Detecting illegal drugs, such as cannabis and methamphetamine, with high accuracy speed is a complex problem that requires an innovative solution. To address this challenge, we propose new method utilizes newly developed electronic nose (e-nose) system unprecedented total of 56 sensors, including four different types: metal-oxide-semiconductor (MOS), electrochemical (EC), non-dispersive infrared (NDIR), photoionization detector (PID). Previous studies on gas sensors have typically validated...

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used measure evaluate such have previously been as indicators for diagnosing mental disorder, major depressive disorder (MDD). In this study, we investigate feasibility of developing an objective is based on examining individuals when they experiencing stress. To perform this, recruited 30 patients with MDD 31 healthy controls. Then, skin conductance (SC) was measured...

Electronic health record (EHR) data are sparse and irregular as they recorded at time intervals, different clinical variables measured each observation point. In this work, to handle multivariate time-series data, we consider the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human knowledge</i> of aspects be measure them in situations, known multi-view features, which indirectly represented data. We propose a scheme realize features...

In this work, we use recurrent neural network (RNN) to predict the medical examination data with missing parts. There often exist parts in due various human factors, for instance, because subjects occasionally miss their annual examinations. Such make it hard future by machines. Thus, imputation of information is needed accurate prediction data. Among types RNNs, choose simple (SRN) and long short-term memory (LSTM) as well data, they show good performance many relevant applications. our...

Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missing values has attracted attention of researchers who have attempted find a better use all available samples for determining solution primary target task through defining secondary imputation problem. Methodologically, existing methods, either...

There are various medical features associated with cardiovascular disease in the EMR data, but frequency of each feature is different. Less frequent may be considered as non-critical feature, although closely risk prediction model. We propose a frequency-aware based Attention-based LSTM (FA-Attn-LSTM) that weighs on important using an attention mechanism considers feature. Our model predicts for ejection fraction target and shows RMSE = 3.65 MAE 2.49.

In this paper, we propose a new approach for managing domain specific thesauri, where object-oriented paradigm is applied to thesaurus construction and query-based browsing. The provides an mechanism assist experts in constructing thesauri; it determines considerable part of relationship degrees between terms by inheritance supplies the expert with information available from other parts being constructed or already constructed. addition that, enables incrementally construct thesaurus, since...

In this work, we use two different types of recurrent neural networks (RNNs) to predict medical examination results a subject given the previous measurements. The first one is simple network (SRN) which models temporal trajectories data sequence infer unknown future observation, and second long short-term memory (LSTM) that enables modeling longer by exploiting forgetting switches. non-linear, evolution status human subjects are approximated RNNs, prediction measurement becomes more accurate...

Coronary Artery Calcification (CAC) score is one of the most important measures in determining degree cardiovascular disease. It time-consuming to do this manually or semi-automatically, so automatic CAC scoring methods are being studied. Most classify calcified pixels(2D) voxels(2.5D 3D) and calculate score. We present a new voxel classification model with multi-scale CNN architecture which can reflect advantages large receptive small CNN. This study used cardiac CT dataset 98 patients from...

According as the protein-protein interaction (PPI) data more increase, we need to optimally visualize them network, in that describing relationship among proteins is able easily analyze biological processes happened a cell. In this paper, fast layout large-scale PPI networks, proposed method taking hub-proteins into consideration, which have interactions than any other network. words, it enforces two core parts of Walshaw's multilevel force-directed placement algorithm (MLFDP) be modified....

본 논문에서는 마비/편마비 환자의 손 재활운동을 위한 증강현실 인터랙션을 통한 재활운동시스템을 제안한다. 주로 기계적 장치에 의존하고 있는 기존의 마비환자 재활운동시스템에서 가정에서 손쉽게 재활훈련을 수행할 수 있도록 시스템을 구성하기위하여 컴퓨터 비전 기법을 이용하여 재활훈련에 필요한 장비를 최소화하고 좀 더 간편하게 설치하여 사용할 하는데 초점을 맞추었다. 논문에서 제안된 방법은 손끝의 움직임과 상태를 손끝마커의 위치와 접촉여부를 검사함으로써 인터랙션 점검한다. 한대의 카메라로부터 입력되는 손끝 마커의 2차원 위치는 3차원 객체와의 위하여 ARToolKit 마커를 기반으로 보정된 카메라 공간상의 좌표로 변환되어 사용된다. 좌표계로 변환과정을 거친 인터랙션에 반영함으로써 기반의 구현하였다. 제시한 기법의 구현내용을 실험결과에서 나타내었고, 기반 테이블탑 환경에서 마비환자의 재활운동에 활용될 있음을 나타내었다. This paper presents an augmented...

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing DTR learning studies are mainly based on offline reinforcement (RL) approaches working electronic healthcare records data. However, trained policy may choose different human clinician's prescription. Furthermore, most them do not consider: 1) heterogeneity sepsis; 2) short-term...

Sepsis is one of the most life-threatening medical conditions. Therefore, many clinical trials have been conducted to identify optimal treatment strategies for sepsis. However, finding reliable remains challenging due limited-scale tests. Here we tried extract sepsis policy from accumulated records. In this study, with our modified deep reinforcement learning algorithm, stably generated a patient artificial intelligence model. As training data, 16,744 distinct admissions in tertiary...

The drawback of conventional content-based video retrieval systems is that they cannot retrieve shots semantically related with the user's intention. One reasons for this extracting semantics from visual features and spatial structures streams extremely difficult. To remedy drawback, we propose a new model based on fuzzy triples. In model, triples are used indexing formulating queries. Moreover, also to express set active rules where spatio-temporal encoded. define generic patterns in by...

This paper proposes a method of predicting future medical examination measurements given the past values. The examinations considered in this are blood sugar level, low and high pressures, cholesterol level. uses specific type artificial neural networks, radial-basis function network (RBFN), to approximate mapping from that upcoming year, order help subjects be aware signs unusual health states without consulting with doctors. Experimental results show RBFN-based estimation is superior...

Gas classification is a machine learning problem that important for various applications including monitoring systems, health care, public security, etc. Since measuring the characteristic of gas molecules greatly affected by external factors such as wind speed and internal setting detecting sensors, should be done taking into account combination these individual factors, which we call <i>condition</i> in this paper. In particular, when classifying data measured under multiple conditions,...

This paper presents an architecture for accelerating CORDIC vectoring mode operations. The processing is sped up by overlapping redundant sum formation and selection of rotation direction. We analyze the latency time area, compare them with a conventional implementation. results show that proposed scheme reduces not only but also overall computation time. Thus, it achieves higher throughput in pipelining.

In this paper, we propose a semantic inheritance/inverse-inheritance mechanism for systematic bio-ontology construction. This allows domain experts to easily manage sophisticated bio-ontologies in which biological knowledge is encoded; it automatically captures semantics inferred from the ontology structure being constructed or already constructed. Based on captured suggests appropriate recommendation experts. While inheritance enables them consistently determine of relationships between...

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression smoothing method, by which information who approaches to can he accurately output even in unanticipated condition using passive infrared VIR) sensor ultrasonic device. The developed system consists transmitter, gateway. Each module communicates with each other through RF (Radio frequency) communication. includes receiver PIR for motion detection. In particular,...

In this work, the recurrent neural networks (RNNs) for medical examination data prediction with missing information are proposed. Simple network (SRN), long short-term memory (LSTM) and gated unit (GRU) selected among many variations of RNNs imputation while they also used to predict future data. Besides, based on bidirectional LSTM is proposed consider past as well in process, traditional can only during imputation. We implemented results experiment using database Koreans. The experimental...

Automatic target detection and recognition (ATD/R) is of crucial interest to the defense community. We present a robust ATD/R system developed at CVRC UT-Austin for in second generation forward looking infrared (FLIR) images. An experiment conducted on 1930 FLIR images shows that this ATR can achieve with high degree accuracy low false alarm rate. This demo first presents brief overview whole methodology, then detailed procedures temporary outputs step by step, running typical low-contrast...

An architecture for sum-of-products computation using conventional carry-save-adder arrays including even/odd CSA array is regular, however, it results in awkward inter-layer interconnections and elongated interconnection length. We introduce a "weight-sorting" which minimizes the complexity. This can be designed systematic way laid out regularly VLSI circuit narrow cells. A comparison made between three at an architectural level. The design of 8/spl times/8 sum-of-two-products computer...