The goal of cyber security is to protect the internet against online attacks. One of the most frequently used terminologies in cybersecurity is “cyber-threats,” which refers to the use of information and communication technology (ICT) for hostile purposes by a range of criminals. The complexity of cyber security architecture makes higher safety measures necessary to guard against system flaws and potential global catastrophes. One of the most important security issues today is cyberattack. A cyber breach could make nuclear systems safety and security safeguards ineffective, which is especially important for nuclear systems. India, which has a sizable and developing nuclear programme, has a similar situation. Over the past few decades, governments, including India, have invested a significant amount of time and money in building effective physical safeguards for nuclear installations, which has raised the risk of a cyber or hybrid attack. The risk of hacking, disruption, or sabotage rises as nuclear infrastructure becomes more and more reliant on cyber technology. Any cyberattack’s antagonistic goal is to take advantage of a system’s weaknesses in order to take over, operate, and keep a presence on the target system. Designing standards that can accommodate both immediate and long-term needs is crucial due to the sensitivity of nuclear materials and infrastructure. The study’s objective is to discuss the proposed arrangement of cyber security in nuclear domain in India

The stock market is the primary entity driving every major economy across the globe, with each investment designed to capitalize on profit while decreasing its associated risks. As a result of the stock market’s importance, there have been enumerable studies conducted with the goal of predicting the stock market through data analysis techniques including machine learning, neural networks, and time series analysis. This paper uses machine learning algorithms to perform stock market index classification using fundamental data while classifying the indices using technical indicators. The data were derived from Yahoo Finance on the top 100 indices in the NASDAQ stock market from January 2000 to December 2020.

The Landsat satellite series program has been developed and managed by the United States Geological Survey (USGS) since 1972, representing the longest temporal record of space-based terrestrial observation and taking into account the primary forces advancing the science concept in the global earth systems. Despite the malfunction images of Landsat 7 in 2003, the malfunction imagery preserves its important value in both technical studies and applications. Thus, there are numerous approaches and algorithms that have been presented to solve the SLC-off trouble by finding appropriate methods and approaches to predict the pixel reflectance values at gap locations accurately. This work aims to familiarize the reader with the concepts related to recovering the gap locations of the Landsat 7 SLC-off imagery approaches.