Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts with epilepsy patients, with a few of them admitting to experiencing suicidal ideation in the past (32 suicidal and 77 control). The selected methods detect suicidal ideation with an average area under the curve (AUC) score of 95% on the merged collection with high suicidal ideation, and the trained models generalize over the third collection with an average AUC score of 69%. Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model. We also observe that a logistic classifier’s performance was comparable with the deep learning method while promising explainability

State-of-Mind Classification From Unstructured Texts Using Statistical Features and Lexical Network Features

https://scholarlycommons.pacific.edu/jmd/1287/thumbnail.jp

Mobil robotlar için insan hareket analizi.

In this thesis, a study of human action recognition with an application on mobile robots is conducted. Movements of the robot depend on human actions estimated after human detection, tracking and action recognition. Therefore, the use of appropriate algorithms for these tasks is proposed. Moving regions are detected by optical flow vectors determined from consecutive frames. People are detected by human recognition and tracking. Each person is assigned a different id and the coordinates of his/her location at each frame are collected. Then, extracted action recognition features are separated using the coordinates belonging to each person so that action recognition is conducted for each person separately. Furthermore, to make a distinction between action cycles, the idea of performing recognition in short frame sequences is proposed. Two feature extraction methods are selected and compared for action recognition: cuboids and tracklets. Based on the obtained results, the advantages of tracklets over cuboids are proven. Then, a literature search is conducted for classification of the tracklet features. The codebook based method, which is the most popularly used method in the action recognition literature, is experimented. Weaknesses of the codebook based method are shown for classification of features extracted from short frame sequences. Thus, a novel classification method is proposed based on the idea of iterative matching. Success of the proposed method over the codebook method is proven with various tests in terms of accuracy and computational time consumption. With the final design of tracking and action recognition system, the mobile robot, experimented on Pioneer2, is commanded instructions: move forward, turn right or left, give alarm to warn authorities.M.S. - Master of Scienc

Comparison of Cuboid and Tracklet Features for Action Recognition on Surveillance Videos

For recognition of human actions in surveillance videos, action recognition methods in literature are analyzed and coherent feature extraction methods that are promising for success in such videos are identified. Based on local methods, most popular two feature extraction methods (Dollar's "cuboid" feature definition and Raptis and Soatto's "tracklet" feature definition) are tested and compared. Both methods were classified by different methods in their original applications. In order to obtain a more fair comparison both methods are classified by using the same classification method. In addition, as it is more realistic for recognition of real videos, two most popular datasets KTH and Weizmann are classified by splitting method. According to the test results, convenience of tracklet features over other methods for action recognition in real surveillance videos is proven to be successful

Evaluation of textural features for multispectral images

Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied

Characterizing Data Dependence Constraints for Dynamic Reliability Using n-Queens Attack Domains

As data centers attempt to cope with the exponential growth of data, new techniques for intelligent, software-defined data centers (SDDC) are being developed to confront the scale and pace of changing resources and requirements. For cost-constrained environments, like those increasingly present in scientific research labs, SDDCs also may provide better reliability and performability with no additional hardware through the use of dynamic syndrome allocation. To do so, the middleware layers of SDDCs must be able to calculate and account for complex dependence relationships to determine an optimal data layout. This challenge is exacerbated by the growth of constraints on the dependence problem when available resources are both large (due to a higher number of syndromes that can be stored) and small (due to the lack of available space for syndrome allocation). We present a quantitative method for characterizing these challenges using an analysis of attack domains for high-dimension variants of the $n$-queens problem that enables performable solutions via the SMT solver Z3. We demonstrate correctness of our technique, and provide experimental evidence of its efficacy; our implementation is publicly available.This article is published as Rozier, Eric W.D., Kristin Y. Rozier, and Ulya Bayram. "Characterizing Data Dependence Constraints for Dynamic Reliability Using n-Queens Attack Domains." Leibniz Transactions on Embedded Systems 4, no. 1 (2017): 05:01-05:26. DOI: 10.4230/LITES-v004-i001-a005. Posted with permission.</p

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

Abstract Background Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. Results The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models’ ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. Conclusions The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed

A FRAMEWORK FOR DETECTING COMPLEX EVENTS IN SURVEILLANCE VIDEOS

This paper presents a framework for detecting complex events in surveillance videos. Moving objects in the foreground are detected in the object detection component of the system. Whether these foregrounds are human or not is decided in the object recognition component. Then each detected object is tracked and labeled in the object tracking component, in which true labeling of objects in the occlusion situation is also provided. The extracted information is fed to the event detection component. Rule based event models are created and trained using Markov Logic Networks (MLNs) so that each rule is given a weight. Events are inferred using MLNs where the assigned weights are used to determine whether an event occurs or not. The proposed system can be applied to detect many complex events simultaneously. In this paper, detection of left object event is discussed and evaluated using PETS-2006, CANTATA and our dataset