In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34% test accuracy by combining magnification levels using a Haar wavelet filter. Results obtained from comparative analyses against general CNN models confirm that the new strategy performs better, thereby validating the efficacy of integrating wavelet-based textural extraction and state-of-the-art CNN models for early-stage breast cancer diagnosis.

Driven by computational linguistics, intercultural communication, and situated cognition, this paper has proposed a research prototype of multimodal, culture-sensitive evaluation of cross-cultural pragmatic competence in English as a global language. The Multimodal Cross-cultural Pragmatic Analysis System (MCPAS) proposed combines verbal, paraverbal, and nonverbal cues through language modeling based on transformers, acoustic-prosodic analysis, and computer-vision characteristics, and applies a culture-calibration layer to condition pragmatic inference on empirically observed cultural orientations and maintain within-group variation. MCPAS is trained and tested on a culturally varied corpus of 645 participants of 27 cultural settings (872 h; 1.9 million utterances). With culture-calibrated inference, in 24-class speech-act classification, the system achieves 94.3% micro-averaged accuracy on a discussed test set, using class-balanced performance (speech-act F1 = 0.913; overall feature-recognition F1 = 0.874). The consistency with the independent expert judgments is high (weighted Cohen’s k = 0.861), and test-retest reliabilities provide high levels of reliability (ICC in general = 0.913). Mixed-methods assessment also investigates the usability of pedagogical methods using expert evaluation, teacher practice, and a small-scale student intervention, indicating that system outputs can be converted into classroom- and professional-based instructional feedback and activities. The results are presented of the corpus and settings under study, external benchmark testing, wider cultural transfer testing, and deployment testing in uncontrolled settings, which is also an important area of future research.

Medical image fusion (MIF) significantly improves diagnosis accuracy by combining complementary data from several imaging modalities. However, the conventional fusion methods often fail to maintain the edge details, uniformity of texture and most relevant characteristics. To overcome these challenges, a novel multi-modality medical image fusion using Triple generator network (MIMO-TGAN) is proposed for accurate brain abnormality detection. The proposed MIMO-TGAN uses the multi-modality images such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) images for MIF process. Initially, the gathered MRI and CT images are denoised with scalable range-based adaptive trilateral (SCRAT) filter and PET images are processed by multi-scale retinex (MSR) technique. The proposed MIMO-TGAN model contains three generators with a discriminator and an abnormality detection phase. The Generator-1 consists of grouped convolutional with different attention blocks for retrieving the relevant features from MRI and CT images to generate a synthesized image. Simultaneously, Generator-2 retrieves the most important voxel features from PET images and MRI images to generate another synthesized image. Finally, Generator-3 is designed to combine the both synthesized images from two generators by using four fusion rules to accurately detect brain abnormalities. The experimental evaluations demonstrate that the proposed MIMO-TGAN achieves an accuracy of 99.43% and 98.24% for dataset-1and dataset-2 respectively. Moreover, the proposed MIMO-TGAN achieves the entropy values of 7.05 and 6.84 for dataset-1 and dataset-2, and overall SSIM of 0.94 that indicates superior fusion quality results compared to existing methods.

Breast cancer is a prevalent disease affecting women globally, and early detection and treatment can improve survival rates. Breast ultrasound is a common method for preoperative diagnosis, but its low resolution can lead to inaccurate judgments. Artificial intelligence (AI) can enhance diagnostic accuracy and aid in formulating effective treatment strategies. This research aimed to integrate medical ultrasound image analysis and AI to develop a multimodal nomogram for preoperative prediction of breast cancer. The study utilized a publicly available dataset that included B-mode ultrasound and color Doppler flow imaging (CDFI) scans from 611 patients, supplemented by relevant clinical variables. The study develops a multimodal deep learning radiomic nomogram model (DeepRadix) based on the ResNet50 backbone and channel attention mechanisms to classify breast malignancy. Radiomics features were extracted from lesion ROIs using PyRadiomics and subsequently reduced via minimum Redundancy–Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO)-based feature selection to identify the most informative radiomic predictors; in parallel, deep representations were learned using a ResNet50-based network and summarized as a deep learning score. The proposed multimodal model integrates predictive radiomic features, deep learning, and clinical information to overcome the limitations of existing methods and fully leverage their strengths. The model also introduced a nomogram-based visualization to enhance interpretability and improve clinical understanding of each patient’s characteristics. The research demonstrated strong discriminative ability in preoperative prediction of benign and malignant breast cancer, revealing potential associations among clinical features, ultrasound imaging features, and disease pathology. These findings could aid precision medicine and inform the design of diagnostic and therapeutic strategies for patients with breast cancer.

Twitter, a microblogging platform with millions of active users, is an indispensable source of human expressions and generates a massive amount of user-generated contents. In order to process this information, the Sentimental Analysis (StA) technique is employed, and it gives the polarity of the opinions effectively. However, due to brevity, inherent noise and the use of sarcasm impose crucial challenges.In this research, a novel Text Con. Robust Grey optimized Linguistic – XLNet is proposed for Opinion Mining (OpM) from Twitter Data. During feature extraction, the conventional techniques face misinterpretation of sentiments and perspectival variation due to inflexible features and textual nuances. Therefore, a TextCon.Neural Network-based Hybrid Term Frequency-Term Discrimination Ability (TCon-HTDA) is employed, and it aids in polarity estimation and sarcasm detection. Besides, if more than one strategy is employed for feature ranking, it leads to spurious associations with poor selection of endpoints and results in a narrowed scope with inflated false positives. Hence, a robustly optimized BERT-Grey Stretch Reasoning Linguistic Approach (RoB-GSRLA) is employed for feature ranking, which reduces the issues with overestimation and also widens the scope. Moreover, as optimal values are prone to step size regulation and global minima, unbalanced optimal values occur during classification. Therefore, an Extended Net with SMOTE-T technique with AsCapsule (XNet-STAC) is employed for classification of optimal opinions, namely Positive, Negative or Neutral, which aids in decreasing the complexities with unbalanced optimal values.The outcomes attained give out better accuracy of about 97.6% and sensitivity of about 97.8% compared to conventional techniques.The proposed text optimized StA, ensures that the defined work is robust to deal with the noises and the informal languages of Twitter Data, while making the whole topology a significant tool for modest OpM.

Data-driven diagnosis and treatment in the medical field are becoming more and more common, yet they are hindered by the shortcomings of the datasets available: they are limited, unbalanced, and require privacy protection. Nonetheless, such extreme scarcity, disproportionality, and privacy issues of medical datasets are major obstacles towards realizing robust and generalizable Artificial Intelligence (AI) models. Privacy-preserving generative frameworks are deemed critical to provide significantly advanced performance that will be able to boost medical data quality and variety without the need to affect the structural and clinical integrity. This research experiment aims to provide a new Generative AI-solution Medical Data Enhancement (GAIMD-E) framework with a dual-stream structure encompassing a Contour-Aware Generative Adversarial Network (CA-GAN) combined with a Variational Transformer Auto-Encoder (VTAE). The CA-GAN contains a contour computation component that explicitly learns the anatomical boundaries and the structural outlines of medical images, and has the capacity of generating high-quality synthesized examples with morphological consistency preserved. At the same time, the VTAE simultaneously represents local feature uncertainty as well as global temporal dependencies on multi-modal medical records (for example, radiology, pathology, and so on). Besides, a new Gradient-Guided Feature Consistency Loss (GGFCL) is proposed to achieve structural consistency in synthetic and real samples that can improve domain credibility. Also, experimental tests on three benchmark datasets Brain Tumor Segmentation (BraTS), Medical Information Mart for Intensive Care III (MIMIC-III), and National Institute of Health (NIH) Chest X-ray reveal up to 31.6% gain in model generalization, 27.4% decrease in false negatives and 21.2% increase in F1-score when training downstream classifiers on augmented data. These findings confirm the effectiveness of the framework to remedy data gaps when maintaining clinical applicability, and it is a viable and privacy-safe option towards advancements in AI-based medicine.

With the booming development of the tourism industry, scenic spots often face the risk of tourist overload during peak hours. In response to the weak dynamic adaptability of traditional safety management methods and the difficulty in accurately predicting passenger flow in high-density scenarios, a safety capacity management method based on Elman Recurrent Neural Network Genetic Algorithm (Elman-GA) is proposed. This method uses Elman neural network for passenger flow prediction, and decomposes the raw data for denoising through Complete Ensemble Empirical Mode Decomposition (CEEMD). Using an improved GA as the optimization engine and designing a simulated annealing mechanism, the network is deeply coupled with the algorithm to achieve intelligent and secure capacity management decisions. The experimental findings reveal that the prediction accuracy of the research method is tested, and the lowest Root Mean Square Error (RMSE) reaches 12.3% when the window length is 6 h. When testing the safety control effect of the research approach, the highest F1 score is 0.91 when the warning threshold reaches 85%. The robustness of the research approach is assessed, and when the noise amplitude increases to 30%, the RMSE reaches 35.4%. The prediction results of the research method are more accurate and stable, which can offer technical foundation to the establishment of smart tourism scenic spots.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by structural brain changes, often analysed using longitudinal 3D structural Magnetic resonance imaging (sMRI) data. Existing deep learning-based disease progression models face limitations in capturing long-term structural changes by handling class imbalance, and generating realistic future sMRI scans. However, very few works consider predicting future brain MRI images within four years. To address these challenges, a novel Adaptive Regression and ROI-enhanced Generative Adversarial Network (ARE-GAN) is proposed for AD progression prediction with the generation of real future MRI images. This framework integrates an improved generator conditioned on baseline (BL) MRI, an ROI-based attention mechanism to focus on critical brain regions, and an adaptive regression model for precise disease stage estimation. A dedicated feature fusion module is introduced to integrate extracted features from 3D sMRI data and clinical BL information. A discriminator refines the generated sMRI images by distinguishing them from real future MRIs by ensuring realistic spatial fidelity. Additionally, a Self-attention integrated DenseNet (SA-DenseNet) is used as the classification model to categorise the generated MRIs into AD, stable-MCI (s-MCI), progressive-MCI (p-MCI), and normal cognition (NC). From the experimental analysis, the proposed ARE-GAN model attains accuracy of 99.25% based on the ADNI dataset. The proposed ARE-GAN model framework increases the overall accuracy by 12.78%, 8.76%, 7.88% and 11.51% better than 3D CNN, ResNet50V2, 3D ResNet with a CBAM and EfficientNet, respectively.