We suggest future research should focus on scalability problems, innovative optimization techniques, domain knowledge integration, and improving interpretability. The present study aims to provide an in-depth r
51, the author proposed an integrated particle swarm optimization (PSO) as well as an ELM technique to analyze PT faults. Despite that, these analysis techniques examined earlier retain their fundamental disadvantages as follows: (i) For EPS, a complete understanding root is a vital feature to ...
The application of classsification methods through multivariate and machine learning techniques has enormous significance in agricultural sector. It is vital to classify various types of seeds as well as identify the quality of seeds which has a great impact on the production of crops. There is a...
In binary classification problems, the first step is to normalize the columns of the measurement matrix A, because in the objective function of the optimization problem (15), all components of the weight vector w are treated equally. In both the data replication and the one vs. all methods,...
et al. Improvement of power quality parameters using modulated-unified power quality conditioner and switched-inductor boost converter by the optimization techniques for a hybrid AC/DC microgrid. Sci. Rep. 12, 21675. https://doi.org/10.1038/s41598-022-26001-8 (2022). Article ADS PubMed Pub...
Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been de
and meta-heuristic optimization techniques. The study gives information on many methods used to identify diseases in various plants. The study’s various elements include the type of segmentation, dividing technology, extracted features, dataset size and year of publication, disease category, methodologie...
The risk r(t) of a node t is the optimization criterion e(t) multiplied by the probability of the node: r(t) = P(t)e(t). The risk for the entire tree is the sum of the risks over the leaf nodes in the tree. In order to not overtrain a tree, the software can add the ...
optimization, the neurons in the same layer are not connected. Neural networks with three layers have the capability to approximate any function. However, the determination of thenetwork architectureis not a trivial task, for example, the number of neurons in hidden layers and theiractivation ...
Moreover, from a runtime viewpoint, although our model might necessitate a longer duration during training, owing to its optimization techniques and structural design, its inference speed in practical applications is comparable to, if not faster than, the baseline models. Figure 6 Comparing ...