In the realm of pulmonary medicine, prognostic assessment of Idiopathic Pulmonary Fibrosis (IPF) poses a significant challenge, necessitating advancements in predictive analytics. This study introduces a pioneering predictive model that synergistically utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze CT scans and clinical data, offering a dynamic prediction of IPF progression. The model’s methodology is rooted in the integration of detailed spatial features and temporal clinical patterns to forecast disease trajectory with heightened accuracy. A novel aspect of this model is its built-in mechanism for uncertainty quantification, enhancing the interpretability of its prognostic output. Through validation, the proposed model demonstrated a mean Out-of-Fold (OOF) validation score 6.9679 and accuracy of 95% notably superior to traditional methods, alongside a conservative yet precise approach to uncertainty estimation. The findings represent a substantial improvement in predictive capabilities, emphasizing the model’s potential to inform clinical decision-making and facilitate personalized treatment strategies. The implications of this research extend beyond IPF, providing a framework for future explorations into machine learning applications in complex disease prognostics.

The use of multimodal medical imaging is on the rise, both in academic and clinical settings. There was a meteoric growth in the use of multimodal imaging analysis (MIA) with the addition of ensemble learning techniques, which had particular advantages in the medical field. We provide an algorithmic framework that allows supervised MIA and Cross-Modality Fusing at the preprocessing phase algorithms for classification and decision-making levels, drawing inspiration from the current triumphs of deep learning approaches in medical imaging. We presented a method for picture segmentation that makes use of sophisticated convolutional neural networks to identify lesions produced by tumors in soft tissues. To do this, MRI tomography and PET scans are combined to provide multi-modal images. Networks trained with multimodal images outperform their single-modal counterparts. When it relates to tumor segmentation, fusing photos throughout the neural network (i.e., within the convolutional layer or totally connected layers) yields better results than photographs that merge the network’s output. The proposed approach employs four pre-trained models, specifically VGG 19, ResNet 50,SqueezeNet, as well as DenseNet 121. Using a dataset of ISL images, the pre-trained models are fine-tuned. Subsequently, the ensemble learning technique is employed to combine the predictions generated by the three models. Here, ensemble is based on a weighted voting method. Impressive results were obtained with the proposed ensemble method: 98.1% accuracy, 97.5% F1 score, and 90.8% Kappa score. The ensemble method outperformed individual models and existing approaches for multimodal medical fusion and classification, with a Jaccard score of 93.8% and a recall of 98.2% demonstrate its effectiveness for multimodal medical fusion and classification.

The identification of events from social media platforms such as Twitter (now known as X) is a hot research problem. It has applications in diverse domains such as journalism, marketing, public safety, crisis management and disaster response. The process includes the identification, monitoring, and analysis of events or incidents while they are being discussed or reported on Twitter. When it comes to identifying events from tweets (i.e. feeds from Twitter), many of the currently available event detection methods mainly rely on keyword burstiness features or structural changes in the network. However, due to the intricate characteristics of tweets and the ever-changing nature of events, they frequently fail to recognise noteworthy occurrences before they become trending. Moreover, these methods face difficulties when it comes to capturing the evolving characteristics of events with limited or insufficient contextual information. In this paper, we propose a window-based tweet-processing method called EDTBERT for detecting events and tracking the evolution of events over time. Our proposed method utilizes the structural and semantic affinities that exist among words in tweets. The method starts by generating graph of tweets, where tweets are represented as nodes, and edges are the similarities between tweets. The method utilizes overlapping hashtags and named entities to capture the structural relationship between tweets. Additionally, a pre-trained sentence transformer model, specifically BERT, is employed to collect contextual knowledge and find semantically similar tweets. Next, the graph clustering technique is employed to identify optimized event clusters. Subsequently, our method generates chain of event clusters for each event to track the evolving variation of the event over time by utilising the ”Maximum-Weight Bipartite Graph Matching” (MWBGM) algorithm. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches.

Social media platforms are increasingly becoming a prominent role in the dissemination of information about real-world happenings. So, early identification of newsworthy events from tweets (i.e. feeds from Twitter) is a hot research problem. However, most existing event detection techniques rely primarily on the burstiness of keywords or the changes in structural networks in order to identify events. Often, these techniques fail to see newsworthy events before they reach a trending state due to the tweet’s challenging characteristics and the evolutionary nature of events. Moreover, these methods lack in capturing evolving characteristics of events based on limited contextual information. To address these issues, we propose EventBoost, a window-based tweet processing method for detecting events and associated aspects by exploiting the lexical and semantic affinity among the words in tweets. The event identification method is enhanced by utilising the temporal dimension and social features of tweets, viz. Hashtags and named entities. We use contextual knowledge, in particular, to find semantically similar tweets to form clusters and improve the quality of clusters. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches.

Mining Twitter streaming posts (i.e., tweets) to find events or the topics of interest has become a hot research problem. In the last decade, researchers have come up with various techniques like bag-of-words techniques, statistical methods, graph-based techniques, topic modelling approaches, NLP and ontology-based approaches, machine learning and deep learning methods for detecting events from tweets. Among these techniques, the graph-based technique is efficient in capturing the latent structural semantics in the tweet content by modelling word cooccurrence relationships as a graph and able to capture the activity dynamics by modelling the user- tweet and user-user interactions. Discussion: This article presents an overview of different event detection techniques and their methodologies. Specifically, this paper focuses on graph-based event detection techniques in Twitter and presents a critical survey on these techniques, their evaluation methodologies and datasets used. Further, some challenges in the area of event detection in Twitter, along with future directions of research, are presented. Conclusion: Microblogging services and online social networking sites like Twitter provide a massive amount of valuable information on real-world happenings. There is a need for mining this information, which will help in understanding the social interest and effective decision making on various emergencies. However, event detection techniques need to be efficient in terms of time and memory and accurate for processing such voluminous, noisy and fast-arriving information from Twitter.

Finding events from streaming tweets is challenging for needing efficient algorithm enable to scoop events from a tweet with restricted text size and to process fast as at given time large number of tweets are emanate on cyberspace. This paper proposes a syntax based approach and presents a preliminary experimental result.