Brain tumors are an extremely deadly condition and the growth of abnormal cells that have formed inside the brain causes the illness. According to studies, Magnetic Resonance Imaging (MRI) is a fundamental imaging method that is frequently used in medical diagnostics to identify, treat, and routinely check for brain cancers. These images include extremely private and delicate details regarding the brain health of the individuals and it must be treated with much care to ensure anonymity of patients. However, traditional brain tumor segmentation techniques usually rely on centralized data storage and analysis, which might result in privacy issues and violations. Federated learning offers a solution by enabling the cooperative development of brain tumor segmentation models without necessitating the transfer of raw patient data to a centralized location. All the data are held securely within their institution. A Reinforcement Learning-based Federated Averaging (RL-FedAvg) model is proposed that fuses the Federated Averaging (FedAvg) model with Reinforcement Learning (RL). To optimize the global model for image segmentation jobs as well as to govern the consumption of client resources, the model dynamically updates client hyperparameters upon real-time performance feedback. A Double Attention-based Multiscale Dense-U-Net model, known as mixed-fed-UNet, is proposed in the work that uses the RL-FedAvg algorithm. The proposed technique achieves 98.24% accuracy and 93.28% dice coefficient on BraTs 2020 dataset. While comparing the developed model with the other existing methods, the proposed methodology shows better performance.

While chatbots are increasingly popular for communication, their effectiveness is limited by their difficulty in understanding users’ emotions. To address this, this study proposes a new hybrid chatbot model called “TEBC-Net” (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. At the core of TEBC-Net is a multi-modal emotion analysis system. One component uses Bidirectional Encoder Representations from Transformers (BERT), a well-regarded model in natural language processing (NLP), achieving an 87.21% accuracy rate in detecting emotional cues from text inputs. The second component captures users’ facial expressions through webcam footage. It begins by detecting faces using a pre-trained classifier like Haarcascade. Then, to improve emotion recognition, it preprocesses the image through brightness adjustments and contrast enhancement with Automatic CLAHE and dual gamma correction. This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. By integrating insights from both text and video analysis, TEBC-Net gains a comprehensive understanding of the user’s emotional state and intent. This combined data then informs the chatbot’s response generation module, enabling it to craft responses that are both empathetic and more directly aligned with the user’s emotional needs.

Captioning images is a challenging task at the intersection of Computer Vision (CV) and Natural Language Processing (NLP), that involves generating descriptive text to depict the content of an image. Existing methodologies typically employ Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for generating captions. However, these approaches often suffer from a lack of contextual understanding, inability to capture fine-grained details, and to generate generic captions. This study proposes VisualCaptionNet (VCN), a novel image captioning model that leverages ResNet50 for rich visual feature extraction and a Long Short-Term Memory (LSTM) network for sequential caption generation while retaining context. By incorporating the Bahdanau attention mechanism to focus on relevant image regions and integrating beam search for coherent and contextually relevant descriptions, VCN addresses the limitations of previous methodologies. Extensive experimentation on benchmark datasets such as Flickr30K and Flickr8K demonstrates VCN’s notable improvements of 10% and 12% over baseline models in terms of caption quality, coherence, and relevance. These enhancements emphasize VCN’s effectiveness in advancing image captioning tasks, promising more accurate and contextually relevant descriptions for images.

Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.