Bøger af Prathibhavani P. M.

Climate change has had a negative impact on the performance of most crops in India over the previous two decades. Crop yield prediction ahead of time would assist farmers and policymakers in determining appropriate marketing, transportation, and storage strategies. This proposed method will assist farmers in determining the crop yield prior to the cultivation of the agricultural land, allowing them to make informed decisions. In this work, first identify the factors that influence crop output to effectively predict the yield. Temperature, soil moisture, humidity, solar radiation, and pH value are all important factors. Needed to collect and analyze data on these factors for our benefit and there are various ways or algorithms for such data analysis in crop prediction, and can predict the crop yield with the help of these algorithms. In this proposed method, would like to look at the problem from the perspective of Machine Learning by evaluating various algorithms such as Random Forest, Simple Linear Regression (SLR), and Neural Networks to guarantee that considered the best algorithm and achieve the highest possible accuracy.

It is incredible how the entire world is totally dependent on the data. Data generation as how human speak and it is also true that they consume data through every possible medium. With the frequent and regular updates and upgrades in technology, human beings have witnessed the growth of smart devices unlike any. We have also seen a significant increase in the usage of the Internet of Things (IoT) and for some of us, it has been an integral part of daily life. Hence, it wouldn't be surprised when we realize that the IoT devices we are using are vulnerable to various malicious attacks. These IoT devices are elemental in both design and security. Their intrinsic design frailty is exploited during firmware attacks and their low-level network security is easily penetrated during the cyber-attacks. One such common cyber-attack is Distributed Denial of Services (DDoS) and the proposed work aims to address this against a network of IoT devices.

Convolutional Neural Networks (CNN) and Machine Learning (ML) have evolved a long way in the modern technological era. They are used in analysis and prediction of various segments of normal life. They have a greater advantage when it comes to understanding a process, sometimes even better than a human brain. The Virus that is generated as COVID-19 is mainly caused when a person coughs, sneeze or exhales and it is transmitted as a small particle to other bodies and form these viruses. One of the major tricks for stopping the spread of this covid-19 is social distancing and rapid testing. This rapid test will take 2-3 days of time to get the result of covid-19. This may trouble a large amount of people in terms of money and time. Therefore, with the advantage of CNN, devised a method to increase the efficiency of the testing process. X-rays and CT scans show considerable advantage in detecting COVID-19 in a person. COVID-19 affects the lungs primarily which could be caught in X-rays as a white overlay. Constructed a model that could identify whether the submitted X-ray is of a normal person or of a COVID-19 positive person.