Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer requiring timely and accurate diagnosis for effective treatment. Automated image-based diagnostic models offer a potential solution but often lack robustness in clinical applications. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework. The proposed model utilizes the Swin Transformer architecture combined with Momentum Contrast (MoCo) for contrastive learning. Swin Transformer’s patch-based embedding and multi-level attention capture local and global cellular features, enhancing feature discrimination across ALL subtypes. MoCo further improves feature extraction by generating distinct embeddings, reducing overlap between cell types. The model achieved an overall classification accuracy of 92.5%, with a precision of 90.3%, recall of 91.1%, and F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework demonstrated a contrastive loss reduction from 0.5 to 0.097 for benign cells, significantly enhancing feature discrimination. Additionally, an ablation study showed that omitting the dynamic queue reduced accuracy by 5%, highlighting its importance for robust feature learning. This approach can be extended to other hematologic malignancies, with potential enhancements using more extensive datasets and integration with real-time diagnostic workflows in clinical settings to support precision medicine.

Alzheimer’s disease (AD) staging via routine MRI remains challenging due to subtle morphology, class imbalance, and variability in clinical phrasing. This work presents NeuroInfinity, a lightweight prompt-driven vision–language framework that reformulates AD staging as image–text alignment on 2D T1-weighted MRI slices via a frozen CLIP ViT-B/32 encoder with minimal trainable prompt adapters. In the primary cohort, NeuroInfinity attained 96.1% accuracy, 94.1% macro-F1, and 0.983 AUC, with strong calibration (balanced accuracy of 94.2%, MCC of 0.91, ECE of 3.4%, Brier of 0.049). A targeted prompt-diversification strategy notably improves the underrepresented moderate impairment class (MoI F1 91.0%), and the model exhibits zero-shot robustness to unseen clinical phrasings (94.8% accuracy, 92.7% F1). External validation on four independent public cohorts (ImagesOASIS, ADNI, and MIRIAD) demonstrated consistent generalizability (accuracy of 94.4–95.3%) with stable calibration profiles. Designed for practical use, the framework runs at ~14 ms per slice on a mid-tier GPU and maintains linear throughput scaling. These results indicate that prompt-guided image–text alignment can deliver accurate, calibrated, and interpretable AD staging under realistic data constraints while remaining computationally efficient and extensible to prospective, multisite evaluation.

Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.

A unbearable amount of textual information accessible in electronic form and need to deliver correct answer to user question is important task in Question Answering System (QAS). Question answer system is the form of information retrieval system which aims to deliver the exact answer to the user question rather than whole document. To answer this user need semantic based reformulation techniques can be used to retrieve the accurate answer from enormous number of document retrieved from the search engine. The goal is to generate the pattern from the web based on lexical semantic and syntactic constrain. These constrain should be defined in the question answering system to evaluate and rank the candidate answer. Here we used TREC-8, TREC-9, and TREC-10 collection as training set. Different types of question and corresponding answer can use from TREC collection. The proposed system retrieves the answer automatically from TREC collection. Word net can be used to help the semantic relation and syntactic tag between the questions and answer pair. Finally weight can be given to each candidate answer according to their length, the level of semantic similarity between question and answer pair and distance between the key word. The proposed QAS be different from other reformulation based Question answering system. The experiments on the TREC data set will show the better result which can be calculated with help of the precision and recall.

Many kind of pattern integration need to be effectively analyzed in large data which require extremely accurate pattern. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. Existing patterns integration extracts low quality of pattern mapping in large databases and the systems focus only on identifying useful patterns at the attribute-value level. We propose a generalized technique to enable seamless integration of Multiple Data Sources It improves the quality of pattern reorganization significantly. Finally, experiments are conducted on few datasets, and the results of the experiments show that our method is useful and efficient.