The North Indian Ocean, including the Arabian Sea and the Bay of Bengal, represents a vital source of livelihood for coastal communities, yet fishermen often face uncertainty in locating productive fishing grounds. To address this challenge, we present an AI-assisted framework for predicting Potential Fishing Zones (PFZs) using oceanographic parameters such as sea surface temperature and chlorophyll concentration. The approach is designed to enhance the accuracy of PFZ identification and provide region-specific insights for sustainable fishing practices. Preliminary results indicate that the framework can support fishermen by reducing search time, lowering fuel consumption, and promoting efficient resource utilization.

The generalization of language models (LMs) is undergoing active debates, contrasting their potential for general intelligence with their struggles with basic knowledge composition (e.g., reverse/transition curse). This paper uncovers the phenomenon of linear correlations in LMs during knowledge composition. For explanation, there exists a linear transformation between certain related knowledge that maps the next token prediction logits from one prompt to another, e.g., "X lives in the city of" $\rightarrow$ "X lives in the country of" for every given X. This mirrors the linearity in human knowledge composition, such as Paris $\rightarrow$ France. Our findings indicate that the linear transformation is resilient to large-scale fine-tuning, generalizing updated knowledge when aligned with real-world relationships, but causing hallucinations when it deviates. Empirical results suggest that linear correlation can serve as a potential identifier of LM's generalization. Finally, we show such linear correlations can be learned with a single feedforward network and pre-trained vocabulary representations, indicating LM generalization heavily relies on the latter.

A major roadblock in the seamless digitization of medical records remains the lack of interoperability of existing records. Extracting relevant medical information required for further treatment planning or even research is a time consuming labour intensive task involving expenditure of valuable time of doctors. In this demo paper we present, MedPromptExtract an automated tool using a combination of semi supervised learning, large language models, natural language processing and prompt engineering to convert unstructured medical records to structured data which is amenable for further analysis.

Population censuses are vital to public policy decision-making. They provide insight into human resource, demography, culture, and economic structure at local, regional, and national levels. However, such surveys are very expensive (especially for low and middle-income countries with high populations, such as India), time-consuming, and may also raise privacy concerns, depending upon the type of data collected. In light of these issues, we introduce SynthPop++, a novel hybrid framework, which can combine data from multiple real-world surveys (with different, partially overlapping sets of attributes) to produce a real-scale synthetic population of humans. Critically, our population maintains family structures comprising individuals with demographic, socioeconomic, health, and geolocation attributes: this means that our "fake" people live in realistic locations, have realistic families, etc. Such data can be used for a variety of purposes: we explore one such use case, Agent-based modelling of infectious disease in India. To gauge the quality of our synthetic population, we use machine learning and statistical metrics. Our experimental results show that synthetic population can realistically simulate the population for various administrative units of India, producing real-scale, detailed data at the desired level of zoom - from cities, to districts, to states, eventually combining to form a country-scale synthetic population. Financial institutions, government agencies, think tanks, etc. are using techniques like agent-based modelling(ABM) Bonabeau (2002) to simulate increasingly complex scenarios for decision-making.

Two key problems in India's water sector are the estimation of dry-spell vulnerability during kharif, the monsoon season, and the design of water and energy-planning inputs to help villages undertake demand-side management during rabi, the post-monsoon season. In this article, we report our joint work with the Government of Maharashtra's Department of Agriculture on a World Bank-assisted program called the Project on Climate Resilient Agriculture, or PoCRA. The project is spread over 5,000 villages in 15 districts of Maharashtra (see Figure 1). Its main objective is to make smallholder farmers resilient to climate variability through targeted interventions. A key strategy is to promote water and energy budgeting in these villages and to supplement the community infrastructure and the capabilities of individual farmers.

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate part of the semantic representation to a strongly pre-trained semantic parser, while learning transpiling with small amount of paired data. We depart from recent work relating AMR and SPARQL constructs, but rather than applying a set of rules, we teach a BART model to selectively use these relations. Further, we avoid explicitly encoding AMR but rather encode the parser state in the attention mechanism of BART, following recent semantic parsing works. The resulting model is simple, provides supporting text for its decisions, and outperforms recent approaches in KBQA across two knowledge bases: DBPedia (LC-QuAD 1.0, QALD-9) and Wikidata (WebQSP, SWQ-WD).

This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities. This can help authorities in these regions with resource planning measures to redirect resources to the regions identified by the model. Due to the lack of publicly available data on the influx of patients in hospitals, or the shortage of equipment, ICU units or hospital beds that regions in these countries might be facing, we leverage Twitter data for gleaning this information. The approach has yielded accurate results for states in India, and we are working on validating the model for the remaining countries so that it can serve as a reliable tool for authorities to monitor the burden on hospitals.

Abstract--A reverse dictionary takes a target word's description This difficulty is handled by the'reverse dictionary'. Currently, there does not exist any Reverse Dictionary provider with support for any Indian Language. Dictionaries have many practical usages e.g. This architecture uses the Translation Language Modeling (TLM) technique, rather than the conventional BERT's Masked Sometimes, new language learners can describe a word in a particular language but fail to retrieve the exact word I. Anomia patients, people who are able to recognize and describe an object but are not able to name it due to a neurological disorder, can also be assisted by using a reverse dictionary. To address all these issues more accurately, we propose and develop a novel open-source Reverse Dictionary named "WordAlchemy", mainly based on the proposed transformerbased mT5 [2] model. In this section, we will focus on the previous work in the domain of Reverse Dictionary.

Udacity has released the list of the most popular Nanodegree programs in India in 2020. The data is based on the number of enrollments during the year, showing the demand across different states and union territories. Deep learning and data engineering were the top Nanodegree programs showing the country's growing interest towards artificial intelligence and data. While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data. There is no doubt about the fact that COVID-19 has changed the global job landscape, said a statement from the company.

We have entered an era of a pandemic that has shaken the world with major impact to medical systems, economics and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes any such modelling attempts unreliable. Hence we need to re-look at the situation with the latest data sources and most comprehensive forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. In this paper, prominent recurrent neural networks, in particular \textit{long short term memory} (LSTMs) networks, bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting the spread of COVID-infections among selected states in India. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak and provide two months ahead forecast that shows that cases will slowly decline. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas. We note that although we made some progress in forecasting, the challenges in modelling remain due to data and difficulty in capturing factors such as population density, travel logistics, and social aspects such culture and lifestyle.