Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia

Enbeyle, Wegayehu; Hamad, Àbdulsattar Abdullah; Al-Obeidi, Ahmed S.; Abebaw, Solomon; Gelaw, Assaye Belay; Markos, Admasu; Adulo, Lema Abate; Derebew, Bizuwork

doi:10.1155/2022/3294954

Cited by 22 publications

(9 citation statements)

References 18 publications

(19 reference statements)

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“…In the ROC curve, the criteria generally used in the evaluation are sensitivity, general accuracy, specificity, positive predictive value, and negative predictive value. By using Figure 4 , the limit values of the tests for these criteria can be determined [ 23 – 28 ]. Sensitivity (%): It shows what percentage of people known to have the disease can be diagnosed with the recommended method.…”

Section: Methodsmentioning

confidence: 99%

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Badr¹,

Al-Otaibi

Alturki

et al. 2022

Computational Intelligence and Neuroscience

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The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

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Section: Methodsmentioning

confidence: 99%

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Badr¹,

Al-Otaibi

Alturki

et al. 2022

Computational Intelligence and Neuroscience

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“…To implement data augmentation, a function was defined in such a way that it would return the instance to the ImageDataGenerator class configured to perform transformations on the data. Taking into account previous articles that analyzed chest X-ray images [10,[17][18][19][20][21], the following transformations were defined: (i) A random rotation within the range of -5/+5 degrees (ii) A random horizontal and vertical shift of 5% (iii) A random increase range of 15% (iv) A random distortion along an axis of 0.1 degrees creates or rectifies the angles of perception (v) A padding to keep the size of the input images constant, mirroring neighboring pixels to fill in the gaps with missing pixels…”

Section: Model Validationmentioning

confidence: 99%

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

Badr¹,

Al-Otaibi

Alturki

et al. 2022

BioMed Research International

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X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks’ knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

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“…In our case, only 20 epochs have been made for the initial model and 30 for the optimized model. This difference in 9 BioMed Research International epochs is due to the lack of adequate equipment to make a model that compiles quickly and efficiently, saving time for the researcher [23][24][25][26].…”

Section: Web Applicationmentioning

confidence: 99%

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

Alshamrani

Asiri

et al. 2022

BioMed Research International

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Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X-rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model’s performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep-learning models can be created to assist health professionals in the early detection of pneumonia.

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Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia

Cited by 22 publications

References 18 publications

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

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