The most important component that can express a person’s mental condition is facial expressions. A human can communicate around 55% of information non-verbally and the remaining 45% audibly. Automatic facial expression recognition (FER) has now become a challenging task in the surveying of computers. Applications of FER include understanding the behavior of humans and monitoring moods and psychological states. It even penetrates other domains—namely, robotics, criminology, smart healthcare systems, entertainment, security systems, holographic images, stress detection, and education. This study introduces a novel Robust Facial Expression Recognition using an Evolutionary Algorithm with Deep Learning (RFER-EADL) model. RFER-EADL aims to determine various kinds of emotions using computer vision and DL models. Primarily, RFER-EADL performs histogram equalization to normalize the intensity and contrast levels of the images of identical persons and expressions. Next, the deep convolutional neural network-based densely connected network (DenseNet-169) model is exploited with the chimp optimization algorithm (COA) as a hyperparameter-tuning approach. Finally, teaching and learning-based optimization (TLBO) with a long short-term memory (LSTM) model is employed for expression recognition and classification. The designs of COA and TLBO algorithms aided in the optimal parameter selection of the DenseNet and LSTM models, respectively. A brief simulation analysis of the benchmark dataset portrays the greater performance of the RFER-EADL model compared to other approaches.

Breast cancer, a common malignancy impacting women globally, involves the uncontrolled growth of breast cancer cells. Timely identification and accurate classification of breast cancer into non-cancerous (benign) and cancerous (malignant) categories are crucial for effective treatment planning and enhanced patient outcomes. Conventional diagnostic techniques depend on histopathological examination of breast tissue samples, a process that can be subjective and time-consuming. The problem statement revolves around developing a computational model to automatically classify images from histopathology into non-cancerous or cancerous categories, addressing the limitations of manual diagnosis. Existing methodologies leverage various machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNNs) being prominently utilized due to their effectiveness in image recognition tasks. However, these methods often require substantial computational resources and can suffer from overfitting due to the complex architecture. The objective of this study is to introduce an External Attention Transformer (EAT) model that utilizes external attention mechanisms, providing an approach to breast cancer image classification. This model aims to achieve high accuracy while maintaining computational efficiency. The primary metrics to assess the model’s performance include precision, recall, F1-score, and overall accuracy. The EAT model demonstrated outstanding performance achieving an accuracy of 99% on the BreaKHis dataset, indicating its potential as a reliable tool for breast cancer classification.

Financial market prediction has shown considerable potential in the past few years from the combination of contemporary Deep Learning (DL) techniques and traditional time series forecasting methodologies. To predict the stock prices of three distinct companies General Electric (GE), Microsoft (MSFT), and Amazon (AMZN) datasets. This study presents a novel hybrid model that combines the Double Exponential Smoothing (DES) method with a Deep Learning (DL) model Dual Attention Encoder-Decoder based Bi-directional GRU, optimized using Bayesian Optimization (DA-ED-Bi-GRU-BO). By combining the best features of contemporary and old methods, the hybrid model seeks to efficiently identify patterns and trends in stock data. When handling time series data, the DES method offers a reliable and flexible mechanism that considers trends and seasonality in the data. The DA-ED-Bi-GRU added to the deep learning model further improves its comprehension of intricate patterns found in the stock data. The parameters are adjusted using Bayesian optimization (BO) to maximize the model’s performance. Several performance indicators, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Square ( R ² ), and Theil’s U-Statistics (TUS), are used to assess the effectiveness of the model. These measures offer thorough insights into the precision, dependability, and accuracy of the model’s predictions. The experimental findings show that the proposed hybrid model has the ability to predict GE, MSFT, and AMZN stock values with reasonable accuracy. Along with the optimization framework, DL and conventional smoothing approaches combine to provide a potent forecasting tool that may help traders and investors make wise judgments.

Stock price forecasting is a critical component of financial market analysis and plays a key role in formulating investment strategies, affecting a wide range of stakeholders. To address the inherent complexities and dynamic fluctuations in stock prices, innovative modeling techniques are required. Deep Learning (DL) approaches, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, have shown notable promise in enhancing prediction accuracy for stock prices. This study introduces a novel hybrid Single Exponential Smoothing with Dual Attention based Bi-directional LSTM optimize with Bayesian Optimization (SES-DA-BiLSTM-BO) designed to predict future stock prices accurately using Life Insurance Corporation of India (LIC) stock price dataset with 1-minute, 3-minute, and 10-minute intervals. The model aims to forecast stock prices, identify market trends, and reveal influential factors more effectively. The performance of the proposed SES-DA-BiLSTM-BO model was evaluated using key metrics: Directional Accuracy (DA=0.4816), Mean Absolute Percentage Error (MAPE=0.0006), Win/Loss Ratio (WLR=0.7517), and Theil’s U-Statistic (TUS=0.0006). These results demonstrate its superior performance compared to traditional models such as Moving Average (MA), Auto-Regressive (AR), Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA), Single Exponential Smoothing (SES) and LSTM models. By outperforming baseline model on varies error metrics, the proposed SES-DA-BiLSTM-BO hybrid model exhibits improved predictive capabilities and lower loss functions, making its highly reliable for forecasting stock prices under diverse market conditions. The integration of statistical methods with deep learning in this hybrid approach offers a scalable and efficient solution for optimizing investment strategies, enhancing market efficiency, and contributing to economic stability by reducing the impact of market volatility.

Dental caries is among the most prevalent oral diseases worldwide, and accurate radiographic detection remains a clinical challenge, particularly for lesions defined by the G.V. Black classification. This study aimed to develop and evaluate CBMNet, a dual-attention enhanced ConvNeXt-Tiny model, for automated classification of G.V. Black Classes I-III using intraoral periapical radiographs. A total of 1103 anonymized periapical radiographs were retrospectively collected from the Sibar Institute of Dental Sciences, India, covering G.V. Black Class I ( n = 408), Class II ( n = 490), and Class III ( n = 205). To address class imbalance, minority classes were supplemented with high-fidelity synthetic images generated via StyleGAN2-ADA, validated using BRISQUE scores and blinded expert review. Images were pre-processed with CLAHE and median filtering, and CBMNet was implemented by integrating Convolutional Block Attention Module (CBAM) and Multi-Scale Attention Module (MSAM) into a ConvNeXt-Tiny backbone. Hyperparameters were optimized using Particle Swarm Optimization (PSO). Performance was evaluated through stratified 5-fold cross-validation, ablation studies, and a held-out real-image test set, with additional robustness testing via test-time augmentation(TTA). CBMNet achieved a mean validation accuracy of 93.26% ( 0.81)across folds and a final held-out test accuracy of 92% with TTA. Class-wise evaluation showed high precision (Class I:0.90, Class II:0.87, Class III:0.99), recall (Class I:0.94, Class II:0.90, Class III:0.91), and F1-scores (Class I:0.92, Class II:0.89, Class III:0.95). Ablation analysis confirmed the complementary contributions of CBAM, MSAM, and TTA. Compared with baseline models (ResNet50, EfficientnetB0, DenseNet121), CBMNet consistently outperformed in overall and class-specific metrics. The proposed CBMNet framework demonstrated robust diagnostic performance for automated classification of G.V. Black Classes I-III from periapical radiographs, with accuracy and class-wise metrics exceeding 90%. By integrating dual-attention mechanisms, GAN-based augmentation, and PSO-driven optimization, CBMNet provides a reliable, interpretable, and clinically relevant tool that may support early detection and standardized diagnosis of dental caries. Future studies with multi-centre datasets and prospective clinician comparisons are warranted to further validate clinical applicability.