Large Language Models (LLMs) have demonstrated remarkable capabilities in text comprehension, but their ability to process complex, hierarchical tabular data remains underexplored. We present a novel approach to extracting structured data from multi-page government fiscal documents using LLM-based techniques. Applied to annual fiscal documents from the State of Karnataka in India (200+ pages), our method achieves high accuracy through a multi-stage pipeline that leverages domain knowledge, sequential context, and algorithmic validation. A large challenge with traditional OCR methods is the inability to verify the accurate extraction of numbers. When applied to fiscal data, the inherent structure of fiscal tables, with totals at each level of the hierarchy, allows for robust internal validation of the extracted data. We use these hierarchical relationships to create multi-level validation checks. We demonstrate that LLMs can read tables and also process document-specific structural hierarchies, offering a scalable process for converting PDF-based fiscal disclosures into research-ready databases. Our implementation shows promise for broader applications across developing country contexts.

Accurate long-range monsoon rainfall prediction is critical for India's rain-fed agricultural economy and climate resilience planning, yet remains hindered by sparse ground data and complex regional variability. This work proposes a multimodal deep learning framework for gridded precipitation classification using satellite-derived geospatial inputs. Unlike previous rainfall prediction methods relying on coarse-resolution datasets of 5-50 km grid, we curate a high-resolution dataset of projected 1 km grid resolution for five Indian states, integrating seven heterogeneous Earth observation modalities, including land surface temperature, vegetation, soil moisture, humidity, wind speed, elevation, and land use, spanning the June-September 2024 period. We adopt a attention-guided U-Net architecture that captures spatial patterns and temporal dependencies across multi-modalities, and propose a combination of focal and dice loss to address class imbalance and spatial coherence in rainfall categories defined by the India Meteorological Department. Extensive experiments show that the multi-model framework significantly outperforms unimodal baselines and existing deep approaches, especially in underrepresented extreme rainfall zones. The framework demonstrates potential for scalable, region-adaptive monsoon forecasting and Earth observation driven climate risk assessment.

Farmers in developing regions like Karnataka, India, face a dual challenge: navigating extreme market and climate volatility while being excluded from the digital revolution due to literacy barriers. This paper presents a novel decision support system that addresses both challenges through a unique synthesis of machine learning and human-computer interaction. We propose a hybrid recommendation engine that integrates two predictive models: a Random Forest classifier to assess agronomic suitability based on soil, climate, and real-time weather data, and a Long Short-Term Memory (LSTM) network to forecast market prices for agronomically viable crops. This integrated approach shifts the paradigm from "what can grow?" to "what is most profitable to grow?", providing a significant advantage in mitigating economic risk. The system is delivered through an end-to-end, voice-based interface in the local Kannada language, leveraging fine-tuned speech recognition and high-fidelity speech synthesis models to ensure accessibility for low-literacy users. Our results show that the Random Forest model achieves 98.5% accuracy in suitability prediction, while the LSTM model forecasts harvest-time prices with a low margin of error. By providing data-driven, economically optimized recommendations through an inclusive interface, this work offers a scalable and impactful solution to enhance the financial resilience of marginalized farming communities.

Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict Tuberculosis (TB) treatment outcomes more accurately. It transforms this prediction task into a binary classification problem, generating risk scores from patient data sourced from NIKSHAY, India's national TB control program, which includes over 500,000 patient records. Data preprocessing is a critical component of the study, and the model achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set, which includes 20,000 patient records.We also explore the use of Natural Language Processing (NLP) for improved model learning. Our results, corroborated by various metrics and ablation studies, validate the effectiveness of our approach. The study concludes by discussing the potential ramifications of our research on TB eradication efforts and proposing potential avenues for future work. This study marks a significant stride in the battle against TB, showcasing the potential of machine learning in healthcare.

Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Even IoT-enabled systems can deliver economical health monitoring solutions tailored to pregnant women within their daily environments. Recent research indicates that these remote health monitoring setups have the potential to enhance the well-being of both the mother and the infant throughout the pregnancy and postpartum phases. One more emerging advancement is the integration of 'panic buttons,' which are gaining popularity due to the escalating emphasis on safety. These buttons instantly transmit the user's real-time location to pre-designated emergency contacts when activated. Our solution focuses on the above three challenges we see every day. Fall detection for the elderly helps the elderly in case they fall and have nobody around for help. Sleep pattern sensing is helpful for pregnant women based on the SPO2 sensors integrated within our device. It is also bundled with heart rate monitoring. Our third solution focuses on a panic situation; upon pressing the determined buttons, a panic alert would be sent to the emergency contacts listed. The device also comes with a mobile app developed using Flutter that takes care of all the heavy processing rather than the device itself.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Janes enables militaries, governments, and defence companies to make critical decisions. Our expert-driven tradecraft, developed over 120 years, combined with human-machine teaming, delivers assured open-source intelligence across military capabilities and order of battle, equipment, events, countries, companies, and markets. Linking millions of assured data points, Janes data model creates a framework of interconnected open-source defence intelligence. This allows our customers to integrate all relevant data and connections into a single intelligence environment to deliver a more complete and accurate answer. Using Janes, our customers can use their scarce resource more effectively, to get to better decisions with higher confidence, more quickly.

Prime Minister Narendra Modi inaugurated the 25th edition of Bengaluru Tech Summit 2022, at a glittering ceremony in the presence of a galaxy of leaders from the IT, BT and start-up sectors. The event was organized by The Department of Electronics, IT, Bt, S&T, Government of Karnataka along with Software Technology Parks of India (STPI), and is the first full-fledged on ground version of BTS post the pandemic. The central theme of BTS this year is'Tech4NexGen' and will focus on Electronics, IT, Deep Tech, Biotech, and Startups. The inaugural ceremony was graced by the Guests of Honor H.E Mr. Petri Honkonen, Minister of Science & Culture, Finland, H.E. Mr. Omar Bin Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy & Remote Work Applications, United Arab Emirates, H.E. Mr. Tim Watts, Assistant Minister for Foreign Affairs, Australia. The ceremony was presided over by Shri Basavaraj S. Bommai, Chief Minister of Karnataka.

A number of models have been developed in India to forecast the spread of the coronavirus disease or COVID-19 in the country. Model building has had to incorporate our evolving knowledge of the disease, including the appearance of new variants, immune escape leading to reinfections, time-varying non-pharmaceutical interventions, the pace of the vaccination program, and breakthrough infections. The predictive power of these models has been hampered by the lack of availability of quality data on infection and deaths as a function of age, the nature of social contacts, demography, and the clinical consequence of infection. An early emphasis on "ensemble models," a thrust toward increased data availability, a greater engagement of modelers with the epidemiological and public health communities, and a more nuanced approach to communicating the limitations of modeling could have substantially increased the usefulness of models during the COVID-19 pandemic in India. Most models were variants of the SEIR model where the individuals in the population move from S susceptible to E exposed to I infectious to R removed compartments.

Bengaluru: BMS College of Engineering (BMCSE) and BMS Institute of Technology and Management (BMSIT&M), managed by BMS Educational Trust, one of the highest-ranking Engineering colleges in Karnataka, has set up its first of its kind Department of Machine Learning and Karnataka's first BS Narayan Center of Excellence in Artificial Intelligence and Machine Learning. The center was inaugurated in the presence of the Chief guest – Dr. Devesh Vatsa, Air Vice Marshal, Awardee of Vishishta SEVA Medal, Commandant, Software Development Institute, Indian Air Force, Bengaluru, Presided by Sri Aviram Sharma, Chairman, BMSIT&M and Trustee, BMSET Guest of Honor – Prof. S Sadagopan, Chairman BoG, IIITDM – Kancheepuram & Prof. Pradip K Das, Professor, Dept of CSE, IIT Guwahati, Assam, along with dignitaries including Dr. B S Ragini Narayan, Donor Trustee & Member Secretary, BMS Educational Trust, Dr. P Dayananda Pai, Chairman, BMSCE & Life Trustee, BMS Educational Trust, Sri Gautham V Kalathur, Chief Technical Officer, BMSET. The Department of Machine Learning, BMSCE poised to be the youngest branch in the 75 years old Engineering college, will focus on teaching and research in the domains of Deep Learning, Data Mining, Big Data and Natural Language Processing. This self-contained department comprises of all necessary facilities that every student need including well-furnished and ventilated, centrally air-conditioned, state-of-the-art classrooms affixed with SENSES smartboards with all educational software. The B S Narayan Center of Excellence (CoE) in Artificial Intelligence and Machine Learning established by BMSET, with an investment of INR 2.5 cr will provide platform incubation support & AI-based entrepreneurship and will foster dynamic industry-academic synergy for AI adoption, impactful projects with industry & government.