The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent

A wearable microwave instrument can detect and monitor traumatic abdominal injuries in a porcine model

Abdominal injury is a frequent cause of death for trauma patients, and early recognition is essential to limit fatalities. There is a need for a wearable sensor system for prehospital settings that can detect and monitor bleeding in the abdomen (hemoperitoneum). This study evaluates the potential for microwave technology to fill that gap. A simple prototype of a wearable microwave sensor was constructed using eight antennas. A realistic porcine model of hemoperitoneum was developed using anesthetized pigs. Ten animals were measured at healthy state and at two sizes of bleeding. Statistical tests and a machine learning method were used to evaluate blood detection sensitivity. All subjects presented similar changes due to accumulation of blood, which dampened the microwave signal (p<0.05). The machine learning analysis yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, showing 100% sensitivity at 90% specificity. Large inter-individual variability of the healthy state signal complicated differentiation of bleedings from healthy state. A wearable microwave instrument has potential for accurate detection and monitoring of hemoperitoneum, with automated analysis making the instrument easy-to-use. Future hardware development is necessary to suppress measurement system variability and enable detection of smaller bleedings.publishedVersio

Machine Learning for Water Quality Assessment Based on Macrophyte Presence

This is the final version. Available on open access from MDPI via the DOI in this recordThe ecological state of the Danube River, as the world’s most international river basin, will always be the focus of scientists in the field of ecology and environmental engineering. The concentration of orthophosphate anions in the river is one of the main indicators of the ecological state, i.e., water quality and level of eutrophication. The sedentary nature and ability to survive in river sections, combined with the presence of high levels of orthophosphate anions, make macrophytes an appropriate biological parameter for in situ prediction of in-river monitoring processes. However, a preliminary literature review identified a lack of comprehensive analysis that can enable the prediction of the ecological state of rivers using biological parameters as the input to machine learning (ML) techniques. This work focuses on comparing eight state-of-the-art ML classification models developed for this task. The data were collected at 68 sampling sites on both river sides. The predictive models use macrophyte presence scores as input variables, and classes of the ecological state of the Danube River based on orthophosphate anions, converted into a binary scale, as outputs. The results of the predictive model comparisons show that support vector machines and tree-based models provided the best prediction capabilities. They are also a low-cost and sustainable solution to assess the ecological state of the rivers

Analisis Keunikan Fitur Cwt Sinyal Eeg Untuk Pembuatan Lima Indikator Pengendalian Kursi Roda BCI

Penelitian ini dilakukan dengan tujuan untuk membuat lima indikator pengendalian kursi roda BCI berdasarkan fitur yang diekstraksi dari sinyal elektroensefalogram (EEG). Sinyal EEG didekomposisi menggunakan metode continuous wavelet transform (CWT). Nilai rata-rata absolut dan standar deviasi dari sinyal yang telah didekomposisi tersebut digunakan sebagai fitur. Fitur hasil ekstraksi kemudian dianalisis keunikannya menggunakan metode Friedman. Untuk mendekati sifat alami fitur sinyal EEG yang nonlinier, metode support vector machine (SVM) dengan kernel radial basis function (RBF) digunakan untuk membuat indikator pengendalian kursi roda BCI berdasarkan fitur sinyal EEG yang paling unik. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan dapat mengukur tingkat keunikan fitur CWT sinyal EEG. Dari penelitian penentuan keunikan fitur CWT dapat diperoleh lima indikator pengendalian untuk kursi roda BCI yang didasarkan pada sinyal EEG dari Neurosky MW001. Akan tetapi, akurasi kelima indikator tersebut belum dapat digunakan sebagai indikator kontrol untuk aktuator kursi roda BCI. Hal ini disebabkan oleh tingkat kepercayaan rata-rata indikator tersebut masih di bawah 60%, sedangkan untuk indikator yang berpasangan masih di bawah 70%