Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Yağanoğlu, Mete; Köse, Cemal

doi:10.3390/electronics7040050

Cited by 28 publications

(21 citation statements)

References 42 publications

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“…The confusion matrix is the summary of the educated network and it shows the performance of the network. A general confusion matrix is shown in Table 1 [31]. Intelligent algorithms focused on ML can provide effective responses and enhancements to the COVID-19 pandemic, while contributing to the development of various models, widely used in clinics around the world.…”

Section: Performance Analysismentioning

confidence: 99%

Determination of Covid-19 Possible Cases by Using Deep Learning Techniques

Oğuz

Yağanoğlu

2021

Sakarya University Journal of Science

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A large number of cases have been identified in the world with the emergence of COVID-19 and the rapid spread of the virus. Thousands of people have died due to COVID-19. This very spreading virus may result in serious consequnces including pneumonia, kidney failure acute respiratory infection. It can even cause death in severe cases. Therefore, early diagnosis is vital. Due to the limited number of COVID-19 test kits, one of the first diagnostic techniques in suspected COVID-19 patients is to have Thorax Computed Tomography (CT) applied to individuals with suspected COVID-19 cases when it is not possible to administer these test kits. In this study, it was aimed to analyze the CT images automatically and to direct probable COVID-19 cases to PCR test quickly in order to make quick controls and ease the burden of healthcare workers. ResNet-50 and Alexnet deep learning techniques were used in the extraction of deep features. Their performance was measured using Support Vector Machines (SVM), Nearest neighbor algorithm (KNN), Linear Discrimination Analysis (LDA), Decisio n trees, Random forest (RF) and Naive Bayes methods as the methods of classification. The best results were obtained with ResNet-50 and SVM classification methods. The success rate was found as 95.18%.

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Section: Performance Analysismentioning

confidence: 99%

Determination of Covid-19 Possible Cases by Using Deep Learning Techniques

Oğuz

Yağanoğlu

2021

Sakarya University Journal of Science

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“…The performance is determined by testing the input dataset which is given by the user. The below table shows the predicted and the actual values (Yağanoğlu and Köse, 2018). TP-The forecasted value is positive, and it is right TN-The forecasted value is negative, and it is right FP -The forecasted value is positive, and it is wrong FN-The forecasted value is negative, and it is wrong…”

Section: Evaluation Measurementioning

confidence: 99%

Makine Öğrenimi Sınıflandırma Algoritmalarını Kullanarak Diyabet Tahmini

NAHZAT¹,

Yağanoğlu²

2021

European Journal of Science and Technology

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Artificial intelligence's use in health systems has evolved substantially in recent years. In medical diagnosis, machine learning (ML) has a wide variety of uses. Machine learning techniques are used to forecast or diagnose a variety of life-threatening illnesses, including cancer, diabetes, heart disease, thyroid disease, and so on. Chronic diabetes is one of the most common diseases worldwide and making the diagnosis process simpler and quicker would have a huge effect on the treatment process. The fundamental goal of this work is to prepare and carry out diabetes prediction using various machine learning techniques and Conduct output analysis of those techniques to find the best classifier with the highest accuracy. This study examines diabetes prediction by taking different diabetes disease-related attributes. We use the Pima Indian Diabetes Dataset and applied the Machine Learning classification methods like K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) for diabetes prediction. The models used in this analysis have various degrees of accuracy. This study shows a model that can correctly forecast diabetes. In comparison to other machine learning methods, the random forest has high accuracy in forecasting diabetes, according to the findings of this study.

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“…Yaganoglu et al [12] provided an example of how to develop wearable systems to support disabled people by using a combination of state-of-the-art low-cost electronic systems and signal processing methods. Specifically, such a wearable device helped hearing-impaired people to detect and classify specific ambient sounds in real-time, e.g., a barking dog, a ringing phone, and a honking sound.…”

Section: The Present Special Issuementioning

confidence: 99%