Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. The adaptive feature fusion layer, a pivotal advancement, facilitates the dynamic integration of features extracted from a ResNet50-based CNN and a vision transformer (ViT). Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. Results: Evaluated on the Alzheimer’s 5-Class (AD5C) dataset comprising 2380 MRI scans, the framework achieves an accuracy of 99.42% (precision: 99.55%; recall: 99.46%; F1-score: 99.50%), surpassing the prior benchmark of 98.24% by 1.18%. Ablation studies underscore the essential role of adaptive feature fusion in minimizing misclassifications, while external validation on a four-class dataset confirms robust generalizability. Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions.

We propose PIDS, Pattern Inference Decomposed Storage, an innovative storage method for decomposing string attributes in columnar stores. Using an unsupervised approach, PIDS identifies common patterns in string attributes from relational databases, and uses the discovered pattern to split each attribute into sub-attributes. First, by storing and encoding each sub-attribute individually, PIDS can achieve a compression ratio comparable to Snappy and Gzip. Second, by decomposing the attribute, PIDS can push down many query operators to sub-attributes, thereby minimizing I/O and potentially expensive comparison operations, resulting in the faster execution of query operators.

Geometric dynamic configurations of body joints play an essential role in distinguishing different human activities. However, many existing human activity recognition approaches lack the capability of automatically learning these configurations from sequences of joints in four-dimensional space (spatio and temporal). In this paper, the authors propose an automatic joint configuration learning method, based on dictionary learning and sparse representation. The proposed method achieves the following features: 1) it automatically learns dynamic spatio-temporal geometric configurations of body joints, involved in activities, in a simple way; 2) it dispenses with the hand crafted feature designing process and provides a new method to organize joint coordinate data as fixed length column vectors, which are suitable for dictionary learning; 3) it replaces the conventional bag of words model with sparse coding method; words in learned dictionary capture subactivity features, and the frequencies of different words appearing in different activities characterize the categories of global activity; 4) it is robust to time misalignment and can classify any length of video sequence (online classification) in real time; 5) it is easy to combine this method with other forms of data for better performance, because of its data driven nature and flexible framework. The proposed method is tested with three state-of-the-art public human activity recognition datasets and the results are found to be better than those of CAD-60 dataset, and comparable to those of both MSR Action 3D and MSR Daily Activity datasets (source codes are publicly available at https://github.com/jinqijinqi/SparseCodingDictionaryLearningHumanActivityRecognition).

Currently, ground truth fusion image, fused image contrast, and naturalness are rarely considered in existing infrared (IR) and visible (VIS) image fusion (IVF) methods. In this paper, we proposed a P seudo S upervised G enerative A dversarial N etwork with Single Scale Retinex Embedding (termed “PS-GAN”) for IVF. Firstly, a pseudo ground truth fusion image (termed “PGI”) conception and its computation method was proposed to solve ground truth fusion image shortage problem. Secondly, a novel single scale retinex (SSR) module embedded residual GAN was designed to improve fusion image contrast and naturalness. Finally, a special dense and mixed modal inputting strategy was also proposed for better modal mixed feature extraction. Extensive experimental results on public IVF datasets verified the superior performance of our proposed approach over other representative methods. It was demonstrated that the fused image details, contrast, and naturalness were significantly improved. Our source codes are accessible at https://github.com/jinqijinqi/PSCM-GAN .