Journal of Artificial Intelligence and Metaheuristics

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. Experi-ments 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.

The classification of brain tumors is crucial in the context of early intervention, as appropriate and timely diagnosis can significantly influence treatment plans and patient outcomes. Radiologists have long relied on their own judgment to read medical images, a process that can be subjective and time-consuming and can lead to inter-observer variability. Artificial intelligence (AI), and particularly deep learning (DL), has transformed medical imaging and could automate diagnosis by providing prompt, reliable, and unbiased assessments. Despite this progress, many AI-based systems remain constrained in their classification accuracy and robustness across datasets. This paper presents a deep-learning model designed to improve brain-tumor classification. Its preprocessing and normalization steps reduce noise and equalize image intensity to support feature extraction. The proposed method adapts DenseNet201 for four-class classification and replaces its conventional output layer with Global Average Pooling (GAP), aiming to address vanishing gradients and overfitting while improving feature aggregation and generalization. The model was evaluated on a public Figshare dataset and a local dataset comprising 3,504 T1-weighted contrast-enhanced MRI images. It achieved 100% overall accuracy, outperforming the comparison methods. These results suggest that architectural modifications combined with preprocessing can support automated brain-tumor classification under the evaluated conditions.

Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy because of suboptimal hyperparameter configurations. This study enhances CO2 emission forecasting by combining deep learning with metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle-emissions data, with hyperparameters tuned using the 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 FbOA–EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming the alternatives reported in the study. These results demonstrate the potential of domain-inspired metaheuristics to improve prediction accuracy. The proposed framework offers a scalable solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.

Hierarchical clustering is applied to global COVID-19 data from the Worldometer tracker, whose final update was April 13, 2024, to partition countries into clusters based on epidemiological indicators. Total cases, deaths, recoveries, active cases, tests, population, and per-million measures are standardized and analyzed using agglomerative hierar-chical clustering with Ward linkage. The evaluation yields an average Silhouette score of 38.5%, a Davies–Bouldin value of 0.87, and a Calinski–Harabasz value of 77.6. Dendrograms and principal component analysis (PCA) projections identify four clusters, reflecting differences in pandemic impact and response. The analysis finds that high-burden clusters account for almost 45% of global deaths, while low-burden clusters predominate in more than 40% of nations with fewer than 100,000 accumulated cases. The results illustrate how hierarchical clustering can support comparative public-health analysis by providing quantitative estimates of epidemiological patterns across countries.

Optical coherence tomography (OCT) provides detailed retinal images, but their interpretation can be difficult across clinical settings and patient groups. This paper presents a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture for classifying retinal OCT images into eight disease categories. Two parallel convolutional branches extract spatial features; their outputs are pooled, reshaped into a sequence, and processed by an LSTM layer to model inter-feature relationships before dense classification. The model is evaluated on 24,000 images and compared with ResNet50, MobileNetV2, InceptionV3, and Xception. The proposed architecture achieves 93.00% accuracy, 94% precision, 93% recall, and a 0.93 F1-score, outperforming the compared models. The results indicate that combining convolutional feature extraction with sequence modeling is a promising approach to automated retinal disease classification.