The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.
The goal of the work is to enhance existing financial market forecasting frameworks by including an additional factor–in this example, a collection of carefully chosen tweets—into a long-short repetitive neural channel. In order to produce attributes for such a forecast, this research used a unique attitude analysis approach that combined psychological labelling and a valence rating that represented the strength of the sentiment. Both lexicons produced extra properties such 2-level polarization, 3-level polarization, gross reactivity, as well as total valence. The emotional polarity explicitly marked into the database contrasted well with outcomes of the innovative lexicon approach. Plotting the outcomes of each of these concepts against actual market rates of the equities examined has been the concluding step in this analysis. Root Mean Square Error (RMSE), preciseness, as well as Mean Absolute Percentage Error (MAPE) were used to evaluate the results. Across most instances of market forecasting, attaching an additional factor has been proven to reduce the RMSE and increase the precision of forecasts over lengthy sequences.
Chronic Obstructive Lung Disease (COLD) is a continual inflammatory lung disorder that causes blocked airflow from the lungs. It is also called Chronic Obstructive Pulmonary Disease (COPD.) It is responsible for a variety of different conditions such as chronic bronchitis (breathing passages in the lungs) and emphysema (air sacs in the lungs). The right diagnosis of sickness is one of the foremost important complications in medicine. Hence, computerized analysis of diagnosing COPD has been developed to decrease the time and reduce the demise rate. A classification algorithm is one of the computerized techniques which is utilized for the diagnosis of COPD. In this research work, we proposed a hybrid algorithm called ODWDT (Outlier Detection with Decision Tree) algorithm. It comprises two phases; in the first phase, the thoracic data set is preprocessed using Inter Quartile Range (IQR). In the second phase, the COPD patients are diagnosed and classified by using the J48 decision tree classifier. The thoracic surgery dataset can be used to assess samples that might be risk factors for lung cancer which is taken from the UCI Machine learning repository. Experimental results showed that the proposed method achieved 95% classification accuracyin diagnosing chronic obstructive lung disease patients. The outcome of this research work is to diagnose patients with better classification accuracy so that we can be planned for the next treatment. Results proved that the proposed ODWDT algorithm performs better in the diagnosis of COPD.
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.
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