A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

AL‐Dhief, Fahad Taha; Latiff, N. M. Abdul; Malik, Nik Noordini Nik Abd; Salim, Naseer Sabri; Baki, Marina Mat; Albadr, Musatafa Abbas Abbood; Mohammed, Mazin Abed

doi:10.1109/access.2020.2984925

Cited by 101 publications

(66 citation statements)

References 104 publications

(90 reference statements)

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“…Developing a strong feature detection method for voice pathology processing based on the deep Learning (33). In order to obtain better performance for a transparent, continuous, and ubiquitous system, the integration of cloud technology with the Internet of Things is necessary in the healthcare industry; IoT has several uses, one of which is Speech Pathology Control (34). Machine learning (ML) has been one of the smart techniques capable of predicting the situation with fair precision depending on the knowledge and learning process automatically.…”

Section: Related Workmentioning

confidence: 99%

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.

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Section: Related Workmentioning

confidence: 99%

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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“…We utilized a variety of assessment measures that include accuracy, precision, f1score, recall, and execution time to evaluate the performance of the proposed algorithms during the identification and classification of conflict flows in terms of efficiency and effectiveness. These evaluation measurements are computed as shown in Equations (10)(11)(12)(13)(14).…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

et al. 2021

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Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.

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“…Table 6 presents the evaluation results of the NN through in all experiments. Additionally, ROC analysis of the NN for the highest result is presented in Fig 18. The NN is regarded as a state-of-the-art technique, and many researchers have used it in health care domains, including COVID-19 detection using chest X-ray images [8,[44][45][46][47]. Therefore, this study compared the proposed approaches of OGA-ELM (random, K-tournament, and roulette wheel) with the NN approach to evaluate the performance of OGA-ELM (random, K-tournament, and roulette wheel).…”

Section: Plos Onementioning

confidence: 99%

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

et al. 2020

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The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

Cited by 101 publications

References 104 publications

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

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