Risk Detection System Research Articles

Predictive analytics for early detection of hospital-acquired complications: An artificial intelligence approach.

Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs. The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia. A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection. All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors. Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance. We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.

Deep Learning Based Fire Risk Detection on Construction Sites.

The recent large-scale fire incidents on construction sites in South Korea have highlighted the need for computer vision technology to detect fire risks before an actual occurrence of fire. This study developed a proactive fire risk detection system by detecting the coexistence of an ignition source (sparks) and a combustible material (urethane foam or Styrofoam) using object detection on images from a surveillance camera. Statistical analysis was carried out on fire incidences on construction sites in South Korea to provide insight into the cause of the large-scale fire incidents. Labeling approaches were discussed to improve the performance of the object detectors for sparks and urethane foams. Detecting ignition sources and combustible materials at a distance was discussed in order to improve the performance for long-distance objects. Two candidate deep learning models, Yolov5 and EfficientDet, were compared in their performance. It was found that Yolov5 showed slightly higher mAP performances: Yolov5 models showed mAPs from 87% to 90% and EfficientDet models showed mAPs from 82% to 87%, depending on the complexity of the model. However, Yolov5 showed distinctive advantages over EfficientDet in terms of easiness and speed of learning.

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.

Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.

Getting Meta: A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messages of Youth

Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption for private conversations will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.

Duty to Respond

Social service providers play a vital role in the developmental outcomes of underprivileged youth as they transition into adulthood. Educators, mental health professionals, juvenile justice officers, and child welfare caseworkers often have first-hand knowledge of the trials uniquely faced by these vulnerable youth and are charged with mitigating harmful risks, such as mental health challenges, child abuse, drug use, and sex trafficking. Yet, less is known about whether or how social service providers assess and mitigate the online risk experiences of youth under their care. Therefore, as part of the National Science Foundation (NSF) I-Corps program, we conducted interviews with 37 social service providers (SSPs) who work with underprivileged youth to determine what (if any) online risks are most concerning to them given their role in youth protection, how they assess or become aware of these online risk experiences, and whether they see value in the possibility of using artificial intelligence (AI) as a potential solution for online risk detection. Overall, online sexual risks (e.g., sexual grooming and abuse) and cyberbullying were the most salient concern across all social service domains, especially when these experiences crossed the boundary between the digital and the physical worlds. Yet, SSPs had to rely heavily on youth self-reports to know whether and when online risks occurred, which required building a trusting relationship with youth; otherwise, SSPs became aware only after a formal investigation had been launched. Therefore, most SSPs found value in the potential for using AI as an early detection system and to monitor youth, but they were concerned that such a solution would not be feasible due to a lack of resources to adequately respond to online incidences, access to the necessary digital trace data (e.g., social media), context, and concerns about violating the trust relationships they built with youth. Thus, such automated risk detection systems should be designed and deployed with caution, as their implementation could cause youth to mistrust adults, thereby limiting the receipt of necessary guidance and support. We add to the bodies of research on adolescent online safety and the benefits and challenges of leveraging algorithmic systems in the public sector.

Intelligent collision risk detection in medium-sized cities of developing countries, using naturalistic driving: A review

Traffic accidents are one of the most serious problems worldwide, being one of the leading causes of death and economic loss in the world. Low-and middle-income countries, mainly their medium-sized cities, are among the most affected by this problem. 93% of traffic accidents occur in low and middle-income countries, even though these countries have approximately 60% of the world's vehicles. This occurs mainly because in these types of countries, especially in medium-sized cities (target context), there are no ideal conditions for driving, such as adequate road infrastructure, good condition of vehicles, and rigorous safety policies. Advanced data analysis techniques including machine learning (ML) have increasingly been used to solve this problem. Naturalistic driving (ND) can be applied as a data collection method that provides information on traffic accidents. ND commonly uses a vehicle's kinematic data to detect high-risk driving behaviors that could cause an accident. The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents, principally using ND; and to propose an intelligent collision risk detection system (ICRDS) for identification of areas with a high probability of TA in the target context. Through the review, it was possible to analyze and evaluate the devices, variables and algorithms that help characterize a risk event in driving, considering the target context. The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable, considering the identified components, with the aim of identifying risk events in driving, and areas of high probability of accidents in the city. • A systematic review of analyses and detection of near-crashes and vehicle data collection. • A comparative list of the best devices for naturalistic driving studies. • A comparative list of Machine Learning algorithms for predicting, classifying or analyzing driving events. • Provide a guidance for future research in naturalistic driving study approaches.

Design, Development and Validation of an Intelligent Collision Risk Detection System to Improve Transportation Safety: The Case of the City of Popayán, Colombia

Several approaches from different perspectives have been used to solve problems with traffic accidents (TA), which mainly affect low- and middle-income countries. Conditions of certain cities, regarding road infrastructure, enforcement of traffic safety regulations, and motor vehicle numbers, influence the increase in TAs. Therefore, medium-sized cities in developing countries (context of interest), which commonly have worrying conditions, are a relevant scenario. One of the approaches to reduce TAs has been the use of data analysis through Machine Learning (ML); however, these techniques require a large amount of data, and medium-sized cities commonly do not have enough. Techniques such as Naturalistic Driving (ND) can be applied as a data collection method. This work proposes an intelligent collision risk detection system (ICDRS) using ND and ML to improve sustainability and safety of transportation in medium-sized cities. The ICRDS design considered the limitations of the context of interest and uses two data collection devices in the vehicle. The ICRDS validation included the design and execution of tests using ND. This validation identified if the collected data in a certain time interval contained high-risk collision events (sudden acceleration, sudden braking, aggressive left or right turn, aggressive left or right lane change). The system implementation results were satisfactory. The developed ML algorithm obtained an average value 0.98 in all the metrics. Two data sets of driving on routes were collected. In addition, the performed tests were able to identify city areas with high accident rates.

Using "Markers of Harm" to Track Risky Gambling in Two Cohorts of Online Sports Bettors.

Online gambling poses novel risks for problem gambling, but also unique opportunities to detect and intervene with at-risk users. A consortium of gambling companies recently committed to using nine behavioral "Markers of Harm'' that can be calculated with online user data to estimate risk for gambling-related harm. The current study evaluates these markers in two independent samples of sports bettors, collected ten years apart. We find over a two-year period that most users never had high enough overall risk scores to indicate that they would have received an intervention. This observation is partly due to characteristics of our samples that are associated with lower risk for gambling-related harm, but might also be due to overly high risk thresholds or flaws in the design of some markers. Users with higher average risk scores had more intraindividual variability in risk scores. Younger age and male gender were not associated with higher average risk scores. The most active users were more likely than other users to have ever exceeded risk thresholds. Several risk scores significantly predicted proxies of gambling-related harm (e.g., account closure). Overall, the current Markers of Harm system has some correctable limitations that future risk detection systems should consider adopting.

ROSE PASSPORT OF A THREAT FOR THE EARLY DETECTION SYSTEM OF THREAT TO THE NATIONAL SECURITY OF UKRAINE

The article considers the problems of identifying threats to national security, in particular in the information sphere, as well as ways to solve them. The experience of leading countries in approaches to the establishment and operation of national risk and threat detection systems, namely the United States, the United Kingdom, the Kingdom of the Netherlands and New Zealand, is analyzed. The significant influence of the peculiarities of the information sphere in the formation of risks and threats of all security spheres is determined. Particular attention is paid to the direction and focus of the monitoring process on the search for certain marker events that will provide a clear idea of the beginning of the formation of risks and identify threats in the early stages. The expediency of development of the Passport of threats to national security of the state for its coordinated use in the system of detection and assessment of threats in all security spheres, in particular for the system of early detection of threats in the information sphere is substantiated. The analysis of theoretical bases of development of passports of threats is carried out. A unified structure of the Passport is proposed, which takes into account the vast majority of aspects of the process of risk formation with the threat to national security. The content of the main and informational aspects of the developed Passport is given. Given the multifunctionality of the Threat Passport, it is noted that the degree of its detail is important for the possibility of its use for the development of a system of early detection of threats in the information sphere of the state. It is determined that the development of the Passport is carried out indefinitely with further adjustment and introduction of new data in connection with the dynamics of the security environment. It is established that for early detection of threats it is necessary to consider the processes of formation and detection of risks with an emphasis on their initial latent stages. This approach combines the description (assessment) and the corresponding threat and its presentation in the information space.

Legal, ethical, and wider implications of suicide risk detection systems in social media platforms.

Suicide remains a problem of public health importance worldwide. Cognizant of the emerging links between social media use and suicide, social media platforms, such as Facebook, have developed automated algorithms to detect suicidal behavior. While seemingly a well-intentioned adjunct to public health, there are several ethical and legal concerns to this approach. For example, the role of consent to use individual data in this manner has only been given cursory attention. Social media users may not even be aware that their social media posts, movements, and Internet searches are being analyzed by non-health professionals, who have the decision-making ability to involve law enforcement upon suspicion of potential self-harm. Failure to obtain such consent presents privacy risks and can lead to exposure and wider potential harms. We argue that Facebook’s practices in this area should be subject to well-established protocols. 1 These should resemble those utilized in the field of human subjects research, which upholds standardized, agreed-upon, and well-recognized ethical practices based on generations of precedent. Prior to collecting sensitive data from social media users, an ethical review process should be carried out. The fiduciary framework seems to resonate with the emergent roles and obligations of social media platforms to accept more responsibility for the content being shared.

An Extended FMEA Model for Exploring the Potential Failure Modes: A Case Study of a Steam Turbine for a Nuclear Power Plant

Critical types of infrastructure are provided by the state to maintain the people’s livelihood, ensure economic development, and systematic government operations. Given the development of ever more complicated critical infrastructure systems, increasing importance is being attached to the protection of the components of this infrastructure to reduce the risk of failure. Power facilities are one of the most important kinds of critical infrastructure. Developing an effective risk detection system to identify potential failure modes (FMs) of power supply equipment is crucial. This study seeks to improve upon prior approaches for risk assessment by proposing a hybrid risk‐assessment model using the concepts of failure mode and effect analysis (FMEA) and multiple‐criteria decision‐making (MCDM). The proposed model includes a cost‐based factor for decision‐makers. The subjectivity and uncertainty in FM assessment are adjusted through the rough number method. The original risk priority number (RPN) can be expanded by including the entropy weights in the risk index. Furthermore, to rank the risk priorities in a rational manner, a modified technique for order preference by similarity to ideal solution (modified TOPSIS) is adopted. The applicability and effectiveness of the proposed method were demonstrated by considering an example of a turbine steam engine in a nuclear power plant.

Application of Expert System with Smartphone-based Certainty Factor Method for Hypertension Risk Detection

Hypertension is a factor health problem with a high prevalence in Indonesia. The total amount of hypertension nationwide is 34.11%. There are only 1/2 were diagnosed, and the remaining 1/2 are undiagnosed. Certainty factor was needed to prove the certain or not of a fact in a metric form, and it commonly was used in expert systems. This method was perfect for diagnosing something uncertain. This research aimed to build a smartphone-based hypertension risk detection system. This system was built by MPX5500DP pressure sensor components that served for blood pressure measurement, Bluetooth module HC-05 as microcontroller data transmission, Arduino Uno as sensor data processing, and Smartphone as hypertension risk detection interface and database access. Measuring blood pressure on the user's right or left arm was used to test the system. Then the data was sent to the smartphone to be classified and processed by using the certainty factor method implanted in the android smartphone to detect the risk of hypertension and then followed by the selection of symptoms and risk factors of hypertension according to previous experience. The results showed that the success rate of the system in detecting the risk of hypertension was 100%, the mean error of the systolic value of the right arm was 2.092%, and the average error of the diastolic value of the right arm was 2.977%, while the average error of the systolic value of the left arm. 1.944% and the mean error of the left arm diastolic value was 2.800%.

Automatic Risk Detection System for Farmer’s Health Monitoring Based on Behavior of Pesticide Use

The Indonesian farmers usually use the agrochemical technology (i.e. pesticide) to prevent or control not only pests but also weeds. Unfortunately, this technique is often applied without adhering to the Occupational Health and Safety (OHS) standards, so that the risks to the pesticide exposure cannot be avoided. Nowadays, the stakeholder has the difficulties for detecting the risks. The current detection system is still performed manually by involving the related experts to assess the farmers’ behavior of pesticide use. This process often spends more times, so that it is impacted on late awareness of the pesticide exposure risk. Due to this, this paper proposed the automatic early detection (ArDeFarm) system with applying Certainty Factor method. This system uses the certainty values acquired from the related experts in order to generate the percentage of the pesticide exposure risk based on the farmers’ behavior in the pesticide mixing, loading and application. It is tested by using farmers’ behavior data collected from 100 orange farmers in Karo District. Based on the experiment, it is able to identify the pesticide exposure risk faced on farmers in the form of the risk percentage automatically, without relying on the experts. Besides, the result shows that it has been appropriate to the expert assessment.

Considering patient safety in autonomous e-mental health systems \u2013 detecting risk situations and referring patients back to human care

BackgroundDigital health interventions can fill gaps in mental healthcare provision. However, autonomous e-mental health (AEMH) systems also present challenges for effective risk management. To balance autonomy and safety, AEMH systems need to detect risk situations and act on these appropriately. One option is sending automatic alerts to carers, but such ‘auto-referral’ could lead to missed cases or false alerts. Requiring users to actively self-refer offers an alternative, but this can also be risky as it relies on their motivation to do so.This study set out with two objectives. Firstly, to develop guidelines for risk detection and auto-referral systems. Secondly, to understand how persuasive techniques, mediated by a virtual agent, can facilitate self-referral.MethodsIn a formative phase, interviews with experts, alongside a literature review, were used to develop a risk detection protocol. Two referral protocols were developed – one involving auto-referral, the other motivating users to self-refer. This latter was tested via crowd-sourcing (n = 160). Participants were asked to imagine they had sleeping problems with differing severity and user stance on seeking help. They then chatted with a virtual agent, who either directly facilitated referral, tried to persuade the user, or accepted that they did not want help. After the conversation, participants rated their intention to self-refer, to chat with the agent again, and their feeling of being heard by the agent.ResultsWhether the virtual agent facilitated, persuaded or accepted, influenced all of these measures. Users who were initially negative or doubtful about self-referral could be persuaded. For users who were initially positive about seeking human care, this persuasion did not affect their intentions, indicating that a simply facilitating referral without persuasion was sufficient.ConclusionThis paper presents a protocol that elucidates the steps and decisions involved in risk detection, something that is relevant for all types of AEMH systems. In the case of self-referral, our study shows that a virtual agent can increase users’ intention to self-refer. Moreover, the strategy of the agent influenced the intentions of the user afterwards. This highlights the importance of a personalised approach to promote the user’s access to appropriate care.

Correction: A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study.

Background: During intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport. Objective: We attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network. Methods: We used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices. Results: Data for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and –1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error. Conclusions: We developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients.

A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study.

BackgroundDuring intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport.ObjectiveWe attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network.MethodsWe used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices.ResultsData for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and –1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error.ConclusionsWe developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients.

Real-Time Traffic Risk Detection Model Using Smart Mobile Device.

Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle’s headway distance. In order to estimate the vehicle’s headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.

Deteksi Tingkat Risiko Kehamilan dengan Metode Fuzzy Mamdani dan Simple Additive Weighting

The risk of pregnancy is a contributing factor in increasing mother maternal mortality (MMR). This study aimed to produce a pregnancy risk detection system based on patient examination results. This research combines fuzzy Mamdani and Simple Additive Weighting (SAW) methods using 11 criteria to determine the risk of pregnant women, that is low, high, and very high. The criteria that determine the risk of pregnancy are expressed as fuzzy statements. In system testing to 100 pregnant women patients, obtained an accuracy of 88% using recognition rate method.

An Early Risk Detection and Management System for the Cloud with Log Parser

In a software-defined data center (SDDC), detecting potential risks from structured or unstructured data in real-time is challenging and proves to be of vital significance to deliver zero downtime services. It is essential for the cloud administrators to be aware of any operation or sequence of operations that could cause critical failures resulting in the loss of Business/Mission Critical Systems (BCS/MCS). This demands for a solution such as an Early Risk Detection and Management System (ERDMS). ERDMS provides insights on the operations that can put the system in peril, and recommends the suitable steps to reduce or eliminate the risks involved.In this work, we present our implementation of an Early Risk Detection and Management System (ERDMS) for the cloud using data analytics, association rule learning and machine learning techniques. The ERDMS continually monitors various system parameters by processing the sequence of operations performed on the system to detect potential risk(s) and recommend the probable solution(s). Initially, it parses the log bundles to learn rules, known as “association rules”, for risk detection using apriori algorithm. Each of these association rules consists of premise − sequence of operations and inference − potential risk(s). While constantly monitoring a system, if ERDMS detects a pattern it has learnt, it classifies the pattern into a set of potential risk(s) using decision tree algorithm. It computes a probability for each potential risk to gauge the impact; it also generates a summary of its learning. Once the potential risk(s) is detected, it searches the relevant sites to recommend a set of probable solution(s). Furthermore, it offers “auto-resolution”, where the recommended steps are automatically executed. Consequently, upon appropriate action, the system might not encounter the issue and will continue to work seamlessly.

A risk detection system of e-commerce: researches based on soft information extracted by affective computing web texts

The product comments in e-commerce circumstance has a strong guidance role on customers, therefore the merchants will usually falsify bogus comments to defraud customers. In order to monitor such behaviors of merchants, we transform the comments of the same product from different merchants to eigenvector by the way of affective computing, then compare it with the vector derived from the comments on the third-party professional assessment and test websites as well as on the microblog, and identify those vectors with big deviation degree, that means the product of those merchants are possibly in fraudulence. Experiment proves that the system can recognize fraudulent merchants.