Tech expert Kurt'CyberGuy' Knutsson joins'Fox & Friends' to discuss the future of AI development in the United States. Vice President JD Vance is poised to kick off a trip to Italy and India on Friday – marking his third international trip with the Trump administration. Vance and the second family are poised to meet with and "discuss shared economic and geopolitical priorities with leaders in each country," according to a statement from Vance's office. When in Rome, Vance is scheduled to meet with Italy's Prime Minister Giorgia Meloni and Vatican Secretary of State Cardinal Pietro Parolin. He will meet with India's Prime Minister Narendra Modi while visiting New Delhi, Jaipur and Agra.

Training a neural network for pixel based classification task using low resolution Landsat images is difficult as the size of the training data is usually small due to less number of available pixels that represent a single class without any mixing with other classes. Due to this scarcity of training data, neural network may not be able to attain expected level of accuracy. This limitation could be overcome using a generative network that aims to generate synthetic data having the same distribution as the sample data with which it is trained. In this work, we have proposed a methodology for improving the performance of ANN classifier to identify built-up pixels in the Landsat$7$ image with the help of developing a simple GAN architecture that could generate synthetic training pixels when trained using original set of sample built-up pixels. To ensure that the marginal and joint distributions of all the bands corresponding to the generated and original set of pixels are indistinguishable, non-parametric Kolmogorov Smirnov Test and Ball Divergence based Equality of Distributions Test have been performed respectively. It has been observed that the overall accuracy and kappa coefficient of the ANN model for built-up classification have continuously improved from $0.9331$ to $0.9983$ and $0.8277$ to $0.9958$ respectively, with the inclusion of generated sets of built-up pixels to the original one.

State space models (SSMs) have emerged as a powerful framework for modelling long-range dependencies in sequence data. Unlike traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), SSMs offer a structured and stable approach to sequence modelling, leveraging principles from control theory and dynamical systems. However, a key challenge in sequence modelling is compressing long-term dependencies into a compact hidden state representation without losing critical information. In this paper, we develop a rigorous mathematical framework for understanding memory compression in selective state space models. We introduce a selective gating mechanism that dynamically filters and updates the hidden state based on input relevance, allowing for efficient memory compression. We formalize the trade-off between memory efficiency and information retention using information-theoretic tools, such as mutual information and rate-distortion theory. Our analysis provides theoretical bounds on the amount of information that can be compressed without sacrificing model performance. We also derive theorems that prove the stability and convergence of the hidden state in selective SSMs, ensuring reliable long-term memory retention. Computational complexity analysis reveals that selective SSMs offer significant improvements in memory efficiency and processing speed compared to traditional RNN-based models. Through empirical validation on sequence modelling tasks such as time-series forecasting and natural language processing, we demonstrate that selective SSMs achieve state-of-the-art performance while using less memory and computational resources.

This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.

On a stifling April afternoon in Ajmer, in the Indian state of Rajasthan, local politician Shakti Singh Rathore sat down in front of a greenscreen to shoot a short video. It was his first time being cloned. Wearing a crisp white shirt and a ceremonial saffron scarf bearing a lotus flower--the logo of the BJP, the country's ruling party--Rathore pressed his palms together and greeted his audience in Hindi. Before he could continue, the director of the shoot walked into the frame. Divyendra Singh Jadoun, a 31-year-old with a bald head and a thick black beard, told Rathore he was moving around too much on camera.

The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS) mounted on drones for reliable and secure power redistribution across the micro-grid network comprising green small cell BSs. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed hybrid drone-based framework integrates long short-term memory with unique cost functions using an evolutionary neural network for drones and BSs and efficiently manages energy and load redistribution. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack

The Eighth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2023) will be held at the beautiful IIT campus at Bluecity Jodhpur, Rajasthan from 21 to 23 July 2023. NCVPRIPG 2023 is being organized by the Indian Institute of Technology Jodhpur in association with the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI). NCVPRIPG aims to bring together researchers and practitioners from the allied areas of computer vision, graphics, image processing, and pattern recognition, in order to promote community-wide discussions of ideas that will influence and foster continued research in the field. Over the years the conference has grown into a vibrant national conference with participation from many students and researchers in the field. More information about the previous editions of the conference can be found here.

New Delhi, May 4: As India prepares the next-generation of coders, Amit Kumar, a data scientist at cure.fit, won the virtual'HackerCamp'22' for his idea of bringing computer vision, sensors and AI-based Fitness solutions to mobile devices, its organisers said here on Wednesday. Healthcare technology company Innovaccer, along with Microsoft and conversational messaging platform GupShup, organised'HackerCamp'22' -- one of the largest coding events that brought together ideas ranging from Augmented Reality/Virtual Reality, Blockchain, Artificial Intelligence, big data analytics and more from over 50,000 coders. The winners received incubation opportunities, cash prizes and other packages worth more than Rs 12 Lakh. While Kumar won in the professionals' track category, the winner of the freshers' track was Vishwas Modi, a B.Tech student at LNMIIT, Jaipur, for his'intelligent yoga trainer' with Leaderboard. "HackerCamp'22 opens the path for the tech wizards of the new age, offering them a platform to demonstrate their innovative ideas," said Ankit Maheshwari, President, R&D and India operations at Innovaccer.

According to the institute, its forecasting solution will help farmers deal with crop diseases in a timely manner and curb overuse of pesticides, which is rampant due to the lack of accurate information about the extent of crop infection. IIIT Naya Raipur's forecasting solution uses drones to monitor crops and capture live images if it detects any issues in them. The images are then sent from the drone in real time to the institute's servers, where an image classification model based on convolutional neural networks (CNN) is used to identify the disease and insects that are affecting it. CNNs are AI algorithms commonly used for image and video recognition. They can process an image, assign importance to its various attributes, and differentiate one image from another.