This paper presents a lossless image-encryption framework combining two non-uniform reversible second-order cellular-automaton passes with a hybrid logistic–SHAKE permutation applied between them in the image bit lattice. A domain-separated SHAKE-256 key schedule derives the rule fields, pre-whitening masks, permutation controls, and optional scan-diffusion controls from a master key and a fresh public nonce. After the first cellular automata pass, the same bit permutation is applied to both consecutive states before the second pass. The permuted reference state is retained as auxiliary data for inversion, and optional byte-domain scan diffusion provides additional mixing. Experiments on six RGB and two grayscale images verified byte-exact recovery with the correct key and nonce and intact encrypted data, including the auxiliary state. The encrypted images had a mean entropy of 7.9994 bits per 8-bit sample and near-zero adjacentpixel correlations. In the reported single-pixel plaintext-perturbation tests, mean ciphertext NPCR and UACI were 99.6104% and 33.46%, respectively. Runtime profiling identified permutation-index generation as the dominant cost, while ciphertext-corruption tests showed extensive error propagation.
The Patient Health Questionnaire-9 (PHQ-9) is a clinically validated tool for assessing depression severity across symptoms such as mood, energy, sleep, and appetite. However, standard PHQ-9 administration relies on fixed categorical response options, which may not capture the nuance and contextual detail present in natural language descriptions of symptoms. This study proposes a hybrid, transfer-learning approach to predict continuous PHQ-9 total scores from 250 students’ semi-structured free-text responses. Our model uses a domain-adapted transformer backbone within the Sentence-Transformers framework to encode each PHQ-9 item response into a dense embedding, and feeds the resulting nine-embedding sequence into a bidirectional LSTM regressor to capture cross-item dependencies. The encoder is fine-tuned on response pairs to better align the embedding space with PHQ-9–related semantics. Evaluated using k-fold cross-validation, the proposed model achieves an average MAE of 1.59, and attains over 97% accuracy after mapping predicted scores to standard PHQ-9 severity categories, outperforming multiple transformer-based encoder baselines and showing more consistent performance across folds. These findings suggest that NLP-based modeling can support PHQ-9 screening by estimating severity scores from natural-language responses in student populations, complementing conventional questionnaire scoring.
Dementia, particularly Alzheimer’s disease (AD), poses a major global health challenge, currently affecting over 55 million people and projected to reach 152 million by 2050. While neuroimaging-based approaches have demonstrated high diagnostic performance, there is growing interest in inexpensive, non-invasive alternatives such as handwriting-derived digital biomarkers. Handwriting analysis, as provided by the DARWIN dataset, can capture subtle motor and cognitive decline using widely available digital devices. Recent machine learning (ML) and DL studies on handwriting-based AD detection show promise but often rely on complex architectures, large datasets, and provide limited transparency regarding feature importance. In this work, we propose an interpretable deep learning framework for AD diagnosis using the DARWIN handwriting dataset. Our approach integrates Recursive Feature Elimination (RFE) with a Feedforward Neural Network (FNN) to identify the most discriminative handwriting features, reduce dimensionality, and enhance generalization. Remarkably, the model achieves strong performance with only 20 selected features, demonstrating robustness in small-sample learning scenarios, as evidenced by a 10-fold cross-validation accuracy of 0.9833[Formula: see text] ± [Formula: see text]0.0356, ROC-AUC of 0.9987[Formula: see text] ± [Formula: see text]0.0039, F1-score of 0.9841[Formula: see text] ± [Formula: see text]0.0331, recall of 0.9818[Formula: see text] ± [Formula: see text]0.0545, and precision of 0.9889[Formula: see text] ± [Formula: see text]0.0333. Unlike data-intensive CNN, RNN, or Transformer models, the FNN attains perfect accuracy, sensitivity, and precision on DARWIN while remaining lightweight and scalable. To strengthen clinical trust, we incorporate SHAP-based explainability to highlight critical handwriting biomarkers and optimize assessment protocols by identifying less informative tasks. Comparative evaluations against established ML and DL baselines confirm consistent performance gains and enhanced interpretability, underscoring the potential of handwriting-based approaches for practical and transparent early AD detection.
Alzheimer's disease (AD) is a progressive neurodegenerative disease that requires early diagnosis to effectively treat and slow down the progression of the disease. To deal with all these issues, we come up with the Self-Aware Quantization Adaptive Federated Transfer Learning framework with FedProx-Optimized Inception-Transformer Networks (SAQ-AFTL-ITN) that is capable of finding accurate AD diagnosis, without hindering the privacy of patients' data. The SAQ-AFTL-ITN framework achieves phenomenal computational efficiency by slashing model size by 45% and communication overhead by 35%, breaking the bottleneck for decentralized healthcare environments. By using FedProx, the stabilization of the training on non-independent and identically distributed (non-IID) datasets is achieved, while TL helps to improve the generalization power of the model, particularly for clients having very limited data. Without noticing, this research shows that this framework, by combining Inception modules with Transformer encoders and using advanced aggregation methods, has already attained state-of-the-art results on the OASIS dataset, attaining an accuracy of 98.0%, precision of 97.6%, recall of 98.15%, and an F1-score of 98.0%. This work unveils the game-changing capabilities of FL integrated with SAQ, thereby forming new benchmarks for systems for medical AI that are privacy-preserving, scalable, and efficient.
Extractive Question Answering (EQA) in the biomedical domain presents significant challenges due to complex medical terminology and intricate relationships between entities. Existing systems often perform suboptimally compared to their counterparts in general domains. Current biomedical QA models struggle with accurately identifying named entities and extracting meaningful relationships within medical contexts, thereby limiting their practical utility. This study introduces a novel transformer-based approach for biomedical EQA that incorporates Named Entity Recognition (NER) to identify key medical terms, Relation Extraction (RE) to understand their interconnections, and a BiLSTM layer to enhance contextual comprehension. These components are integrated within an ensemble framework that leverages pretrained biomedical language models. Our methodology systematically evaluated six transformer encoder models and identified Bio+ClinicalBERT as the optimal foundation for developing three progressively advanced biomedical EQA models. The final model — Bio+ClinicalBERT_NER_RE_BiLSTM — incorporating ensemble learning techniques, demonstrated substantial performance improvements: an F1 score of 91. 69%, exact match of 88. 35%, and lenient accuracy of 0.84, marking a 5. 45% performance gain over baseline methods. These results highlight the model’s superior ability to navigate complex biomedical language and extract accurate information from medical texts. The developed system directly benefits biomedical researchers, clinical practitioners, medical students, and healthcare professionals by delivering accurate and rapid access to precise information within extensive medical literature. By bridging the gap between intricate medical texts and effective information retrieval, this work significantly advances automated biomedical knowledge extraction and supports evidence-based medical decision-making.
