Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
In this paper, we display the definition and arrangement of the beginning esteem issue in composite materials for warm condition. The issue includes finding the starting temperature conveyance when as it were the temperature spreading at time t=T>0 is given. Typically, a challenging issue since it has a place to a course of numerically unsteady issues that are ill-posed. To characterize this issue, we have to be present work spaces and unravel the coordinate issue to decide them. The method of division of factors is commonly utilized to fathom the coordinate issue, but it isn't reasonable for the due to the expansive blunders and disparate arrangement it produces. Ivanov V.K. proposed a strategy to get a steady inexact arrangement by supplanting the coming about arrangement with a fractional whole that depends on δ, N=N(δ). Another approach is the Picard strategy that employments a family of administrators to map the space into itself and get a regularized inexact arrangement. We show the comes about of computational tests and assess the viability of the Picard strategy.
Read MoreDoi: https://doi.org/10.54216/JAIM.060101
Vol. 6 Issue. 1 PP. 08-17, (2023)
Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.
Read MoreDoi: https://doi.org/10.54216/JAIM.060102
Vol. 6 Issue. 1 PP. 18-26, (2023)
The subset of manufactured insights (AI) known as machine learning starts in design acknowledgment, where information can be organized for human comprehension. For a long time, various applications utilizing machine learning have been created in healthcare, fund, military gear, and space investigation; presently, machine learning is a zone that's extending and progressing quickly. It utilizes information to optimize computer execution. AI is vital in combating modern coronaviruses in 2019 (COVID-19) -related matters and is used additionally in computer-assisted blend-making plans. Computer programs' settings are optimized based on preparing information or past encounters. It can moreover make future forecasts utilizing the information. With the assistance of machine learning, we are creating a numerical demonstration based on the data's measurements. Numerous illustrations outline the viability of machine learning and counterfeit insights in this field. Counterfeit insights strategies can improve the consistency of forecasts and choices by making valuable calculations. AI is useful not for foreseeing people with COVID-19 but for assessing general wellbeing. It can screen the COVID-19 episode at different levels; in our paper, we use three machine learning calculations to analyze and predict. The leading precision was in XGP= 99%, but SVM and RF gave great precision at 98%.
Read MoreDoi: https://doi.org/10.54216/JAIM.060103
Vol. 6 Issue. 1 PP. 27-34, (2023)
In modern times, a disease known as COVID-19 that is highly contagious is continuing to have a profoundly negative influence on the people of the entire world. The fundamental purpose of the model that has been proposed is to improve its predictive capabilities while also providing an effective model for predicting COVID-19 that has a robust diagnostic. Image scaling and noise reduction are two examples of the types of pre-processing techniques that are used at the very first step. The adoption of picture scaling and median filtering techniques, both of which work to enhance the quality of the input data in preparation for further processing steps, allows this goal to be accomplished. Several distinct data augmentation strategies, like flipping and rotation, are utilized to improve the model's performance on a limited dataset and assist it in better comprehending the differences present in the training data. In this article, we will provide a unique Optimized Architecture for COVID-19 Prediction (OACP) model to classify COVID-19 situations as either positive or negative effectively. Using CXR pictures, this novel method, based on a tunable deep learning technique called DenseNet, may predict the presence of COVID-19-positive patients. Based on the findings, it was determined that the proposed model utilized achieved better outcomes, with an accuracy of 98%.
Read MoreDoi: https://doi.org/10.54216/JAIM.060104
Vol. 6 Issue. 1 PP. 35-47, (2023)
Managing the increasing number of patients requiring first screening can be significantly aided by real-time automated detection of COVID-19. It's feasible that Deep CNN models that have been trained on sufficiently large datasets will emerge as the most promising options for achieving the goal. This study aims to automatically detect and classify COVID-19 and viral pneumonia infections in chest X-ray images using a deep CNN model. Our proposed model relies on multiclass labeling to accomplish our aims. Design and Organization: Using the ImageNet pre-trained weights, the proposed model is built on top of the VGG16 framework. Additional custom layers were used to fine-tune the model and produce a better overall performance that is more specific to the goal. In terms of its subjects and methods, this study uses 15,153 samples in total. There are X-rays of the lungs from patients with COVID-19, those with viral pneumonia, and healthy volunteers. The entire dataset was split into an 80:20 split for the train and test sets before the model was trained. Image preprocessing and augmentation were used to enhance crucial parts of the photos before they were sent to the model in batches. We measure the model's efficacy with accuracy, precision, recall, and the F1 score. The analysis that was performed statistically was. The model's output is compared to the results of other recent research that have set the standard in the field. The proposed model has a 98% accuracy in multiclass classification on the test dataset, as measured by 98% precision, 96% recall, and 97% F1 score. Receiver operating characteristic curve area scores of 0.99 were achieved in all three multiclass classification situations. Finally, the proposed categorization model may show to be highly useful in the first diagnosis of COVID-19 and viral pneumonia patients, especially when dealing with heavy workloads and large volumes.
Read MoreDoi: https://doi.org/10.54216/JAIM.060105
Vol. 6 Issue. 1 PP. 48-55, (2023)