Forecasting rainfall in tropical areas is challenging due to complex atmospheric behaviour, elevated humidity levels, and the common presence of convective rain events. In the Indian context, the difficulty is further exacerbated because of the monsoon intra seasonal oscillations, which introduce significant variability in rainfall patterns over short periods. Earlier investigations into rainfall prediction leveraged numerical weather prediction methods, along with statistical and deep learning approaches. This study introduces deep learning spatial model aimed at enhancing rainfall prediction accuracy on a finer scale. In this study, we hypothesize that integrating physical understanding improves the precipitation prediction skill of deep learning models with high precision for finer spatial scales, such as cities. To test this hypothesis, we introduce a physics informed ConvLSTM2D model to predict precipitation 6hr and 12hr ahead for Mumbai, India. We utilize ERA5 reanalysis data select predictor variables, across various geopotential levels. The ConvLSTM2D model was trained on the target variable precipitation for 4 different grids representing different spatial grid locations of Mumbai. Thus, the use of the ConvLSTM2D model for rainfall prediction, utilizing physics informed data from specific grids with limited spatial information, reflects current advancements in meteorological research that emphasize both efficiency and localized precision.

The proliferation of digital images and the advancements in deep learning have paved the way for innovative solutions in various domains, especially in the field of image classification. Our project presents an in-depth study and implementation of an image classification system specifically tailored to identify and classify images of Indian cities. Drawing from an extensive dataset, our model classifies images into five major Indian cities: Ahmedabad, Delhi, Kerala, Kolkata, and Mumbai to recognize the distinct features and characteristics of each city/state. To achieve high precision and recall rates, we adopted two approaches. The first, a vanilla Convolutional Neural Network (CNN) and then we explored the power of transfer learning by leveraging the VGG16 model. The vanilla CNN achieved commendable accuracy and the VGG16 model achieved a test accuracy of 63.6%. Evaluations highlighted the strengths and potential areas of improvement, positioning our model as not only competitive but also scalable for broader applications. With an emphasis on open-source ethos, our work aims to contribute to the community, encouraging further development and diverse applications. Our findings demonstrate the potential applications in tourism, urban planning, and even real-time location identification systems, among others.

About Indian Railways Indian Railways is the state-owned railway company of India, which is owned and operated by the Indian government. It is the fourth-largest railway network in the world and is responsible for providing transportation services to millions of passengers and freight across the country. Indian Railways was first established in 1853 when the first train ran from Bombay (now Mumbai) to Thane. Since then, it has grown to become a major contributor to the Indian economy, providing employment to over 1.3 million people, facilitating the transportation of goods and people, and promoting tourism. The railway network of Indian Railways is divided into 18 zones, each headed by a general manager. The zones are further divided into divisions, which are responsible for the management of train services and infrastructure in their respective areas.

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New Delhi: Scientists have used machine learning, a variant of artificial intelligence (AI), to identify the nature of thousands of new cosmic objects such as stars, black holes and pulsars. The researchers at Tata Institute of Fundamental Research (TIFR), Mumbai, and Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram, applied machine learning techniques to hundreds of thousands of space objects observed in X-ray wavelengths (0.03 and 3 nanometres) with NASA's Chandra space observatory. The study, published in the journal Monthly Notices of the Royal Astronomical Society, applied the technique to about 2,77,000 X-ray objects, the nature of most of which was unknown. A classification of the nature of unknown objects is equivalent to the discovery of objects of specific classes, the researchers said. This research has thus led to a reliable discovery of many thousands of cosmic objects of classes, such as black holes, neutron stars, white dwarfs, stars, etc, and opened up an enormous opportunity for the astronomy community for further detailed studies of many interesting new objects, they said.

The problems of selecting partial correlation and causality graphs for count data are considered. A parameter driven generalized linear model is used to describe the observed multivariate time series of counts. Partial correlation and causality graphs corresponding to this model explain the dependencies between each time series of the multivariate count data. In order to estimate these graphs with tunable sparsity, an appropriate likelihood function maximization is regularized with an l1-type constraint. A novel MCEM algorithm is proposed to iteratively solve this regularized MLE. Asymptotic convergence results are proved for the sequence generated by the proposed MCEM algorithm with l1-type regularization. The algorithm is first successfully tested on simulated data. Thereafter, it is applied to observed weekly dengue disease counts from each ward of Greater Mumbai city. The interdependence of various wards in the proliferation of the disease is characterized by the edges of the inferred partial correlation graph. On the other hand, the relative roles of various wards as sources and sinks of dengue spread is quantified by the number and weights of the directed edges originating from and incident upon each ward. From these estimated graphs, it is observed that some special wards act as epicentres of dengue spread even though their disease counts are relatively low.

Scientists of Tata Institute of Fundamental Research (TIFR), Mumbai, India and Indian Institute of Space Science and Technology (IIST) have identified the nature of thousands of new cosmic objects in X-ray wavelengths using machine learning techniques. Machine learning is a variant or part of artificial intelligence. Astronomy is entering a new era, as a huge amount of astronomical data from millions of cosmic objects are becoming freely available. This is a result of large surveys and planned observations with high-quality astronomical observatories, and an open data access policy. Needless to say that these data have a great potential for many discoveries and new understanding of the universe.

Speaking at the Microsoft Future Ready Leadership Summit in Mumbai as part of his four-day India visit, Nadella highlighted six "digital imperatives" that businesses must focus on today, and underlined the role that technologies and applications built natively on cloud platforms can play for modern businesses. Nadella highlighted that while generative AI tools, such as ChatGPT and Dall-E, generated less than 1% of the world's AI data sets in 2021, this can increase to 10% of all data generated by AI by 2025. "In future, the generative models will generate most of the data. We are right now seeing the emergence of a new reasoning engine. We'll clearly have to talk about this reasoning engine -- what are its responsible uses, what displacements will it cause, and so on. But on the other side, we should also think about how it can augment us in what we are doing today since it can have a huge impact on our future," Nadella said.

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