Day 2

The talk will elaborate on building an end-to-end AI deployable pipeline for various industries (Health, Smart Spaces, Autonomous Driving, Industry 4.0, Conversational AI, Retail, Telecommunication, Energy) that can be brought to live-production in optimal cost and efficient timeframe for businesses.

Advancements in technology, data and analytics are enabling predictive modeling and forecasting to help improve health outcomes. Similar to how weather forecasting proves to be very useful to prepare for uncongenial weather conditions, disease forecasting can be used to mobilize, allocate and manage resources as well as inform the general population of the impending onset of a seasonal disease, such as the flu, in a timely manner. In the coming years, disease forecasting is positioned to become more accurate and meaningful, with richer and better sources of data from wearable devices, improved ambient computing, more readily available gene data etc. Forecasting methodologies and data analytics models are also advancing, with ongoing innovations around recurrent neural networks, compared to traditional statistical techniques. All of this is powered by the awareness of populations for more accurate information on seasonal and chronic diseases.

Undeniably, customer experience has emerged to be one of the most critical business imperatives, especially in the post-pandemic period. As organizations rethink and reimagine their CX strategies, Customer Care Operations functions are struggling to embed a data-backed decisioning system in their processes, which not only requires harmonizing humongous amount of customer interaction data across channels and touchpoints, but also measuring the right metrics and leveraging the right analytics frameworks to unlock quick, accurate and actionable insights, every single day. In this talk, we will share an approach on developing a data led, analytics accelerator for Customer Care domain that stitches together reusable data engineering, analytics, and ML components to optimize Data-to-Insights-Action journey in customer contact centers.

A brain tumor can be deadly and may cause many severe issues and even death if not diagnosed and treated at the early stages. So, early detection of brain tumors is of prime importance. Meningioma, Glioma, and Pituitary tumors are the most common, and 73% (1) of brain tumors are diagnosed as one of these types. Brain MRI image is one of the crucial methods to diagnose a brain tumor. Manual reading of MRIs could be time-consuming, and interpretation may vary based on the reader’s expertise. So an AI-based automated computer-aided diagnosis (CAD) can help identify and classify various brain tumors. The extensive potentiality of AI has successfully solved innumerable complex problems in medicine. However, the lack of transparency persists due to the increased complexity of advanced Deep Learning models. The black-box nature of the models complicates the decisions of AI applications in clinical use. Thus to perceive the underlying high-stake decision-making processes and to make the models interpretable, explainable AI (XAI) can be used. This study has proposed a classification model based on EfficientNet- B5 with attention-based weighted Global Average Pooling (GAP) to classify three brain tumors. It also demonstrated the use of Explainable AI to visualize the affected region or area of interest identified by our black-box model. Transfer Learning has been used, and specific layers of pre-trained EfficientNet- B5 have been fine-tuned with an attention-based GAP layer. The proposed model achieved 93.73% validation accuracy in multi-class classification, which was 2% higher than the EfficientNet B5+GAP without the Attention layer. We have used Grad CAM to implement the visual interpretability of our classification model. Our model achieved a macro-F1 score of .94 on the validation dataset, indicating very high macro-Recall and macro-Precision. Still, if there’s any case of false positive or false negative, that can be identified by looking into the visual interpretation of the model predictions. The proposed method outperformed many similar studies and effectively explained the region of interest identified by our black-box model using Explainable AI.

Query Autocompletion (QAC) is a common feature for input text-based applications where a user’s partially typed prefix input is completed. It has primarily been studied for applications involving search-based queries that are short sequences or phrases. We present a novel approach to QAC for a Question Answering system in an Augmented Analytics platform where queries are essentially business and analytical questions in natural language. Our approach involves a combination of semantic search and natural language generation via beam search for completing questions. To enable generative completion in natural language and handle unseen prefixes, we use a pretrained distilgpt2 model that is fine-tuned for question completion tasks. In addition, we describe a method to synthesize training data from limited available past queries for fine-tuning the model and generating quality results for completion. We evaluate the proposed method using two datasets from different business domains based on fluency, style of text and business sense of the application with perplexity and precision. Experimental results demonstrate that our framework is effective and generalizes well on unseen prefixes with acceptable latency using suitable optimizations.