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

Sanket S; Ansuman

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

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 lead to the loss of vision and are a significant burden to health, and a timely and accurate diagnosis should be conducted to maximize treatment and clinical outcome. The research has been applied in the holistic examination of various eye health diseases such as cataracts, glaucoma and retinary aberrations which are separated into normal eye related cases and artificial networks. Using a large set of retinal images, the study conducts a thorough quantitative analysis of both complicated models like CNN, K-NN, and SVM in the form of parameters of accuracy, sensitivity, specificity, and F-Score. The CNN model had a better performance with a fantastic overall accuracy 94.05% and good sensitivity in classifying pathological states. It can be proven by the comparative analysis that CNN architecture is an effectual diagnostic instrument in the sphere of ophthalmology and demonstrates tremendous prospects in the replication of ophthalmology screening screening with the help of ophthalmology automation. This timely and vast assessment of the machine learning methods contributes a lot to the literature not only in terms of establishing relative lines between different technological solutions but also in helping style the advanced technological solutions to carry out screening to help the ophthalmologist make reliable diagnostic prescriptions.

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 agricultural 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 Perceptron (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 the 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

Journal of Artificial Intelligence and Metaheuristics | 2025

Network Requests Classification using Advanced Metaheuristic Optimization for Enhanced Network Security Systems

The importance of network security has greatly been enhanced in the modern digital environment that continuously changes. Network security, on the other hand, is a multi-layered defense mechanism that seeks to protect networks, data, and systems from malpractices such as unauthorized access breaches or activities. Cyber threats become ever more advanced, and traditional protective measures can no longer prove to be adequate. Given the necessity of such a threat to adapt and be intelligent, an active intrusion detection system must necessarily rapidly evolve its methods in response. The central element contained in this research is the proposal of a novel algorithm, BBERSC (Balance Between Al Biruni Earth Radius Optimization and Sine Cosine Algorithm). This algorithm is carefully crafted to achieve a compromise between the means for local search provided by Al-Biruni Earth Radius Optimization and probabilistic improvement, which are characteristic of the Swine Cosine Algorithm. BBERSC brings forward the cause of harmonizing these two optimization methods to revolutionize model accuracy and credibility, which may be achieved for network security’s distinctiveness. One of the crucial elements of this study lies in the fact that hyperparameter tuning is quite a detailed process, especially for Random Forest. Parameters, including the number of trees, maximum depth, and minimum samples, are systematically employed to vary to augment pattern recognition capability by employing model processing network traffic. To ensure the validation of the effectiveness of the proposed models and algorithms, statistical analysis is carried out through ANOVA test & Wilcoxon Signed Rank Test. These tests show the models’ results through rigorous assessments and emphasize differences between them. As the conclusion of this study, It is displayed that the Random Forest model utilized inside BBERSC algorithmic framework facilitates operational accuracy level 0.9901719, which is incomparable among all other machine learning algorithms.

groups

Marwa M. Eid mail

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

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Somersaulting Spider Optimizer (SSO): A Nature-Inspired Metaheuristic Algorithm for Engineering Optimization Problems

The growing complexity of engineering optimization problems has revealed significant limitations in traditional mathematical programming approaches, necessitating the development of innovative metaheuristic algorithms capable of handling high-dimensional, multi-modal, and discontinuous objective functions. This paper presents the Somersaulting Spider Optimizer (SSO), a novel bio-inspired metaheuristic algorithm that draws inspiration from the extraordinary locomotion mechanisms of Somersaulting Spider, a desert-dwelling arachnid species renowned for its acrobatic somersaulting capabilities. The proposed algorithm incorporates dual movement mechanisms that effectively balance global exploration through somersaulting behavior and local exploitation via controlled rolling movements. A distinctive feature of SSO lies in its adaptive energy management system, which dynamically regulates exploration-exploitation transitions based on solution improvement patterns and stagnation detection. The algorithm employs complementary adaptive parameters that ensure perfect balance between global search and local refinement throughout the optimization process. Comprehensive experimental evaluation was conducted on four challenging benchmark engineering design problems: pressure vessel design, welded beam optimization, three-bar truss design, and cantilever beam optimization. A comparison with known metaheuristic algorithms, such as the Genetic Algorithm, Whale Optimization Algorithm, Harris Hawks Optimization, and Bat Algorithm, shows that SSO outperforms all of them on the test problems. ANOVA and Wilcoxon signed-rank tests statistically validate the significance of performance improvement, and SSO has the lowest optimization cost without compromising the high-performance consistency. The results confirm that SSO is an effective and powerful optimization method for complex engineering design problems, and that the method shows significant improvements in solution quality, convergence stability, and computational efficiency.

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/JAIM.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

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