Figure 5 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
The antioxidant power of eugenol and vitamin C was examined by analyzing the ability of these ligands to bind to the NADPH oxidase protein target and evaluating their bond interactions with critical residues. The results confirm that docked ligands are more stable in the specified active region of 2CDU during a MD simulation of 100 ns and 2CDU prot...
Citations
... The results of the validation tests show that the suggested system is functional as expected. While the system did have some flaws, they were far from disastrous [135][136][137][138][139]. ...
Support vector machines, in the field of machine learning, are supervised learning models that examine data for classification and regression using learning methods that are connected with them. An SVM training algorithm constructs a model that allocates new examples to one of two categories based on a set of training examples, where each example is marked as belonging to one of the two sets. Using SVMs, datasets with unequal class frequencies can be handled. It is possible to set the slack penalty for positive and negative classes to different values in many implementations (asymptotically equivalent to changing the class frequencies). Improving classification algorithms or balancing classes in the training data (data preparation) before giving the data as input to the machine learning algorithm are techniques to deal with imbalanced datasets. Because of its more generalizability, the second method is better. In this research, we offer a strategy for efficiently classifying data with many features using a support vector machine (SVM). Instead of training all of the base classifiers required for a decision combination in advance, as is the case with typical combination methods, the suggested method trains each classifier separately and then combines their choices all at once. Because our suggested method entails addressing a single optimization problem, rather than the multiple optimization problems that existing methods need, training complexity can be decreased. In addition, while combining base classifiers, our suggested method takes their performance on training data and their ability to generalise into account. But conventional combination methods just look at how well a base classifier did on the training set. The results of the experiments validated the effectiveness of our strategy.
... The USB connection sends the board additional code. The first pin is a source of force [89,90,91,92,93,94,95]. The Simple In has 6 pins. ...
... When there are a lot of individuals in a realistic scenario, and some of them are partially or completely obscured for short periods of time, this becomes an even bigger issue. Researchers now have access to the software and hardware they need to consistently track and recognise human poses, thanks to the open-sourced implementation of human pose recognition and tracking, the emergence of open-source robotics, and the advent of affordable RGB-D sensors like the Microsoft Kinect [64][65][66][67][68][69][70][71]. ...
In the subject of computer vision, one of the most significant problems to investigate is human pose estimation. In today's world, there is a greater emphasis on automation, and we use surveillance and cameras to record everything that happens in our immediate environment or surroundings. The computer has a difficult time determining their stances for the purpose of the analytic process. Pose estimation is the process of anticipating the positions of the body parts or joints. Utilizations may include video monitoring, assisted living, advanced driver assistance systems, and sports analysis, among other potential applications. Because of their adaptability, humans are able to modify their stances regularly. An unsupervised machine learning approach known as a Generative Adversarial Network (GAN) is utilised by us in order to conduct an analysis of the postures assumed by human movement. With proper training, a GAN may be taught to produce images from random noises. The GAN is comprised of a generator and a discriminator. The generator is responsible for producing fake samples by utilising sounds, while the discriminator is responsible for attempting to differentiate between fake and real images. A basic input is used to generate a complex output, which is the goal of the GAN algorithm.
In this study, we use a machine learning method to identify cases of diabetic retinopathy in humans. The suggested approach uses classification algorithms on various variables from an existing Diabetic Retinopathy dataset, such as optical disc diameter, lesion-specific information (microaneurysms, exudates, or presence of haemorrhages), and so on. Following feature extraction, the presence of diabetic retinopathy may be predicted. A Decision Tree, a Logistic Regression, and a Support Vector Machine were all utilised in the proposed system's prediction process. Results from the current works were significantly worse than the suggested method's 88% accuracy. Using the SVM method, it can detect the existence of diabetic retinopathy, macular degeneration, myopia, and other retinal illnesses. The next step is to sort them according to their hue and morphological assumptions. For improved accuracy, the system is classified using an approach that combines Decision Trees with Logistic Regression and Support Vector Machines.
