Today, the diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers in this field owing to the increase in the occurrence of the diseases and the need for early diagnosis. Unfortunately, the nature of high dimension of neural data and few available samples led to the creation of a precise computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field. Inspired by the concept of unsupervised feature learning that uses artificial intelligence to learn features from raw data, a two-stage method was presented for an intelligent diagnosis of Alzheimer’s disease. At the first stage of learning, scattered filtering, an uncontrolled two-layer neural network was used to directly learn features from raw data. At the second stage, SoftMax regression was used to categorize health statuses based on the learned features. The proposed method was validated by the data sets of Alzheimer’s Brain Images. The results showed that the proposed method achieved very good diagnostic accuracy and was better than the existing methods for brain image data sets. The proposed method reduces the need for human work and makes it easy to intelligently diagnose for big data processing, because the learning features are adaptive. In our experiments with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, a dual and multi-class classification was conducted for AD/MCI diagnosis and the superiority of the proposed method in comparison with the advanced methods was shown.

Financial prediction has gained significant attention due to the complex and non-linear dynamics of the market. A promising approach for generating accurate predictions is Transformers. Encoder-decoder structures efficiently capture complex temporal dependencies and patterns within large-scale data. However, relying on a single attention mechanism may limit the model’s ability to capture more intricate relationships. This paper proposes a dual attention architecture to improve the encoder-decoder framework for financial forecasting. First, the Price Attention Network (PAN) extracts complex features from price data and forecasts future prices using historical price inputs. Two key improvements are introduced to enhance self-attention: a Masked Self-Attention module focusing on the most relevant information and Multi-head Attention facilitating more profound insights into the data. Second, the Nonprice Attention Network (NAN) is proposed as a parallel network that processes related financial features. This network utilizes ConvLSTM, BiGRU, and Self-Attention to dynamically weigh and extract meaningful information from nonprice data. Finally, the PAN and NAN networks are integrated, enhancing prediction accuracy. The proposed approach outperforms five state-of-the-art models. Moreover, qualitative assessments of over 26 financial datasets, spanning large and small datasets with short and long histories, further validate the proposed model's ability. Evaluations using seven metrics show the model’s superiority, achieving a Mean Absolute Error (MAE) of 0.01991, Mean Squared Error (MSE) of 0.00084, Mean Pinball Loss (MPL) of 0.00996, Symmetric Mean Absolute Percentage Error (SMAPE) of 3.03324, and Mean Absolute Scaled Error (MASE) of 1.85436. This framework represents a significant advancement in financial prediction, offering accurate and interpretable forecasts across various time series tasks.

Developing an automatic system for detection, segmentation, and classification of skin lesions is very useful to aid well-timed diagnosis of skin diseases. Lesion segmentation is a crucial task for automated diagnosis of skin cancers, as it affects significantly the accuracy of the subsequent steps. Varieties in sizes and locations of lesions, and the lesions with low-contrast boundaries make this task very challenging. In this paper, a three-stage CNN-based method is presented for accurate segmentation of lesions from dermoscopic images. At the first step, normalization, approximate locations and sizes of lesions are estimated. Due to the importance of the normalization stage, three CNN-based networks (Mask R-CNN, RetinaNet, and YOLOv3) are used for the lesion detection. A convolutional network is presented and used to combine the results of the object detection networks with a novel approach. The output of the first stage is a normalized cropped image containing the detected lesion in the center. At the second stage, segmentation, a CNN in a DeepLab3+ structure, is used to extract the lesion from the normalized image. Finally, an active contour method is used as the postprocessing to enhance the boundary of the segmented lesion. The proposed method is evaluated on well-known datasets. Experiments show that the proposed method outperforms all the previous state-of-the-art methods.