Amanullayevna mail -

Askarov Abdurashid Murodjonovich mail

link https://doi.org/10.54216/IJNS.260219

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Descent Conjugate Gradient Method for Large Scale Unconstrained Optimization Problems with Application

In recent years, there has been a surge of attention to the Conjugate Gradient Method (CGM) and its applications. This is because the algorithm of CGM does not require the computation of the second derivative or an approximation during the iteration process. In this study, a four-term descent CGM is proposed by utilizing the famous Polak–Ribiere–Polyak (PRP) conjugate gradient formula. The direction of the proposed method achieves the descent property without line search consideration. In addition, the convergence properties are met to generate the stationary points. Findings from numerical experiments on unconstrained optimization and robotic motion control problems demonstrate that the novel approach outperforms some existing methods including the famous CG-Descent conjugate gradient method.

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Ahmad Alhawarat mail -

Sultanah Masmali mail -

Ibrahim M. Sulaiman mail -

Issam A. R. Moghrabi mail -

Norazura Ahmad mail -

Shahrina Ismail mail

link https://doi.org/10.54216/IJNS.260220

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Study Neutrosophic Quasi-Frobenius by Local and Artinian Rings

In this paper, we study the relationships between the Neutrosophic quasi-Frobenius rings and the Neutrosophic of local rings and Artinian rings. In addition, we present study the relationship between the Neutrosophic quasi-Frobenius ring and some concepts such as Neutrosophic semisimple ring, Neutrosophic module injective and Neutrosophic Noetherian ring. Finally, we introduce some mathematical formulas with an commutative, coherent and Neutrosophic perfect ring, through which we obtain the Neutrosophic quasi-Frobenius ring.

groups

Omar A. Khashan mail -

Majid M. Abed mail

link https://doi.org/10.54216/IJNS.260222

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Multi Chronic Disease Prediction by Fine Tuning Random Forest using Social Group Optimization

Accurate disease prediction is essential for enabling preventive healthcare and reducing the burden of chronic illnesses. This study introduces an innovative multi-disease prediction framework leveraging machine learning and optimization techniques to address limitations in precision and scope present in prior research. Specifically, we focus on predicting five major diseases—diabetes, heart disease, kidney disease, liver disease, and breast cancer—by employing the Social Group Optimization (SGO) algorithm to fine-tune the Random Forest (RF) classifier's hyperparameters.The proposed SGO-optimized RF model optimizes seven critical parameters - n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, and criterion simultaneously, significantly enhancing the model's performance. Our approach, applied to five disease datasets, achieves notable accuracy improvements: 98.25 When tested on diverse datasets, the model achieves exceptional accuracy: 98.25% for breast cancer, 84.62% for liver disease, 93.44% for heart disease, 82.47% for diabetes, and 100% for chronic kidney disease. On average, the SGO-optimized RF outperforms existing methods with a 2.3% accuracy improvement across datasets. This research highlights the transformative potential of SGO-based optimization in advancing the accuracy and reliability of predictive models. The results demonstrate the framework's applicability in clinical scenarios, providing precise and actionable insights that support early diagnosis and improve patient outcomes.

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Sudhirvarma Sagiraju mail -

Jnyana Ranjan Mohanty mail -

Anima Naik mail

link https://doi.org/10.54216/FPA.190225

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

ML-kNN-H: A Multi-Label Classification Model based on Hoeffding’s Inequality

Multi-label data stream classification plays a crucial role in various applications, including recommendation systems, real-time monitoring systems, smart cities, social media analysis, and healthcare. Its ability to classify constantly generated, potentially unbounded data at a high rate is of utmost importance. Besides accommodating multiple labels, data streams may evolve due to concept drift and bias toward particular classes due to class imbalance. This research introduces the multi-label classification model based on Hoeffding inequality (ML-kNN-H). The proposed model aims to process multi-label data streams, handle concept drift, and class imbalance. ML-kNN-H removes instances introducing errors based on a dynamic value computed from the Hoeffding inequality instead of a fixed value, thereby enhancing the model's efficiency and applicability to different types of data streams. Several experiments have been conducted to assess the model's performance in the presence of concept drift (abrupt and gradual drift) and class imbalance. Particularly, it has been evaluated against six kNN multi-label classifiers on ten datasets: synthetic and real world. The results indicate that ML-kNN-H outperformed the other classifiers on benchmark datasets in terms of Subset Accuracy, Accuracy, Hamming Score, and F-score, except in running time. Statistical analysis has also been utilized to measure the significance of the ML-kNN-H compared to the state-of-the-art classifiers.

groups

Mashail Althabiti mail -

Manal Abdullah mail -

Omaima Almatrafi mail

link https://doi.org/10.54216/FPA.190226

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

An Intelligent Fusion Framework of Deep Learning with Secretary Bird Optimization Algorithm for Named Entity Recognition in Arabic Language Texts

As increasingly Arabic textual data becomes accessible through the Intranet and Internet services, there is an important requirement for technologies and devices to handle the related data. Named Entity Recognition (NER) is an Information Extraction task that became a major part of several other Natural Language Processing (NLP) tasks. NER for Arabic has been obtaining improving attention, but possibilities for development in performance are even accessible. In recent decades, the Arabic NER (ANER) task has been confined to great effort to increase its performance. The ANER difficult task is to collect vast corpora or immense white gazetteers/lists that address probably the majority of Arabic language challenges like complexity, orthography, and ambiguity. Recently, deep learning (DL) has been the most typically applied NER model in the Arabic language and others. DL methods utilize the features of words and text to identify NEs. This paper presents a Secretary Bird Optimization Algorithm for Enhancing Fusion Deep Learning in Arabic Named Entity Recognition (SBOFDL-ANER) model. The main intention of the SBOFDL-ANER technique is to develop an effective method for NER in Arabic text. At first, the text pre-processing stage is applied to clean and transform the raw text into a structured format for analysis. Next, the word embedding method has been implemented by the Word2Vec method. Besides, the proposed SBOFDL-ANER technique designs ensemble models such as deep belief network (DBN), elman recurrent neural network (ERNN), and multi-graph convolutional networks (MGCN) for the process of classification. Eventually, the secretary bird optimization algorithm (SBOA) implements the hyperparameter choice of ensemble models. A wide-ranging simulation was applied to verify the performance of the SBOFDL-ANER method. The experimental outcomes demonstrated that the SBOFDL-ANER model highlighted improvement over other existing methods

groups

Ebtesam Hussain Almansor mail

link https://doi.org/10.54216/FPA.190227

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Integrating Tent Chaotic Dung Beetle Optimization with Deep Ensemble Learning for Diabetic Retinopathy Recognition on Fundus Imaging

Diabetic Retinopathy (DR) is a general difficulty of diabetes mellitus, resulting in retina damage that affects vision. If left undetected, it has the potential to cause blindness. Regrettably, DR is irreversible, and only treatment can maintain vision. The early analysis and treatment of DR can considerably decrease the potential for visual impairment. Unlike computer-aided diagnosis (CAD) systems, the manual diagnostics method of DR retinal images by ophthalmologists is effort-, cost-, and time-consuming and liable to misdiagnoses. In present scenario, deep learning (DL) has become the classical approach that has remarkable performance in different fields, mainly in medical image classification and analysis. Convolutional neural networks (CNN) are more commonly deployed as a DL system in medical image analysis and they are very efficient. In this manuscript, we offer the design of Tent Chaotic Dung Beetle Optimization with Deep Ensemble Learning for Diabetic Retinopathy (TCDBO-DELDR) Recognition approach on Fundus Imaging. The foremost intention of the TCDBO-DELDR technique is to automate the DR detection process on fundus images via the ensemble DL model. To eradicate the noise, the TCDBO-DELDR technique initially exploits the median filtering (MF) methodology. In the TCDBO-DELDR model, the Inception v3 (IV3) model is employed for the purposes of feature extractor. For the hyperparameter tuning procedure, the TCDBO technique is used for IV3 model. Finally, the detection of DR is carried out utilizing an ensemble of three classifiers namely Deep Feedforward Neural Network (DeepFFNN), Convolutional FFNN (ConvFFNN), and Convolutional bi-directional long short-term memory (ConvBLSTM). For ensuring the enhanced efficiency of the TCDBO-DELDR system in the DR detection procedure, a widespread experimental study is prepared on the benchmark DR database. The results illustrate the superior efficiency of the TCDBO-DELDR technique with other recent DL approaches.

groups

Arwa Darwish Alzughaibi mail -

Ashrf Althbiti mail -

Sultan Ahmed Almalki mail -

Mohammed Al-Jabbar mail -

Mohammed Alshahrani mail

link https://doi.org/10.54216/JISIoT.160212

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Blockchain with IoT Integrated Framework for Tourism Service Customization and Management

The Internet of Things (IoT) has extensively converted the industry of tourism, reforming travel design, supply, and experiences. The technology of Blockchain (BC) signifies a paradigm shift with the latent to transform many industries, more like spreadsheets altered office efficiency. BC technology provides frequent potential advantages to the tourism industry, with enhanced transparency, security, and efficacy in regions such as payments, bookings, and identity verification, which potentially mains to a more perfect and reliable travel experience. In the tourism region, BC with IoT is mainly attractive owing to the latent benefits it provides in terms of improving the experience of tourism, enhancing operational efficacy, and guaranteeing data security and transactions. Recently, numerous scholars globally have employed deep learning (DL) technology in the industry of tourism to combine physical and social influences for improved travel recommendation services. This study presents a Blockchain for Tourism Service Customization and Management using Whale‐goshawk Optimization Algorithm (BCTSCM-WOA) technique. The main goal of the BCTSCM-WOA method relies on improving the effectual model for tourism service customization. Initially, blockchain technology is applied to provide secure, transparent, and decentralized solutions for handling traveler data, payments, and service personalization. Then, the data pre-processing employs min‐max scaling to transform input data into a suitable format. Besides, the crayfish optimization algorithm (COA) to select the most relevant features from the data has executed the feature selection procedure. For the classification process, the proposed BCTSCM-WOA method projects multi-dimensional attention-spiking neural network (MASNN) technique. At last, the parameter tuning process is performed through the whale‐goshawk optimization (WGO) algorithm for refining the classification performance of MASNN model. The experimental evaluation of the BCTSCM-WOA algorithm has been examined on a benchmark dataset. The extensive outcomes highlight the significant solution of the BCTSCM-WOA approach to the classification process when compared to existing techniques.

groups

Samer Yaghmour mail

link https://doi.org/10.54216/JISIoT.160213

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On the generalized numerical radii of operators

It is shown that if A, B,X, and Y are operators acting on a finite dimensional Hilbert space, then. ωu (AXB&lowast; ± BYA&lowast;) ≤ 2 ∥A∥ ∥B∥ ωu ([0 X, Y 0]) where ωu (T ), ∥T ∥, are, respectively, the U-numerical radius, the spectral norm, of an operator T .

groups

M. Abu Saleem mail -

Khalid Shebrawi mail -

Tasnim Alkharabsheh mail

link https://doi.org/10.54216/IJNS.260221

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

SCNN-UNet: A Novel Deep Learning Approach for Pulmonary Embolism Detection in COVID-19 Patients Using Super Pixel Segmentation

Inventory management is crucial for optimizing consumer demand and supply chains in e-commerce companies.  This is the stage at which precise inventory forecasting becomes necessary for forecasting future demand patterns and stock levels.  Traditional forecasting methods often struggle with e-commerce data due to seasonality, sudden changes in customer behavior, and nonlinearity.  Machine learning (ML) and deep learning (DL) techniques have become powerful weapons for inventory prediction because they can analyze huge amounts of data with high dimensionality. E-commerce firms can improve their resource allocation, inventory management, and customer experience in highly competitive market environments.  This paper proposes different types of inventory forecasting models and mainly evaluates the applicability of sophisticated machine learning algorithms.  While we commonly use old methods like Random Forest, ARIMA, and MLPs, they often lack the necessary robustness to nonlinearity within inventory data.  To address these problems, we introduce a novel method that combines convolutional neural networks (CNN) and XGBoost called CNN-XGBoost, which provides better feature extraction than the conventional prediction model and regression performance.  We then compared CNN-XGBoost's performance to traditional forecasting methods (another common approach to contextualizing predictive model performance) using a 52-week simulated dataset in which we mimic patient data growing over time.  We used key performance metrics such as R2, mean squared error (MSE), and mean absolute percentage error (MAPE) to assess each model's accuracy.  The CNN-XGBoost model performed much better than others, with an R2 of 0.78, which means our proposed model can explain more variation compared to other competitors, as depicted in the results section.  It also had the best MSE of 0.15, indicating better predictive performance.  The CNN-XGBoost model demonstrated promising prospects as a robust inventory forecasting tool for commerce despite its slightly higher MAPE value (0.69), suggesting some vulnerability to outlier data points.  This study demonstrates the potential of using a convolutional neural network in combination with gradient boosting techniques to tackle the complexity of stock management issues and the fact that it outperforms based line methods by a large margin.

groups

Sukhwinder Bir mail -

Vijay Dhir mail

link https://doi.org/10.54216/JISIoT.160214

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new