On Convex Combinations of Starlike and Convex Functions Associated with the Epicycloid Domain

Nur Athirah Hani; Yuzaimi; Nur Hazwani Aqilah Abdul; Rashidah

doi:https://doi.org/10.54216/IJNS.270121

Journal of Artificial Intelligence and Metaheuristics | 2025

Hybrid CNN-LSTM Architecture for OCT Retinal Disease Classification

The ability to accurately classify retinal fundus images has been made possible by rapid improvements in deep learning (DL) and artificial intelligence (AI). This motivation led to developing a new AI-driven hybrid Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) architecture for precisely categorizing retinal diseases. The model first receives high-resolution retinal fundus images to extract various spatial properties, which are then processed by two parallel CNN branches after a standard convolutional layer. These branches use residual learning with convolutional and identity blocks to extract features. Following the reshaping and concatenation of the features from both branches, an LSTM layer captures inter-feature relationships. Eight retinal disorders are then predicted to belong to the same disease class via a fully linked classifier. Extensive simulations were run on a benchmark retinal OCT dataset, and performance was assessed using various criteria. The experimental results showed that the suggested hybrid model was adequate, with a high overall accuracy of 93% with F1-score values of 0.93, 0.94, and 0.93 for precision, recall, and accuracy, respectively. The model demonstrated considerable predictive abilities for all classes while perfectly classifying AMD, CNV, CSR, DME, DR, MH, and routine diseases to reveal its clinical value as an automated retinal processor.

groups

Ehsan khodadadi mail -

P. K. Dutta mail -

Amel Ali Alhussan mail -

Marawa Metwally mail

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

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

EEG Signal Classification for Mental States Using Deep Learning

In recent years, EEG based recognition and characterization of brain states has received much interest due to the advances in deep learning and machine learning methods. The non-invasive and highly inexpensive activity of EEG presents a patient with details concerning the activity and the conditions of the brain. The synthesis of artificial intelligence (AI) models (convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and collaborative knowledge options has been explored in a series of studies that recognize the mental state accurately in a large number of cases. The literature focuses on introducing strong, explainable models as well as on multimodal data to boost classification accurateness and reliability. The results are a 1D CNN and a LSTM network were trained separately and in a hybrid, architecture (CNN-LSTM) to classify the EEG signals. The models were appraised using accurateness, accuracy, recollection, F1-score, and confusion matrix analysis.

groups

Abdulrahman W. H. Al-Askari mail

link https://doi.org/10.54216/FPA.210220

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Multi-View Feature Learning Approach in Deep Learning Model for Improving Endometrial Cancer Detection from Medical Images

An accurate diagnosis of Endometrial Cancer (EC) is crucial for gynecologists, as different types may require specific treatments. Radiomics, a quantitative method, can help analyze and quantify image heterogeneity, aiding in lesion diagnosis. Previous research introduced a Transformer-based Semantic-Aware U-Net with Deep Endometrial Cancer Prediction (TSA-UNet-DeepECP) to segment and classify EC stages in Magnetic Resonance Imaging MRI scans. However, the heterogeneous properties of input scans can affect the DeepECP model's performance. Hence, this study presents the TSA-UNet with an Improved DeepECP model (TSA-UNet-IDeepECP) for EC stage classification. This IDeepECP model incorporates a multi-view learning approach, combining local 2D MRI image information with global 3D MRI image information. First, the endometrium MRI scans are collected, augmented, and segmented using the TSA-UNet model. Various Deep Learning (DL) models, one for 2D and one for 3D, are fed the segmented images. In contrast to the 3D view model, which collects global information from 3D MRI images, the 2D view model primarily recovers local features from 2D MRI data. The multi-view DeepECP model is trained using these combined characteristics. A Fully Connected (FC) layer and the softmax classifier are used for classifying EC stages using the combined features. When compared to traditional models, a TSA-UNet-IDeepECP model achieves better performance in EC detection from MRI images.

groups

Karthick Natarajan mail -

Nithya Palanisamy mail

link https://doi.org/10.54216/JISIoT.170215

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Identification of Post Flood Water Level Severity through UAV Images Using Attention Based Deep Learning Techniques

Floods are among the most devastating natural disasters, causing widespread damage to infrastructure, homes, and human lives. Rapid assessment of flood severity is critical for effective disaster response and resource allocation. This study explores several deep learning approaches for flood water level classification using UAV imagery. A curated dataset of 2,000 UAV images from diverse regions, including India, the United States, and Brazil, was developed and augmented to improve generalization. Multiple architectures were evaluated, including pre-trained CNNs, ResNet50v2, MobileNetv2, Vision Transformers, and Swin Transformers, with and without the Convolutional Block Attention Module (CBAM) and adaptive learning strategies. Experimental results reveal that integrating Vision Transformers with CBAM achieves the highest classification accuracy of 90.6%, while a hybrid CNN–Vision Transformer model further improves performance to 92.3%. These findings highlight the potential of attention-based hybrid models for precise flood severity mapping. The proposed framework can aid rescue teams and disaster management authorities by prioritizing high-risk areas, enabling faster response and optimized allocation of resources during emergency operations.

groups

Sanket S Kulkarni mail -

Ansuman Mahapatra mail

link https://doi.org/10.54216/FPA.210221

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Empirical Analysis of Computationally Intelligent Technique for Software Risk Prediction

Software development is inherently associated with a high degree of uncertainty, often arising from unforeseen activities during different phases of the SDLC. As software systems expand in scale and complexity, the likelihood of failures and project delays also increases. Such situations, which are usually not anticipated, are known as software risks. They arise due to different reasons, which affect activities like essentials of engineering, making, putting into usage, and test. These risks need to be identified and managed in the initial phase for delivering software-related products that are both excellent and can be relied upon. While it has been standard practice in assessing software risks to depend upon human skills and previous experiences, it has been observed they lead to issues in consistency and often are reported to be unreliable. The current study is an attempt to tackle this issue through usage of predictive models that have their roots in machine learning (ML).  Borrowing from existing data, software risks are identified and classified through five popular machine-learning tools. To improve correctness and make it more robust, selection techniques of selection with multiple features are implemented. Among the other models, the Support Vector Machine (SVM) exhibited the maximum performance, achieving a classification accuracy of approximately 80%, with a precision of 84%, recall of 80%, and an F1 score of 80%. In terms of performance, Mutual Information was found to be best in methods of applied feature selection. The study indicates the ability of ML based methods in predicting and managing software risks. Additionally, this research highlights the potential of computationally intelligent techniques to assist project managers in early risk identification, proactive decision-making and enhancing the overall success rate of s/w projects.

groups

Mohd Shabbir mail -

Rakesh Kumar Yadav mail -

Mohd Waris Khan mail -

Hitendra Singh mail

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

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Hierarchical Clustering of Global COVID-19 Statistics: Comparative Insights from Pandemic Indicators

Hierarchical clustering is applied in this research to study world COVID-19 data up to January 2025 and partition the primary clusters of countries based on epidemiological criteria. Total cases, deaths, recoveries, active cases, tests, population, and per-million were the data explored and were standardized and thereafter analyzed employing agglomerative hierarchical clustering with Ward linkage. The assessment yielded an average Silhouette of 38.5%, Davies–Bouldin value of 0.87, and Calinski–Harabasz value of 77.6, reflecting cluster validity in separation. The application of dendrograms and PCA projections to plot identified four clusters, reflecting differences in the severity of COVID-19 impacts and responses. Clustering analysis revealed that the high-burden clusters accounted for almost 45% of global death, while low-burden clusters were predominant in over 40% of nations with fewer than 100,000 accumulated instances. The outcomes illustrate hierarchical clustering as an unsupervised learning approach to analyzing epidemiological data and give quantitative estimates to facilitate comparative public health interventions across communities.

groups

Noor Razzaq Abbas mail -

Ghassan AL-Thabhawee mail -

Isam Bahaa Aldallal mail -

Mostafa Abotaleb mail -

Klodian Dhoska mail

link https://doi.org/10.54216/JAIM.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Optimizing CO2 Emission Forecasting from Vehicles Using Deep Learning and Football Optimization Algorithm

Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy due to suboptimal hyperparameter configurations. This s tudy a ims t o e nhance C O2 e mission f orecasting b y c ombining d eep l earning with advanced metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle emissions data, with hyperparameters tuned using a novel Football Optimization Algorithm (FbOA), inspired by cooperative team dynamics in football. Comparative evaluation against eight other optimizers shows that FbOA achieves the best performance. The optimized EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming all alternatives. These results demonstrate the effectiveness of domain-inspired metaheuristics in improving prediction accuracy. The proposed FbOA-EALSTM framework offers a scalable, accurate solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.

groups

Omnia M. Osama mail -

El-Sayed M. El-Rabaie mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

DenseNet201-Based Deep Transfer Learning Framework for Brain Tumor Classification in MRI Scans

The classification of brain tumors is crucial in the context of early intervention, as the appropriate and timely diagnosis can significantly influence the treatment plan and patient outcomes. Radiologists have long relied on their own judgment and have read these medical images through their own eyes, which is often subjective, time-consuming, and inter-observer variability is also likely to occur. Applications built on artificial intelligence (AI), or more specifically, deep learning (DL)-based algorithms, have radically changed the medical imaging field over the last couple of years and could potentially be used to automate the diagnosis process, offering prompt, trustworthy, and unbiased assessments. Despite such developments, most existing systems that rely on AI are constrained, especially when it comes to classification accuracy and robustness across different datasets. To overcome these problems, the article in this chapter presents a more effective DL model with a specifically designed architecture that aims to improve the classification of brain tumors. The specified methodology is based on preprocessing and data normalization steps that reduce noise and level out the data intensity, enabling effective feature extraction from the MRI images. This will increase the accuracy of the later classification. The primary component of the proposed methodology is an adapted version of DenseNet-201, designed explicitly for the four class brain tumor classification. To achieve optimal performance, the conventional output layer of DenseNet-201 was replaced with a Global Average Pooling (GAP) layer, designed to address the issues of vanishing gradients and overfitting commonly encountered during the training of deep networks. The architectural adjustment helps to combine the features and increase the overall generalization capacity of the model. The model was thoroughly tested using two datasets: one publicly available dataset on Figshare and a locally available dataset comprising a total of 3,504 T1-weighted contrast-enhanced MRI (T1-w MRI) images. The results of the experiment provided the proposed model with a general accuracy of 100 percent, which was higher than that of the existing comparative methods. Such results support the idea that complex architectural adjustments with the broader preprocessing strategy can be effective, and why deep neural networks can be viewed as trustworthy diagnostic tools in clinical neuro-oncology, potentially achieving extremely high accuracy.

groups

Doaa Sami Khafaga mail

link https://doi.org/10.54216/JAIM.100204

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Quantum-Inspired Machine Learning: Bridging Classical and Quantum Algorithms

Integration of quantum-inspired algorithms in machine learning has opened up new horizons for improving predictive performance, efficiency, and scalability across a broad spectrum of application domains. This paper presents a comparative investigation between traditional machine learning techniques and quantum-inspired models. Experimental experiments demonstrate that quantum-inspired approaches exhibit higher accuracy, training effectiveness, and stability on difficult datasets than traditional methods. Results point towards higher convergence rates, shorter runtime, and enhanced generalization capacity in quantum-inspired models, realized in the form of enhanced accuracy, precision, recall, and F1-scores. Receiver operating characteristic (ROC) and precision–recall analyses further confirm the superior discriminative power of quantum-inspired approaches. Results point toward the potential of quantum-inspired machine learning as an interface between conventional algorithms and the new frontier of quantum computing with a stepping stone to future-proof intelligent systems.

groups

Ahmed Hamid Elias mail -

Dhurgham Abbas Mohsin Albojwaid mail -

Ahmed younus abdulkadhim mail -

Raad S. Alhumaima mail -

Laith Farhan mail

link https://doi.org/10.54216/JAIM.100205

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

On Convex Combinations of Starlike and Convex Functions Associated with the Epicycloid Domain

This paper introduces the class Mε,4L, defined through a convex combination of starlike and convex functions associated with a four-cusped epicycloid domain, where the parameter satisfies 0 ≤ ε ≤ 1. Unlike earlier studies that focused on circular or conic domains, this work extends the geometric framework to epicycloidal domains. Within this framework, sharp estimates for the first coefficients are obtained, together with the Fekete-Szeg¨o inequality and the second Hankel determinant evaluations. These findings extend several classical results for starlike and convex functions and offer new perspectives on analytic function theory related to epicycloidal domains.

groups

Nur Athirah Hani Senin mail -

Yuzaimi Yunus mail -

Nur Hazwani Aqilah Abdul Wahid mail -

Rashidah Omar mail

link https://doi.org/10.54216/IJNS.270121

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new