Brain Stroke Detection in CT Images Using Transfer Learning and Deep Learning Models

Hussein Alkattan; Mostafa Abotaleb

doi:https://doi.org/10.54216/JAIM.100103

Journal of Intelligent Systems and Internet of Things | 2025

Greylag Goose Optimization for Feature Selection and Hyperparameter Tuning in Chronic Kidney Disease Detection

Chronic Kidney Disease (CKD) is a global health concern that necessitates accurate and timely detection to improve patient outcomes and reduce healthcare costs. This study focuses on enhancing CKD classification using machine learning techniques, leveraging 400 instances with 25 clinical features to predict binary outcomes of CKD or non-CKD. The main objective is to improve detection accuracy by applying feature selection and model optimization. Standard machine learning models, including Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were employed, with optimization achieved through binary optimization algorithms such as Greylag Goose Optimization (GGO), Particle Swarm Optimization (PSO), Bat Algorithm (BA), and Whale Optimization Algorithm (WAO), along with hyperparameter tuning using genetic algorithms and other metaheuristics. Results indicate significant improvements in classification performance after feature selection and optimization, with the GGO-optimized MLP model achieving an accuracy of 97.06%. The contributions of this paper include (i) benchmarking baseline models for CKD detection, (ii) a comprehensive analysis of feature selection strategies, (iii) optimization of machine learning models for CKD classification, and (iv) visualization of model performance to aid future research in healthcare machine learning applications.

groups

Mohamed Saber mail -

Ebrahim A. Mattar mail -

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.170211

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Forecasting CO2 Emissions from Cement Manufacturing with iHOW-Tuned Machine Learning Models

Cement production is a major contributor to global CO2 emissions, posing a challenge for climate mitigation efforts. Accurate forecasting of these emissions is vital for guiding policy and industrial decarbonization. This study addresses the need for improved predictive frameworks by developing an optimized ensemble-based machine learning model for CO2 emissions forecasting. The model is trained on a corrected global cement emissions dataset and enhanced through hyperparameter tuning using ten metaheuristic algorithms. Among them, the Improved Henry’s Optimization Algorithm (iHOW) achieved superior performance. The iHOW-optimized model attained an MSE of 1.21×10−6 and R2 of 0.9657, improving over the best baseline model (Gradient Boosting: MSE = 0.0164, R2 = 0.8621) by more than 99%. These results confirm the effectiveness of iHOW in producing accurate and reliable forecasts. The proposed framework offers strong potential for integration into carbon tracking systems and policy support tools.

groups

Omnia M. Osama mail -

El-Sayed M. El-Rabaie mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JISIoT.170212

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Optimizing Earthquake Prediction Accuracy using Somersaulting Spider Optimizer for Dynamic Ensemble Weighting

Earthquake prediction is one of the most challenging problems in geophysical science, and conventional approaches have proven arduous in capturing the complexity and non-linearity of seismic measurements. The multidimensional nature of earthquake variability, along with class imbalance and the strong dependence of prediction results on hyperparameters, necessitates the development of more robust and flexible predictive models. In this paper, we introduce a bio-inspired ensemble learning method based on the Somersaulting Spider Optimizer (SSO) for dynamically adjusting classifier weights in earthquake classification. The proposed method addresses limitations of existing weighting strategies, which primarily focus on maximizing classifier contribution based on performance characteristics. Experiments were conducted on an earthquake dataset augmented with features modeled and mapped by time, space, and magnitude to capture patterns of seismic events. We compared the SSO-optimized ensemble with BaggingClassifier, CatBoost, HistGradientBoosting, LightGBM, and DecisionTree, as well as traditional ensemble approaches. Results show that the SSO-boosted ensemble achieved superior performance, with an accuracy of 97.01%, sensitivity of 97.04%, specificity of 99.36%, precision of 97.64%, and an F1-score of 97.33%, outperforming other models and traditional ensembles. These improvements were confirmed statistically using Wilcoxon signed-rank tests, while visual analyses demonstrated enhanced stability and generalization. Overall, the integration of bio-inspired optimization and ensemble learning shows strong potential to overcome challenges in earthquake forecasting and to support reliable early warning and disaster preparedness systems.

groups

Ahmed Mohamed Zaki mail -

Hala B. Nafea mail -

Hossam El-Din Moustafa mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.180227

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Best Proximity Point Theorems in Neutrosophic Complete Metric Spaces

In this work, we introduce the notion of best proximity point for a non-self map defined in a neutrosophic complete metric space. Moreover, we define the class of neutrosophic proximal contraction of first kind and second kind, and we prove theorems which ensures existence and uniqueness of best proximity point for such mappings in neutrosophic complete metric spaces. Additionally, a technique to identify an optimal approximation solution intended as a best proximity point is demonstrated.

groups

A. Sreelakshmi Unni mail -

V. Pragadeeswarar mail -

Manuel De La Sen mail

link https://doi.org/10.54216/IJNS.270119

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Solving Unconstrained Minimization Problems and Training Neural Networks via Enhanced Conjugate Gradient Algorithms

Artificial neural networks have become a cornerstone of modern artificial intelligence, powering progress in a wide range of fields. Their effective training heavily depends on techniques from unconstrained optimization, with iterative methods based on gradients being especially common. This study presents a new variant of the conjugate gradient method tailored specifically for unconstrained optimization tasks. The method is carefully designed to meet the sufficient descent condition and ensures global convergence. Comprehensive numerical testing highlights its advantages over traditional conjugate gradient techniques, showing improved performance in terms of iteration counts, function evaluations, and overall computational time across a variety of problem sizes. Additionally, this new approach has been successfully used to improve neural network training. Experimental results show faster convergence and better accuracy, with fewer training iterations and reduced mean squared error compared to standard methods. Overall, this work offers a meaningful contribution to optimization strategies in neural network training, displaying the method is potential to tackle the complex optimization problems often encountered in machine learning.

groups

Bassim A. Hassan mail -

Issam A. R. Moghrabi mail -

Talal M. Alharbi mail -

Alaa Luqman Ibrahim mail

link https://doi.org/10.54216/IJNS.270120

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoT and AI for Clinical Decision Support with Hierarchical Attention

The integration of Clinical Informatics (NI) and Artificial Intelligence (AI) promises to transform healthcare by improving clinical decisions, optimizing workflows, and personalizing patient care. However, most current systems fail to incorporate contextual reasoning, real-time adaptation, or ethical sensitivity, leading to fragmented support and increased cognitive burden on clinicians. To address these limitations, we propose NI-AIH—a hybrid clinical-AI framework built on a Context-Enriched Hierarchical Attention Network (CE-HAN). This deep architecture employs dual-attention mechanisms to interpret structured and unstructured clinical data—including EHR entries, nursing notes, and real-time IoT sensor feeds—capturing temporal patterns and contextual cues essential to patient status. The NI-AIH framework consists of four core components: a Clinical Context Engine (CCE) that uses CE-HAN for semantic modeling; a Predictive Care Optimizer (PCO) that applies risk-stratified deep ensembles; an Adaptive Interaction Layer (AIL) that enables seamless nurse–AI collaboration; and an Ethical Decision Integrator (EDI) that uses fuzzy logic to ensure real-time ethical alignment. In a trial deployment within a smart geriatric care unit, NI-AIH demonstrated a 23% improvement in early sepsis detection (p<0.01), a 31% reduction in clinician cognitive load (measured via NASA-TLX survey), and a 19% increase in workflow efficiency compared to conventional rule-based systems. By uniting clinical precision with ethical and context-aware intelligence, NI-AIH establishes a new paradigm for compassionate and effective AI-assisted healthcare.

groups

Sasikumar M. S. S. mail -

Ranganayaki V. C. mail -

R. Suganthi mail -

Nalini Subramanian mail -

T. Sethukarasi mail -

T. A. Mohanaprakash mail

link https://doi.org/10.54216/JISIoT.170213

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid Routing and Efficient Mobility Model with Ant Optimization in Mobile Ad-Hoc Networks

Mobile Ad hoc Networks (MANETs) are emerging technologies used to transfer data across locations within both infrastructure-less and infrastructure-based network models. To ensure quality communication among mobile devices in various applications, an efficient routing model and an optimal data transfer path are essential, helping to reduce delay and power consumption during transmission. This article focuses on 'A hybrid routing and efficient mobility model with ant optimization' (HEMAOM). HEMAOM introduces a novel hybrid routing approach combined with an energy-efficient optimization model to lower power consumption and improve data transmission. Using an energy model, power usage during data transfer is minimized, boosting overall efficiency. Additionally, an optimization model is developed to identify the best path for data transfer between areas. These processes collectively decrease delay and power consumption, enhancing the communication performance of mobile devices. Compared to state-of-the-art methods like EOMFM, OLSRM, and MPOUA, HEMAOM shows superior performance in energy efficiency and data delivery. The model is implemented using NS3 software, considering parameters such as packet delivery ratio, network throughput, average delay, energy efficiency, and routing overhead.

groups

Mohammed Ahmed Jubair mail -

Marwan Harb Alqaryouti mail -

Mohammed Ihsan Hashim mail -

Ala Eddin Sadeq mail -

Rabei Raad Ali mail -

Mohamed Doheir mail

link https://doi.org/10.54216/JISIoT.170214

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Machine Learning Algorithm Comparison for Four-Class Retinal Disease Classification Using Digital Fundus Images

Retinal diseases are a major cause of vision loss, making timely and accurate diagnosis important for effective treatment. This study compares convolutional neural networks (CNN), k-nearest neighbors (K-NN), and support vector machines (SVM) for classifying digital fundus images into four categories: normal retina, cataract, glaucoma, and other retinal diseases. The models are evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score. CNN achieves the highest reported accuracy (94.05%) and sensitivity (0.9977), with an F-score of 0.9693. The results indicate that CNN can be useful for automated retinal screening, while its low specificity (0.0178) highlights the need to reduce false-positive predictions. The comparison illustrates both the potential and the limitations of machine-learning-based retinal disease classification.

groups

Nima Khodadadia mail -

Benyamin Abdollahzadeha mail

link https://doi.org/10.54216/JAIM.100101

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Apple Quality Classification Using a Metaheuristic-Optimized Machine Learning Framework

This study presents a comprehensive evaluation of metaheuristic-optimized machine learning models for automated apple quality classification, addressing the critical need for accurate and consistent fruit grading systems in agricul-tural applications. The research integrates four bio-inspired optimization algorithms—Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Cuckoo Search (CS), and Bat Algorithm (BAT)—with Multi-Layer Percep-tron (MLP) classifiers to enhance fruit quality assessment performance. Experimental validation was conducted using a comprehensive apple quality dataset containing seven key attributes: size, weight, sweetness, crunchiness, juiciness, ripeness, and acidity. The results demonstrate that WOA-MLPClassifier achieves superior performance with 95.37%accuracy, 95.99% sensitivity, and balanced effectiveness across all evaluation metrics including specificity, positive predictive value, negative predictive value, and F1 Score. Statistical validation through one-way ANOVA and Wilcoxon signed-rank tests confirms significant performance improvements over baseline models and alternative optimization approaches, with p-values less than 0.001. The proposed framework exhibits remarkable consistency across multiple evaluation runs, with perfect positive rank sums indicating reliable optimization behavior. These findings establish a new benchmark for automated fruit quality classification systems and provide valuable insights for deploying bio-inspired optimization techniques in agricultural machine learning applications where both accuracy and reliability are essential for commercial viability.

groups

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JAIM.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Brain Stroke Detection in CT Images Using Transfer Learning and Deep Learning Models

A brain stroke represents a deadly health condition that emerges from poor blood flow to the brain. Brain tissue affected by a stroke will completely cease regular operations. Immediate detection of a brain stroke leads to better treatment success. Images from computed tomography (CT) provide a quick diagnosis of stroke. But time is passing quickly as physicians examine each brain CT scan. This situation could cause therapy to be delayed and mistakes to be made. Thus, we focused on using a practical artificial intelligence algorithm for stroke detection. This paper proposes several deep neural network models, such as DenseNet121, ResNet50, Xception, and EfficientNetV2S, for transfer learning to study the features of stroke lesions and achieve complete intelligent automatic detection by classifying CT images into two categories (stroke and normal). The dataset comprises 437 testing, 235 validation, and 1843 training photos. Using the same dataset, the experimental findings outperform all state-of-the-art. The optimal model utilizing the EfficientNetV2S model for transfer learning has an overall accuracy of 99.57% and the same value for precision and recall.

groups

Hussein Alkattan mail -

Mostafa Abotaleb mail

link https://doi.org/10.54216/JAIM.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new