Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM

Comparing clothing-mounted sensors with wearable sensors for movement analysis and activity classification

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing

Neural activity classification with machine learning models trained on interspike interval series data

The flow of information through the brain is reflected by the activity patterns of neural cells. Indeed, these firing patterns are widely used as input data to predictive models that relate stimuli and animal behavior to the activity of a population of neurons. However, relatively little attention was paid to single neuron spike trains as predictors of cell or network properties in the brain. In this work, we introduce an approach to neuronal spike train data mining which enables effective classification and clustering of neuron types and network activity states based on single-cell spiking patterns. This approach is centered around applying state-of-the-art time series classification/clustering methods to sequences of interspike intervals recorded from single neurons. We demonstrate good performance of these methods in tasks involving classification of neuron type (e.g. excitatory vs. inhibitory cells) and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep states) on an open-access cortical spiking activity dataset

A low-luminosity type-1 QSO sample; III. Optical spectroscopic properties and activity classification

We report on the optical spectroscopic analysis of a sample of 99 low-luminosity quasi-stellar objects (LLQSOs) at $z\leq 0.06$ base the Hamburg/ESO QSO survey (HES). The LLQSOs presented here offer the possibility of studying the faint end of the QSO population at smaller cosmological distances and, therefore, in greater detail. A small number of our LLQSO present no broad component. Two sources show double broad components, whereas six comply with the classic NLS1 requirements. As expected in NLR of broad line AGNs, the [S{\sc{ii}}]$-$based electron density values range between 100 and 1000 N$_{e}$/cm$^{3}$. Using the optical characteristics of Populations A and B, we find that 50\% of our sources with H$\beta$ broad emission are consistent with the radio-quiet sources definition. The remaining sources could be interpreted as low-luminosity radio-loud quasar. The BPT-based classification renders an AGN/Seyfert activity between 50 to 60\%. For the remaining sources, the possible star burst contribution might control the LINER and HII classification. Finally, we discuss the aperture effect as responsible for the differences found between data sets, although variability in the BLR could play a significant role as well.Comment: 22 pages; 5 tables; 17 figures; in press with A&