Ms Gali, who was visiting from India's Andhra Pradesh state, was reportedly heading towards a nearby temple with her family when she was swallowed by the 8m (26ft) deep sinkhole on the street of Jalan Masjid India. Excavators were deployed shortly after the incident to dig up the area around the sinkhole, while rescuers used sniffer dogs and crawler cameras - robotic cameras used to inspect pipes - to get a better sense of what was happening underground. They have also tried to break apart hardened debris using high-pressure water jets, iron hooks and rope. On Tuesday, officials wheeled a ground-penetrating radar device onto the site, to help them pinpoint changes in material density underground. The next day, a second sinkhole appeared just 50m from the first one.

September 8 has been celebrated as the'International Literacy Day' across the world since 1967. The significance of this day arises from the fact that despite the steady rise in literacy rates over the past 50 years, there are still 773 million illiterate adults around the world. In India, though the literacy rate has seen phenomenal growth--from 18.3% to 74.4% between 1951 and 2018--there are 313 million illiterate people, according to the study, "Literacy in India: The gender and age dimension." Illiteracy and dropout rates are acutely linked. Dropping out of school is a rampant trend in India.

New Delhi: Centre of Excellence on Satellite & Unmanned Remote Vehicle Initiative (CoE-SURVEI) has developed an Artificial Intelligence-based software which can automatically detect change on ground, including unauthorised constructions and encroachments in a time series using Satellite Imagery. The CoE-SURVEI, established by Directorate General Defence Estates (DGDE) at National Institute of Defence Estates Management, leverages latest technologies in survey viz. The CoE was inaugurated by Raksha Mantri Rajnath Singh on December 16, 2021. This Change Detection Software has been developed by CoE-SURVEI in collaboration with knowledge partner Bhabha Atomic Research Centre (BARC), Visakhapatnam. Presently, the tool uses National Remote Sensing Centre (NRSC) Cartosat-3 imagery with trained software.

Visakhapatnam: The need of the hour is the pervasive application of machine learning and data science in business management. This area requires skill-building, renewal of public policy and innovation, said director, Indian Institute of Management-Visakhapatnam, Prof M Chandrasekhar. The two-day virtual symposium on'applications of machine learning and data science in interdisciplinary areas' concluded on Monday at the Inter-Disciplinary Decision Sciences and Analytics Lab (IDeAL) of IIM-V. Former director of the Bill and Melinda Gates Foundation in India, Dr Nachiket Mor, spoke about the application of artificial intelligence-machine learning in public health settings. Former head of the Data Analytics Cell at NITI Aayog and faculty, Indian School of Business, Hyderabad, Dr Avik Sarkar, spoke about the role of analytics and policy modelling in assessing the progress toward sustainable development goals.

The Computer History Museum (CHM) in Silicon Valley has honored Raj Reddy, an Indian-American professor and researcher, as part of its 2021 Fellow Awards program to contribute to artificial intelligence and continuous voice recognition. The other three recipients for 2021 were Raymond Ozzie, Lillian F. Schwartz, and Andries van Dam. Reddy, who grew up in the Andhra Pradesh district of Chittoor, has been teaching for five decades and is the founder of The Robotics Institute at Carnegie Mellon University in Pittsburg, Pennsylvania. The AI pioneer was also a driving force behind the Rajiv Gandhi University of Knowledge Technology establishment in Nuzvid, Andhra Pradesh. "New technologies have made it easier than ever to share knowledge and information and reach new audiences in this digital world," Reddy added.

The School of Entrepreneurship and Management Studies (SEAMS), SRM University-AP Andhra Pradesh, introduces a series of academic webinars exclusively for its students from the departments of BBA and MBA. The first among the series, organised on the theme AI/ML in Healthcare, will be held on June 19, 2021, at 4.00 pm. Ltd, will be the guest speaker of the webinar. Dr Dasgupta earned his PhD in Statistics from the University of Florida and is currently an adjunct professor of Data Science at Chennai Mathematical Institute. Speakers from industry and academia will be invited for every session of the webinar series to throw light on diverse topics.

CMR Shopping Mall, a subsidiary of the CMR Group, is a known brand in Andhra Pradesh with a strong presence in textiles, jewellery, and real estate. While pandemic has put a dent on the shopping mall business, CMR is picking up momentum, with an average footfall of 4,000-10,000 every day. However, as a large retailer, CMR Shopping Mall's technology adoption was subpar. Due to the scarcity of skilled workforce amid pandemic, the retailer had to bear the brunt of fraudulent activities and inefficiency in its supply chain management. Moreover, CMR Shopping Mall was beset by price wars and was struggling with tax structure complexities.

After successfully rolling out in Maharashtra, Mahindra & Mahindra's Farm Equipment Sector (FES), recently rolled out Krish-e centres in Tadepalligudem, Nandyala and Tenali in Andhra Pradesh along with Mahbubnagar, Miryalaguda and Kamareddy districts in Telangana as part of Mahindra's new'Farming as a Service' (FaaS) business. Parinaam Dikhaye' – Krish-e is a business vertical that provides technology driven services which are progressive, affordable and accessible to farmers. Krish-e aims to increase farmers' income through digitally enabled services, across the complete crop cycle. These include agronomy advisory, access to advanced farm equipment rentals and new-age precision farming solutions, all focused on bringing down overall farming costs and improving crop output and consequently the farmers' income. Besides Maharashtra, Adhra Pradesh and Telangana, Krish-e will also roll-out centres across other states in a phased manner.

By August 2020, India had reported several million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with cases tending to show a younger age distribution than has been reported in higher-income countries. Laxminarayan et al. analyzed data from the Indian states of Tamil Nadu and Andhra Pradesh, which have developed rigorous contact tracing and testing systems (see the Perspective by John and Kang). Superspreading predominated, with 5% of infected individuals accounting for 80% of cases. Enhanced transmission risk was apparent among children and young adults, who accounted for one-third of cases. Deaths were concentrated in 50- to 64-year-olds. Incidence did not change in older age groups, possibly because of effective stay-at-home orders and social welfare programs or socioeconomic status. As in other settings, however, mortality rates were associated with older age, comorbidities, and being male. Science , this issue p. [691][1]; see also p. [663][2] Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures. [1]: /lookup/doi/10.1126/science.abd7672 [2]: /lookup/doi/10.1126/science.abe9707

Ensemble models are powerful model building tools that are developed with a focus to improve the accuracy of model predictions. They find applications in time series forecasting in varied scenarios including but not limited to process industries, health care, and economics where a single model might not provide optimal performance. It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated models), the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble models use a static set of weights. Due to this limitation, approaches using a static set of weights for weighing ensemble models cannot capture the dynamic changes or local features of the data effectively. To address this issue, a Reinforcement Learning (RL) approach to dynamically assign and update weights of each of the models at different time instants depending on the nature of data and the individual model predictions is proposed in this work. The RL method implemented online, essentially learns to update the weights and reduce the errors as the time progresses. Simulation studies on time series data showed that the dynamic weighted approach using RL learns the weight better than existing approaches. The accuracy of the proposed method is compared with an existing approach of online Neural Network tuning quantitatively through normalized mean square error(NMSE) values.