Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods
اندازه گیری یخ زدگی هوشمند برای کنترل هوشمند ضد فاجعه در گلخانه ها از طریق تعبیه روش های اینترنت اشیا و هوش مصنوعی ترکیبی-2020
A novel Agro-industrial IoT (AIIoT) technology and architecture for intelligent frost forecasting in greenhouses via hybrid Artificial Intelligence (AI), is reported. The Internet of Things (IoT) allows the objects interconnection on the physical world using sensors and actuators via the Internet. The smart system was designed and implemented through a climatological station equipped with Artificial Neural Networks (ANN) and a fuzzy associative memory (FAM) for ecological control of the anti-frost disaster irrigation. The ANN forecasts the inside temperature of the greenhouses and the fuzzy control predicts the cropland temperatures for the activation of five output levels of the water pump. The results were compared to a Fourier-statistical analysis of hourly data, showing that the ANN models provide a temperature prediction with effectiveness higher than 90%, as compared to monthly data model. Moreover, results of this process were validated through the determination of the coefficient of variance analysis method (R2).
Keywords: Smart frost measurement in greenhouses | Anti-frost irrigation | Artificial Neural Network | Fuzzy expert system | Internet-of-things | Hybrid AI methods
A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model
An Expert System Gap Analysis and Empirical Triangulation of Individual Differences, Interventions, and Information Technology Applications in Alertness of Railroad Workers
تجزیه و تحلیل شکاف سیستم خبره و مثلث تجربی تفاوت های فردی ، مداخلات و کاربردهای فناوری اطلاعات در هوشیاری کارگران راهآهن-2019
In this abstract we would like to provide some exciting concrete information including the article’s main impact and significance on expert and intelligent systems. The main impact is that the PTC expert intelligent system fills in the gaps between the human and software decision making processes. This gap analysis is analyzed via empirical triangulation of rail worker data collected from its groups, individuals and the rail industry itself. We utilize an expert intelligent system PTC information technology application to both measure and to improve the alertness of the groups and workers in order to improve the overall safety of the railways through reduced human errors and failures to prevent accidents. Many individual differences in alertness among military, railroad, and other industry workers stem from a lack of sufficient sleep. This continues to be a concern in the railroad industry, even with the implementation of positive train control (PTC) expert system technology. Information technology aids such as PTC cannot prevent all accidents, and errors and failures with PTC may occur. Furthermore, drug interventions are a short-term solution for improving alertness. This study investigated the effect of sleep deprivation on the alertness of railroad signalmen at work, individual differences in alertness, and the information technology available to improve alertness. We investigated various information and communication technology control systems that can be used to maintain operational safety in the railroad industry in the face of incompatible circadian rhythms due to irregular hours, weekend work, and night operations. To fully explain individual differences after the adoption of technology, our approach posits the necessary parameters that one must consider for reason-oriented action, sequential updating, feedback, and technology acceptance in a unified model. This triangulation can help manage workers by efficiently increasing their productivity and improving their health. In our analysis we used R statistical software and Tableau. To test our theory, we issued an Apple watch to a locomotive engineer. The perceived usefulness, perceived ease of use, and actual use he reported led to an analysis of his sleep patterns that eventually ended in his adoption of a sleep apnea device and an improvement in his alertness and effectiveness. His adoption of the technology also resulted in a decrease in his use of chemical interventions to increase his alertness. Our model shows that the alertness of signalmen can be predicted. Therefore, we recommend that the alertness of all railroad workers be predicted given the safety limitations of PTC.
Keywords : Sleep Deprivation | Fatigue | Stress | Expert System | Alertness | Empirical Analysis
An ontology-based methodology for hazard identification and causation analysis
یک روش مبتنی بر هستی شناسی برای شناسایی ریسک و تجزیه و تحلیل علیت-2019
This article presents a dynamic hazard identification methodology founded on an ontology-based knowl-edge modeling framework coupled with probabilistic assessment. The objective is to develop an efficientand effective knowledge-based tool for process industries to screen hazards and conduct rapid risk esti-mation. The proposed generic model can translate an undesired process event (state of the process)into a graphical model, demonstrating potential pathways to the process event, linking causation tothe transition of states. The Semantic web-based Web Ontology Language (OWL) is used to captureknowledge about unwanted process events. The resulting knowledge model is then transformed intoProbabilistic-OWL (PR-OWL) based Multi-Entity Bayesian Network (MEBN). Upon queries, the MEBNsproduce Situation Specific Bayesian Networks (SSBN) to identify hazards and their pathways along withprobabilities. Two open-source software programs, Protégé and UnBBayes, are used. The developed modelis validated against 45 industrial accidental events extracted from the U.S. Chemical Safety Board’s (CSB)database. The model is further extended to conduct causality analysis.
Keywords:Hazard identification |Probabilistic ontology |Web ontology language | Multi-entity Bayesian network | Expert system
Track circuit fault prediction method based on grey theory and expert system
روش پیش بینی خطای مدار بر اساس تئوری خاکستری و سیستم خبره-2019
Due to the lack of accurate state judgment and health analysis of equipment operation, track circuit implements the repair and maintenance strategy of fault repair or planned repair. For this reason, a novel track circuit fault prediction method is proposed based on grey theory and expert system. In the proposed method, the feature of grey prediction model is to establish dynamic differential equation and then predict its own development according to its own data. The dynamic prediction model with equal dimension is applied to improve original grey model. Based on the gray models, the expert system is used to simulate human experts to solve the problems in a professional field. It contains man-computer interface, inference engine, knowledge library, knowledge management system, interpretation module and dynamic database. The measurement data show this system can effectively predict several typical faults of HVAP track circuit, and prove the proposed system structure is effective. Such condition-based fault prognostic maintenance mechanism provides an effective solution to improve equipment maintenance efficiency, reduce maintenance cost and reduce equipment fault rate.
Keywords: Track circuit | Fault prediction | Grey theory | Expert system
Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019
Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug- AE relations from unstructured hospital discharge summaries. Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program
Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction
Extreme learning machine-based prediction of uptake of pharmaceuticals in reclaimed water irrigation lettuces in the Region of Murcia, Spain
پیش بینی مبتنی بر یادگیری ماشین افراطی از جذب داروهای دارویی در کاهو های آبیاری قابل احیا در منطقه مورسیا ، اسپانیا-2019
The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feedforward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk.
Keywords: WWTP effluent | Intelligent expert system | ELM | Carbamazepine | Diclofenac | Lettuces
Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression
طراحی و اجرای سیستم خبره فازی در روش های مونت کارلو برای رگرسیون خطی فازی-2019
In this study, fuzzy expert system (FES) in Monte Carlo (MC) method, which is used for estimating fuzzy linear regression model (FLRM) parameters, is applied to determine the parameter intervals, for the first time in the literature. MC method in estimating FLRM parameters is a new field of study that is very useful and time saving. However a major problem might occur in determining the parameter intervals from which the regression model parameters are supposed to come. If the intervals are calculated too large, FLRM error will be very large. Accordingly, the actual model parameters will not be obtained if the intervals are calculated too narrow. This drawback has not been addressed in the literature before and only optimization methods have been applied to achieve the best interval values. In this article, the FES is used for the first time in order to solve the problem in parameter estimation process for the FLRM in the field of statistics. For this purpose, the difference between the fuzzy observation value and fuzzy estimation value’s support set (W) is taken into account. The most appropriate intervals calculated for the parameters are those that make W as small as possible. Thus, FES is designed to determine the best intervals for the model parameters. The system knowledge base is composed of 7 fuzzy rules. As a result, it is deduced that the FLRM parameter estimates obtained from the MC method using FES are very close to the real values. The real impact of this paper will be in showing the applicability of FESs in order to solve problems that we encounter in the field of statistics by the help of linguistic expressions. Moreover, these outcomes will be useful for enriching the studies that have already focused on FLRMs and will encourage researchers to use FES to solve problems in statistics. To sum up, this study demonstrates that FESs which is used in technological devices and makes our lives easier can also be used in solving problems that we confront in the field of statistics efficiently with using linguistic expressions like human inference system.
Keywords: Fuzzy expert system | Fuzzy linear regression | Monte Carlo
Designing a general type-2 fuzzy expert system for diagnosis of depression
طراحی سیستم تخصصی فازی نوع 2 برای تشخیص افسردگی-2019
Depression is a common and important mental disorder that affects the quality of human life. Since people with depression are not aware of their disorder and sometimes suffer from physical symptoms such as chronic pain, refer to a physician instead of a psychologist. Hence, physician’s diagnosis is not always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis, considering two main objectives, was developed. These objectives include accuracy of the system and diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system has five steps. In the first step, we generate general type-2 membership function by using zSlices method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The proposed general type-2 expert system has been tested and evaluated to show its performance. This Intelligent system is able to diagnose depression accurately at a suitable time.
Keywords: Depression Computing with words (CWW) | General type-2 fuzzy sets | zSlices | MOEA/D algorithm | Feature selection | Beck Depression Inventory-II test (BDI-II) | Adaptive system | Expert system
Detection of peripheral arterial disease using Doppler spectrogram based expert system for Point-of-Care applications
تشخیص بیماری شریانی محیطی با استفاده از سیستم خبره مبتنی بر طیف سنجی داپلر برای کاربردهای نقطه مراقبت-2019
tPeripheral arterial disease (PAD) is a common manifestation of cardiovascular diseases and more preva-lent in underdeveloped countries. Ultrasound (US) is one of the preferred non-invasive diagnostictechniques for the evaluation of PAD. This work aims at achieving a low-cost PAD detection technique formass screening. A computer aided diagnosis (CAD) method has been proposed based on the Doppler bloodflow spectrograms of lower limb arteries. The proposed scheme initially removes noise from the spectro-gram (350 × 175 pixels) and extracts the hemodynamic features which are generally independent of theDoppler angle. From these, best feature subsets are selected using the wrapper algorithm and supervisedclassifiers are developed in a machine learning framework to perform using 10-fold cross-validationtechnique. Overall, 334 arterial segments of 60 subjects are investigated where reference measurementis taken from the triplex mode US scanning. The quantitative assessment using random forest based clas-sifier provides an accuracy of 84.37% and 87.93% for detecting the blood flow irregularities in above-kneeand below-knee arterial segments, respectively. To classify the arterial diseases into normal, stenosis andocclusion categories, support vector machine (SVM) classifier is found to provide 97.91% accuracy on theunknown testing dataset. Moreover, variations of diagnostic parameters around the proximal and distalarterial segments define the zone of significant stenosis. The degree of stenosis is determined to quantifythe severity of obstruction and the accuracy for stenosis greater than 50% is found to be 96.83%. Finally,smartphone application is implemented to Keywords:Ultrasonography | Peripheral artery disease | Features extraction |Machine learning |Androida