Hypertension is a health issue whose late diagnosis could lead to renal, cerebral, and cardiac events. In this work, it is proposed to use the wavelet scattering transform (WST) as a feature extraction technique applying classical machine learning techniques using photoplethysmography (PPG) signals as input to detect elevated blood pressure and compare its performance with transfer learning applied through fine-tuned convolutional neural networks. The results show that the features obtained by applying the WST and training a logistic regression and support vector machine produced similar results in terms of accuracy compared to fine-tuned convolutional neural networks, with the advantage that the WST could be used to generate a white-box model, which is better suited for a potential medical diagnosis application. 

A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection

Hypertension is a health issue whose late diagnosis could lead to renal, cerebral, and cardiac events. In this work, it is proposed to use the wavelet scattering transform (WST) as a feature extraction technique applying classical machine learning techniques using photoplethysmography (PPG) signals as input to detect elevated blood pressure and compare its performance with transfer learning applied through fine-tuned convolutional neural networks. The results show that the features obtained by applying the WST and training a logistic regression and support vector machine produced similar results in terms of accuracy compared to fine-tuned convolutional neural networks, with the advantage that the WST could be used to generate a white-box model, which is better suited for a potential medical diagnosis application.

Hipertensi merupakan faktor utama dalam perkembangan penyakit seperti stroke, gagal jantung, infark miokard, fibrilasi atrium, penyakit arteri perifer, dan diseksi aorta. Prediksi dini jenis hipertensi dari riwayat kesehatan merupakan hal yang penting agar kita dapat mengetahui penyakit yang disebabkan olehnya. Prediksi ini dapat diperoleh dengan memanfaatkan teknologi machine learning untuk menemukan pengetahuan baru dari data dasar sehingga menemukan pola yang valid, berguna, dan mudah dipelajari. Model klasifikasi neural network diusulkan dalam penelitian ini. Kontribusi kami dalam penelitian ini adalah membuat model klasifikasi neural network. Kami melihat peneliti sebelumnya hanya mengejar nilai akurasi yang tinggi semata. Berbeda dengan penelitian sebelumnya, kami menggunakan teknik optimasi hyperparameter gridsearch cv pada model klasifikasi artificial neural network. Parameter yang digunakan dalam model ini yaitu solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1. Nilai akurasi ketepatan prediksi dalam menentukan jenis hipertensi ini sebesar 85% yang menunjukan bahwa model yang dibangun tenyata sudah cukup baik dalam proses klasifikasi

/pdf/model-prediksi-otomatis-jenis-penyakit-hipertensi-dengan-3bt8yx69.pdf

Model Prediksi Otomatis Jenis Penyakit Hipertensi dengan Pemanfaatan Algoritma Machine Learning Artificial Neural Network

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10041115.pdf

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions. 

A machine learning approach for hypertension detection based on photoplethysmography and clinical data

Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.

/pdf/a-review-of-road-surface-anomaly-detection-and-2ow2as5f.pdf

E. Martinez-Ríos

Papers

A machine learning approach for hypertension detection based on photoplethysmography and clinical data

A Review of Road Surface Anomaly Detection and Classification Systems Based on Vibration-Based Techniques

A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection

Generalized Morse Wavelets parameter selection and transfer learning for pavement transverse cracking detection

A holistic educational platform for the study of the smart grid