As given in Agriculture Material census report 2011, Uttar Pradesh has population of 19.98 crore at that time. Since, in 2001 it was 16.62 crore approximation i

In the rapid world of technology, the IT Industry is growing at a rapid speed with Cloud Image Security. In the developing trends, cloud orchestration is becoming impressive and uncertain of dealing a large amount of information. In the last two decades there has been extensive development in image encryption. Chaos based image encryption techniques form framework for a faster algorithm than traditional techniques. Chaos based techniques are highly efficient in case of multimedia data. The proposed algorithm uses the modified logistic map for the generation of chaotic sequence, encrypts the image in single scan, so runs faster and makes the proper use of confusion and diffusion. It dynamically updates the key during execution. This algorithm is secure against the brute force attack and differential attack.

In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the Saliency Map Model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.

For the security of the Society, Video surveillance systems have become an important tool that can help in preventing crime in public or private establishments, leading to more safe and secure environment. With the rise of technology that can handle high volune video data and the need for continuous monitoring for security to citizens, the analysis of the video footages, during security breach attempts has become more challenging. Video summarization can play a crucial role in secure surveillance systems by optimizing the analysis time of the security breach. This research attempts to summarise the important aspects of the original video without losing its context and also optimizes the storage requirement. To achieve the same a unique KM-LSTM based technique has been developed that utilizes deep learning methods to understand links among highlight as well as non-highlight video segments through the use of pairwise distance calculations. A new Deep LSTM model has been used to extract the features from all segments of frames. These extracted features are clustered using k-means clustering. Then, such features are trained using a pairwise deep learning model to get highlight scores as well as leverage the comparative similarity among pairs of the highlight as well as non-highlight segments. At last, video summarization takes place to generate the video summary from the highlight segments only. From the results of these experiments discovering the moments of the user’s major or special interest (namely, highlights) in a video, to generating summarization of videos. The above technique can help in increasing the analysis of Video content and thus achieving a more vigilant and secure society.

The issue of cheating in competitive gaming is a major concern that undermines fair play and the overall gaming experience. Although machine learning has proven to be an effective tool for detecting cheats, there is still untapped potential for innovative approaches that can further improve anti-cheat systems. This research paper delves into unexplored machine learning methods that hold promise for future cheat detection strategies. These include Graph Neural Networks (GNNs) for multifaceted analysis, reinforcement learning-based cheat adaptation, federated learning for privacy-preserving cheat detection, Explainable AI (XAI) for transparent cheat detection, Generative Adversarial Networks (GANs) for anomaly detection, unsupervised learning for unlabeled data analysis, active learning for efficient data collection, continuous learning for evolving cheat landscapes, multi-agent reinforcement learning for collaborative cheat detection, and transfer learning for cross-game cheat detection. By leveraging these novel techniques, the gaming industry can develop more robust and comprehensive cheat detection solutions that uphold the integrity of competitive gaming environments.