The research work introduces a new framework which optimizes reinforcement learning with graph neural networks for detecting fraudulent transactions in unbalanced financial data. The proposed method connects community-swapping data mining techniques with multi-type anomaly detection algorithms. The method uses time-series patterns combined with structural properties and contextual features to detect fraudulent transactions. The model system uses Graph Attention Networks (GAT) connected to an RL controller which enhances fraud detection results through a reward mechanism that balances precision, computational efficiency, and community quality. The implemented system establishes superior results on IEEE-CIS data through an AUROC metric of 0.872 while achieving 0.683 average precision which leads to 15.7% increased discriminative power and 33% lower false positive rates when compared to GNN baseline models. The complete framework reached 0.839 F1-score, 0.872 AUROC, 0.683 average precision, and 0.54 MCC, demonstrating the beneficial effects of RL optimization beyond conventional accuracy. The model demonstrates improved detection of clustered fraud patterns, achieving a 19.7% gain in recall and a 33% reduction in false positives compared to baseline GNN models. Furthermore, the framework provides near real-time inference capability (average latency ~ 42 ms per batch) and scalability to transaction graphs with over 500K transactions, making it a practical option for deployment in financial institutions.

Quantum machine learning (QML) presents a promising avenue for addressing complex classification challenges, yet its application in medical imaging remains largely unexplored. This work introduces a hybrid quantum classical framework designed to classify skin diseases—Chickenpox, Measles, Monkeypox, and Normal—using a 4 qubit quantum system. The framework integrates a Quantum Convolutional Neural Network (QCNN) for feature extraction and a Variational Quantum Classifier (VQC) for classification, processing a dataset of 770 images with significant class imbalance. To address this imbalance, we employ a class weighted loss function with weights w_yi = [3.0, 3.5, 0.6, 0.4], ensuring balanced representation across all classes. The model is optimized using Simultaneous Perturbation Stochastic Approximation (SPSA) over 150 iterations, achieving a test accuracy of 70.13% on a subset of 154 images, which represents 20% of the dataset. This marks a substantial improvement over both a random baseline of 25% and a prior run that achieved 37.66% accuracy, which was skewed toward the majority class. Principal Component Analysis (PCA) reduces image features to a 4 dimensional representation, enabling compatibility with the quantum system. The QCNN extracts these features, while the VQC performs classification based on measurements from 1024 shots on the QASM simulator. To ensure practical reusability, the trained model—including quantum circuits, optimized parameters, and PCA transformations—is saved for future deployment. Validation on a separate set of 24 samples (6 per class) yields 70.83% accuracy (17 out of 24 correct predictions), demonstrating the model’s generalization capabilities. While the performance falls short of classical benchmarks, this study establishes the feasibility of QML with minimal qubits, paving the way for quantum driven diagnostics in resource constrained environments.

Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and avoiding bias in this field. This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2ES) dataset, for affective computing research. The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various ethnicities. The subjects were exposed to 25 carefully selected audio–visual stimuli to elicit specific targeted emotions. An analysis of the participants’ self-assessment and a list of the 25 stimuli utilised are also presented in this work. Emotion recognition systems are built using ECG and PPG data; five machine learning algorithms: support vector machine (SVM), k-nearest neighbour (KNN), naive Bayes (NB), decision tree (DT), and random forest (RF); and deep learning techniques. The performance of the systems built are presented and compared. The SVM was found to be the best learning algorithm for the ECG data, while RF was the best for the PPG data. The proposed database is available to other researchers.

More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.

Confidentiality and access control are essential to protect sensitive data, prevent cyber threats, ensure compliance, and avoid risks like identity theft. Hence, a framework towards secure patient Data access using Hybrid Integrated Hashing Method is introduced to ensure patient confidentiality and efficient data access in healthcare systems. Unlike conventional solutions that rely solely on standard blockchain and secure hash algorithm 256 for data protection, this proposed method integrates a multi-layer hybrid hashing approach combining dynamic hash chaining with temporal entropy encoding, making hash collisions virtually infeasible. A selective data compression mechanism is also embedded to maintain performance while preserving cryptographic strength. Additionally, the system employs role-based decentralized access control, enforced through smart contracts, enabling real-time permission verification and immutable audit trails. A simulated blockchain environment evaluates the proposed method’s resilience against ransomware, hash collision, and data manipulation attacks. By employing a standard secure hash algorithm 256 hashing without compression or access-layer optimization, experimental findings show 27% reduced storage usage and 35% quicker data retrieval than typical blockchain-based electronic health record systems. The system shows robust resistance to illegal access compared to traditional role-based access control systems.