Authors - Yogini Prasanna Paturkar, Amol P. Bhagat, Priti A. Khodke Abstract - Social media platforms generate vast amounts of interaction data, offering valuable insights into user behavior, preferences, and trends. However, the sheer volume and velocity of this data pose significant challenges for real-time analysis and computational efficiency. This paper proposes a framework for random sampling of social media interaction data to address these challenges. By employing probabilistic sampling methods, we aim to reduce data volume while preserving key statistical properties and minimizing information loss. The proposed methodology leverages stratified and weighted random sampling techniques to ensure the representation of diverse user groups and interaction types. Applications of this approach include sentiment analysis, trend detection, and user behavior modeling. Preliminary experiments demonstrate that random sampling can achieve a significant reduction in computational overhead while maintaining analytical accuracy within an acceptable margin of error. This framework has the potential to enhance data processing pipelines in fields such as marketing, public opinion analysis, and event monitoring, enabling timely and resource-efficient decision-making.
Authors - Riya Reddy, Ayoush Singh, Keshav Tanwar, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti Abstract - This study examines the underlying drivers of usage intention of chatbots by bank customers using UTAUT model. This model assesses four key factors determining the usage intent of chatbots: performance expectancy, effort expectancy, social influence, and facilitating conditions. Primary data was obtained through structured questionnaire from retail banking customers. Structural Equation Modelling method is used and statistical tool SmartPLS 4.0 was employed to analyze complex associations between variables. The results show that performance expectancy, facilitating conditions, and motivation have a significant association with the usage intention of chatbots; however, effort expectancy and social influence have no connection. In addition, the results show that perceived security and trust are essential criteria for adopting a chatbot in banking. The study contributes value to the existing body of knowledge by proving the factor influencing the intent usage of chatbots in the banking industry. It also highlights future work directions in the form of long-term impacts and insights that can guide banks in designing customer-centric AI systems and improving chatbot services.
Authors - Soham Kulkarni, Suhani Thakur, Twsha Vyass, V. Yasaswini, Pooja Kamat Abstract - Dyslexia is a learning disorder that causes difficulty in reading and identifying relations between words and letters.[1] To improve reading accessibility within dyslexic patients, this study aimed to develop models for summarization of documents as well as provide a streamlined method to convert documents into dyslexia-friendly versions. Within this study, several summarization models were tested to generate effective text summaries, while defining Python functions to convert regular text into dyslexia friendly text. Models attempted with are: Term Frequency- Inverse Document Frequency Summarizer, Term Frequency-Inverse Document Frequency Summarizer with Support Vector Machine, and BART Transformer. After analysing the results, the BART Trained Summarization Model results are fruitful having a ROUGE R-1 F1 Score of 0.4510, a R-2 F1 Score of 0.2571 and a R-L F1 Score of 0.4177, ultimately successfully generating dyslexia-friendly summarized documents.
Authors - Mihir Deshpande, Hrishikesh Patkar, Madhuri Wakode Abstract - Intelligent Document Processing (IDP) automates extraction and categorization of information from unstructured and semi-structured documents. While standard Optical Character Recognition (OCR) computerizes the parsing of printed and scanned documents, so-called parsing tools like PyPDF2 and React-PDF take on the extraction of text from digital files. Emerging developments in IDP apply Large Language Models (LLMs) to enhance Natural Language Processing (NLP) to ensure the efficient interpretation, classification, and analysis of the extracted data. This paper provides a survey of IDP technologies with possible applications in financial and retail sectors-invoice processing, purchase order matching, and fraud detection. It also focuses on accuracy, scalability, and outlook of LLM-based IDP on the cloud, towards next-generation automation in more detail.
Authors - Sahana Balajee, Sakhi Saswat Panda, Anushruth Gowda, Animesh Giri Abstract - The extension of conventional Internet of Things (IoT) technologies from basic applications to advanced industrial processes has revolutionised operational efficiency, giving rise to a new class of IoTs called the Industrial Internet of Things (IIoT) systems. Despite these advancements, critical IIoT infrastructures remain exposed to a growing range of cybersecurity threats due to open vulnerabilities, stemming from the fact that these IIoT networks try to interconnect physical machinery with digital systems. Cyber attacks targeting IIoT systems attempt to exploit these vulnerabilities in order to cause severe operational disruptions and costly outages. These intrusions pose significant risks to data integrity, operational continuity, and total production, highlighting the need for effective cybersecurity measures in the industrial setting. To help prevent such intrusions this work proposes a unified predictive framework combining cyber attack detection with outage prediction to enhance resilience in IIoT environments. By leveraging machine learning algorithms, this framework analyses two types of data - network traffic for cyber threats and historical sensor data for forecasting the Remaining Useful Life (RUL) of critical components. This approach aims to identify risks and send alerts to minimise operational downtime preemptively, integrating predictive techniques for both cybersecurity and system reliability to strengthen IIoT systems, thereby helping industries maintain continuous, secure, and safe operations.
Authors - Sakshi Prakash Masand, Sadhana Shashidhar, Animesh Giri Abstract - High-stakes industries like the aviation industry demand minimal downtime requiring unified solutions that address all maintenance and troubleshooting needs. Existing solutions often function in isolation, requiring multiple systems for predictive analytics and manual-based repairs. To bridge this gap, this article presents a novel integration of machine learning based predictive maintenance and conversational AI for routine servicing and troubleshooting operations in industrial IoT systems, tailored to critical sectors such as aviation. A robust predictive maintenance model is built to predict the Remaining Useful Life (RUL) for aircraft components. Multiple traditional machine learning models like Random Forest, Support Vector Regression (SVR), XGBoost, and deep learning techniques like Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) are compared for performance and accuracy, ultimately focusing on a refined time series specific approach. When the predicted RUL of a component falls below a predefined threshold, operators are automatically alerted to schedule maintenance. For interactive support, Rasa, a customisable conversational AI framework and a fine tuned LLaMA model provide instant and enterprise specific guidance, offering step by step instructions and reducing reliance on lengthy manuals. This solution combines predictive maintenance with dynamic assistance, saving valuable time and resources for the industry.
