A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.

Detecting the presence of a disease requires laboratory tests, testing kits, and devices; however, these were not always available on hand. This study proposes a new approach in disease detection using machine learning algorithms by analyzing symptoms experienced by a person without requiring laboratory tests. Six supervised machine learning algorithms such as J48 decision tree, random forest, support vector machine, k-nearest neighbors, naïve Bayes algorithms, and artificial neural networks were applied in the “COVID-19 Symptoms and Presence Dataset” from Kaggle. Through hyperparameter optimization and 10-fold cross validation, we attained the highest possible performance of each algorithm. A comparative analysis was performed according to accuracy, sensitivity, specificity, and area under the ROC curve. Results show that random forest, support vector machine, k-nearest neighbors, and artificial neural networks outweighed other algorithms by attaining 98.84% accuracy, 100% sensitivity, 98.79% specificity, and 98.84% area under the ROC curve. Finally, we developed a web application that will allow users to select symptoms currently being experienced, and use it to predict the presence of COVID-19 through the developed prediction model. Based on this mechanism, the proposed method can effectively predict the presence or absence of COVID-19 in a person immediately without using laboratory tests, kits, and devices in a real-time manner.

With the increasing popularity of Twitter as both a social media platform and a data source for companies, decision makers, advertisers, and even researchers alike, data have been so massive that manual labeling is no longer feasible. This research uses a semi-supervised approach to sentiment analysis of both English and Tagalog tweets using a base classifier. In this study involving the Philippines, where social media played a central role in the campaign of both candidates, the tweets during the widely contested race between the son of the Philippines’ former President and Dictator, and the outgoing Vice President of the Philippines were used. Using Natural Language Processing techniques, these tweets were annotated, processed, and trained to classify both English and Tagalog tweets into three polarities: positive, neutral, and negative. Through the Self-Training with Multinomial Naïve Bayes as base classifier with 30% unlabeled data, the results yielded an accuracy of 84.83%, which outweighs other studies using Twitter data from the Philippines.

Object detection has received a lot of research attention in recent years because of its close association with video analysis and image interpretation. Detecting objects in images and videos is a fundamental task and considered as one of the most difficult problems in computer vision. Many machine learning and deep learning models have been proposed in the past to solve this issue. In the current scenario, the detection algorithm must calculate from beginning to end in the shortest amount of time possible. This paper proposes a method called GradCAM-MLRCNN that combines Gradient-weighted Class Activation Mapping++ (Grad-CAM++) for localization and Mask Regional Convolution Neural Network (Mask R-CNN) for object detection along with machine learning algorithms. In our proposed method, images are used to train the network, together with masks that shows where the objects are in the image. A bounding box is regressed around the region of interest in most localization networks. Furthermore, just like any classification task, the multi-class log loss is minimized during training. This model enhances the calculation time and speed, as well as the efficiency, which recognizes objects in images accurately by comparing state-of-the-art machine learning algorithms, such as decision tree, Gaussian algorithm, k-means clustering, k-nearest neighbor, and logistic regression. Among these methods, we found logistic regression performed well with an accuracy rate of 98.4%, recall rate of 99.6%, and precision rate of 97.3% with respect to ResNet 152 and VGG 19. Furthermore, we proved the goodness of fit of our proposed model using chi-square statistical method and demonstrated that our solution can achieve great precision while maintaining a fair recall level.

One of the fundamental advancements in the deployment of object detectors in real-time applications is to improve object recognition against obstruction, obscurity, and noises in images. In addition, object detection is a challenging task since it needs the correct detection of objects from images. Semantic segmentation and localization are an important module to recognizing an object in an image. The object localization method (Grad-CAM++) is mostly used by researchers for object localization, which uses the gradient with a convolution layer to build a localization map for important regions on the image. This paper proposes a method called Combined Grad-CAM++ with the Mask Regional Convolution Neural Network (GC-MRCNN) in order to detect objects in the image and also localization. The major advantage of proposed method is that they outperform all the counterpart methods in the domain and can also be used in unsupervised environments. The proposed detector based on GC-MRCNN provides a robust and feasible ability in detecting and classifying objects exist and their shapes in real time. It is found that the proposed method is able to perform highly effectively and efficiently in a wide range of images and provides higher resolution visual representation than existing methods (Grad-CAM, Grad-CAM++), which was proven by comparing various algorithms.

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