With reinforcement learning powered by big data and computer infrastructure, data-centric AI is d... more With reinforcement learning powered by big data and computer infrastructure, data-centric AI is driving a fundamental shift in the way software is developed. To treat data as a first-class citizen on par with code, software engineering must be rethought in this situation. One surprise finding is how much time is spent on data preparation throughout the machine learning process. Even the most powerful machine learning algorithms will struggle to perform adequately in the absence of high-quality data. Advanced technologies that are data-centric are being used more frequently as a result. Unfortunately, a lot of real-world datasets are small, unclean, biased, and occasionally even tainted. In this study, we focus on the scientific community for data collecting and data quality for deep learning applications. Data collection is essential since modern algorithms for deep learning rely mostly on large-scale data collecting than classification techniques. To enhance data quality, we invest...
Natural language processing innovations in the past few decades have made it feasible to synthesi... more Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large languag...
The Review of Contemporary Scientific and Academic Studies
With reinforcement learning powered by big data and computer infrastructure, data-centric AI is d... more With reinforcement learning powered by big data and computer infrastructure, data-centric AI is driving a fundamental shift in the way software is developed. To treat data as a first-class citizen on par with code, software engineering must be rethought in this situation. One surprise finding is how much time is spent on data preparation throughout the machine learning process. Even the most powerful machine learning algorithms will struggle to perform adequately in the absence of high-quality data. Advanced technologies that are data-centric are being used more frequently as a result. Unfortunately, a lot of real-world datasets are small, unclean, biased, and occasionally even tainted. In this study, we focus on the scientific community for data collecting and data quality for deep learning applications. Data collection is essential since modern algorithms for deep learning rely mostly on large-scale data collecting than classification techniques. To enhance data quality, we investigate data validation, cleaning, and integration techniques. Even if the data cannot be completely cleaned, robust model training strategies enable us to work with imperfect data during training the model. Furthermore, despite the fact that that these issues have gotten less attention in conventional data management studies, bias and fairness are significant themes in modern application of machine learning. In order to prevent injustice, we investigate controls for fairness and strategies for doing so before, during, and after model training. We believe the information management community is in a good position to address these problems.
A monitoring and alerting application is introduced in this paper. IP Cameras are special cameras... more A monitoring and alerting application is introduced in this paper. IP Cameras are special cameras that stream the video feed into the internet as a video stream. This stream is captured and can be monitored for any unauthorized activities. Motion detection is done when movements are detected and the alarm goes ON if such movements are detected. We can start the alarm feature by entering a password and clicking on start button. Enabled for both local cameras as well as IP cameras. Remote monitoring from any part of the world using a very efficient motion detection algorithm for better security. The motion detection module consists of the motion detection algorithm which helps us to analyze the camera feed and to detect and signal any motion related triggers. It also comes with a motion sensitivity panel were you get to adjust the level of motion sensitivity that might be required. Block matching algorithm is used for motion detection and frame validation in the system.
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Papers by Gowri Vidhya N