With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

Journal of Information Systems Engineering and Business Intelligence

The text-based question-answering (QA) system aims to answer natural language questions by querying the external knowledge base. It can be applied to real-world systems like medical documents, research papers, and crime-related documents. Using this system, users don't have to go through the documents manually the system will understand the knowledge base and find the answer based on the text and question given to the system. Earlier state-of-the-art natural language processing (NLP) was recurrent neural network (RNN) and long short-term memory (LSTM). As a result, these models are hard to parallelize and poor at retaining contextual relationships across long text inputs. Today, bidirectional encoder representations from transformers (BERT) are the contemporary algorithm for NLP. BERT is not capable of handling long text sequences; it can handle 512 tokens at a time which makes it difficult for long context. Smooth inverse frequency (SIF) and the BERT model will be incorporated together to solve this challenge. BERT trained on the Stanford question answering dataset (SQuAD) and SIF model demonstrates robustness and effectiveness on long text sequences from different domains. Experimental results suggest that the proposed approach is a promising solution for QA on long text sequences.