Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging (MRI) and Electrocardiogram (ECG). Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network (TCGN) for ECG feature extraction, combining these through an attention mechanism to emphasize relevant features. Classification is performed using Optimized TCGN. This approach improves early detection, reduces diagnostic errors, and supports personalized risk assessments and continuous health monitoring. The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models, achieving accuracies of 92.56% for Heartnet Dataset I (HNET-DSI), 93.45% for Heartnet Dataset II (HNET-DSII), and 91.89% for Heartnet Dataset III (HNET-DSIII), mitigating the impact of apparatus faults and image quality issues. These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net and ResNet can be consider as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant usually named as U-Net Segmentation has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw of high memory and CPU consumption, while moving the whole feature map to the related decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids the reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problem with the efficient use of memory consumption. The proposed model has been evaluated on standard benchmark dataset of Eye Net having 32 classes of retinal diseases. From experimental evaluation it has been concluded that the proposed model performs better in terms of memory management and accuracy. The overall comparison has been performed on the bases of precision, recall, accuracy with different number of epochs and time consumption by each step. Proposed technique achieved an accuracy of 95% on Eye-net dataset.

Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture.

The rapid growth of Internet-enabled applications, such as social media platforms, e-commerce sites, and blogs, has led to a surge in user-generated content. This vast amount of data has made sentiment analysis increasingly valuable. Modern Aspect-Based Sentiment Analysis (ABSA) offers a more detailed approach by identifying sentiment trends related to specific aspects within the text. However, the challenge lies in analyzing reviews that are often short, unstructured, and filled with slang and emotive language, making it difficult to gauge customer opinions accurately. To address these issues, we proposed an effective hybrid approach “RoBERTa-1D-CNN-BiLSTM” for ABSA. Initially, the pre-trained Robustly Optimized BERT approach (RoBERTa) and One Dimensional Convolutional Neural Network (1D-CNN) models are used to extract features at the aspect level from the context of the review, following which classification is performed using Bidirectional Long Short-Term Memory (BiLSTM). The approach is evaluated on three cross-domain standards datasets, yielding an accuracy of 92.33%. The results of the experiments show that it surpasses the current leading methods in sentiment analysis and product recommendation.