Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting induction is complicated by its diverse causes. We investigated feasibility developing machine-learning model predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained hypotension, occurring between tracheal intubation incision, using data for period from start...

Hypotensive events in the initial stage of anesthesia can cause serious complications patients after surgery, which could be fatal. In this study, we intended to predict hypotension tracheal intubation using machine learning and deep techniques one minute advance. Meta models, such as random forest, extreme gradient boosting (Xgboost), especially convolutional neural network (CNN) model (DNN), were trained occurring between incision, data from four minutes before intubation. Vital records...

Abstract There are still no definite treatment modalities for interstitial cystitis (IC). Meanwhile, stem cell therapy is rising as potential alternative various chronic diseases. This study aimed to investigate the safety of clinical-grade mesenchymal cells (MSCs) derived from human embryonic (hESCs), code name MR-MC-01 (SNU42-MMSCs), in IC patients. Three female patients with (1) symptom duration >6 months, (2) visual pain analog scale (VAS) ≥4, and (3) one or two Hunner lesions...

With increasing amount of data, the threat malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware such type attacks. In this paper, we design a convolutional neural network tackle detection on files. We collect benign files manually label byte sequences within intensively examine structure input data illustrate how proposed based...

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network gaming services. The widespread adoption such services made possible analysis large-scale archival data containing detailed human interactions, presenting a very promising opportunity understand the rich complex behavior. In collaboration with leading global provider Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present science-based...

Localization of sensor nodes is a key technology in Wireless Sensor Networks(WSNs).Trilateration an important position determination strategy.To further improve the localization accuracy, novel Trilateration based on Point In Triangle testing (TPITL)algorithm proposed paper.Unlike traditional trilateration algorithm which randomly selects three neighbor anchors, TPITL special anchors unknown node for trilateration.The construct smallest anchor triangle encloses node.To choose optimized we...

As the online service industry has continued to grow, illegal activities in world have drastically increased and become more diverse. Most occur continuously because cyber assets, such as game items money games, can be monetized into real currency. The aim of this study is detect bots a massively multiplayer role playing (MMORPG). We observed behavioral characteristics found that they execute repetitive tasks associated with gold farming trading. propose bot detection method based on user...

The prevalence of non-executable malware is on the rise, presenting a major threat to users, including public institutions and corporations. While extensive research has been conducted detecting threats, there noticeable gap in studying document-type compared with executable files. proposed model will solve this by classifying families using script codes, tags, write documents languages execute malicious functions. These codes offer insights into how was constructed operates victim’s system....

Abstract This study investigated the safety and efficacy of MR-MC-01, a mesenchymal stem cell therapy derived from human embryonic cells, in patients with interstitial cystitis (IC), particularly those Hunner lesions unresponsive to pentosan polysulfate sodium (PPS). Conducted as prospective, randomized, double-blind, placebo-controlled phase I/IIa clinical trial, it enrolled 22 patients, six completing I 16 participating IIa. Phase tested 2 doses (2.0 × 107 5.0 cells) determine maximum...

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim our to assist the medical personnel check blood and control amount of insulin doses. Herein, employed algorithm, especially recurrent neural network (RNN), that consists sequence processing layer classification prediction. tested simple RNN, gated unit (GRU), long-short term memory (LSTM) varied architectures determine one with best...

Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it possible to precisely predict a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study real-time model for predicting 3-min-ahead from the start anesthesia surgical incision. We used only vital signs anesthesia-related data obtained during anesthesia-induction phase designed...

End stage renal disease (ESRD) is the last of chronic kidney that requires dialysis or a transplant to survive. Many studies reported higher risk mortality in ESRD patients compared with without ESRD. In this paper, we develop model predict postoperative complications, major cardiac event, for who underwent any type surgery. We compare several widely-used machine learning models through experiments our collected data yellow size 3220, and achieved F1 score 0.797 random forest model. Based on...

Intelligent attacks using document-based malware that exploit vulnerabilities in document viewing software programs or file structure are increasing rapidly. There many cases of PDF (portable format) proportion to its usage. We provide in-depth analysis on and JavaScript content embedded PDFs. Then, we develop the diverse feature set encompassing metadata such as size, version, encoding method keywords, features object names, readable strings JavaScript. When diverse, it is hard adversarial...

This article investigates whether individual users are more likely to display malicious behavior after receiving social reinforcement from friends in their online networks. We analyze the dynamics of game bot diffusion on basis real data supplied by a major massively multiplayer role-playing company. find that reinforcement, measured ratio over total friends, affects likelihood adoption and commitment terms usage time.

One of the major goals Chromosome-Centric Human Proteome Project (C-HPP) is to fill knowledge gaps between human genomic information and corresponding proteomic information. These are due "missing" proteins (MPs)-predicted with insufficient evidence from mass spectrometry (MS), biochemical, structural, or antibody analyses-that currently account for 2579 19587 predicted (neXtProt, 2017-01). We address some lessons learned inconsistent annotations missing in databases (DB) demonstrate a...

User interactions in massively multiplayer online role-playing games (MMORPGs) generate social networks and diffuse user behavior throughout the network. We test diffusion model adoption of a game bot among players connected via goal-oriented communities using real data provided by major MMORPG company. In based on probabilistic process, we used expectation maximization to infer probability usage. The experimental results showed that can explain spread malicious behavior.

Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, study proposes an intelligent ensemble deep learning system to predict BG 15, 30, 60 min prediction horizons (PHs) based on historical collected via continuous monitoring devices as endogenous factor carbohydrate intake insulin administration information (times) exogenous factors. Although there are numerous algorithms available, applied five algorithms, namely,...

This article investigates whether individual users are more likely to display malicious behavior after receiving social reinforcement from friends in their online networks. We analyze the dynamics of game bot diffusion on basis real data supplied by a major massively multiplayer role-playing company. find that reinforcement, measured ratio over total friends, affects likelihood adoption and commitment terms usage time.

As in the real world's criminal investigation, cyber profiling is important to attribute attacks. Every crime committed by same hacker or hacking group has unique characteristics such as attack purpose, methods, and target's profile. Therefore, a complete analysis of hacker's activities can give investigators hard evidence attacks unveil criminals. In this paper, we implemented WHAP, system that uses Case-Based Reasoning (CBR). We verified WHAP's usefulness analyzing large scale web...

We analyze multi-relational social interaction networks in a large-scale commercial Massively Multiplayer Online Role-Playing Game(MMORPG). Our work is based on data from AION, currently the world's second most-played MMORPG with 3.4 million subscribers as of mid 2010, created and serviced by NCSoft, Inc. construct characterize six distinct interactivity (Friend, Private Messaging, Party invitation, Trade, Mail, Shop), each representing diverse player types.

Malware detection of non-executables has recently been drawing much attention because ordinary users are vulnerable to such malware. Hangul Word Processor (HWP) is software for editing non-executable text files and widely used in South Korea. New malware HWP continues appear the circumstances between Korea North There have various studies solve this problem, but most them limited they require a large amount effort define features based on expert knowledge. In study, we designed convolutional...

MMORPG (Massively Multiplayer Online Role-Playing Game) is one of the best platforms to observe human's behaviors. In collaboration with a leading online game company, NCSoft, we can all behaviors in large-scale commercialized MMORPG. Especially, analyzed behavioral differences between bots and human users. We categorized five groups, Bot-Bot, Bot-All, Human-Human, Human-All All-All, characteristics six social interaction networks for each group. As result, found that there are significant human.