Rule-based Mamdani-type fuzzy modeling of skin permeability

Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Due to the worldwide water supply crisis, sustainable strategies are required for a better use of this resource. The use of magnetic water has been shown to have potential for improving irrigation efficacy. However, a lack of modelling methods that correspond to the experimental results and minimize error is observed. This study aimed to estimate the replacement rates of magnetic water provided by irrigation for lettuce production using a mathematical model based on fuzzy logic and to compare multiple polynomial regression analysis and the fuzzy model. A greenhouse study was conducted with lettuce using two types of water, magnetic water (MW) and conventional water (CW), and five irrigation levels (25, 50, 75, 100 and 125%) of crop evapotranspiration. Plant samples for biometric lettuce were taken at 14, 21, 28 and 35 days after transplanting. The data were analyzed via multiple polynomial regression and fuzzy mathematical modeling, followed by an inference of the models and a comparison between the methods. The highest biometric values for lettuce were observed when irrigated with MW during the different phenological stage evaluated. The fuzzy model provided a more exact adjustment when compared to the multiple polynomial regressions.

A Fuzzy-Based Model to Predict the Spatio-Temporal Performance of the Dolichogenidea gelechiidivoris Natural Enemy against Tuta absoluta under Climate Change

The South American tomato pinworm, Tuta absoluta, causes up to 100% tomato crop losses. As Tuta absoluta is non-native to African agroecologies and lacks efficient resident natural enemies, the microgastrine koinobiont solitary oligophagous larval endoparasitoid, Dolichogenidea gelechiidivoris (Marsh) (Syn.: Apanteles gelechiidivoris Marsh) (Hymenoptera: Braconidae) was released for classical biological control. This study elucidates the current and future spatio-temporal performance of D. gelechiidivoris against T. absoluta in tomato cropping systems using a fuzzy logic modelling approach. Specifically, the study considers the presence of the host and the host crop, as well as the parasitoid reproductive capacity, as key variables. Results show that the fuzzy algorithm predicted the performance of the parasitoid (in terms of net reproductive rate (R0)), with a low root mean square error (RMSE) value (<0.90) and a considerably high R2 coefficient (=0.98), accurately predicting the parasitoid performance over time and space. Under the current climatic scenario, the parasitoid is predicted to perform well in all regions throughout the year, except for the coastal region. Under the future climatic scenario, the performance of the parasitoid is projected to improve in all regions throughout the year. Overall, the model sheds light on the varying performance of the parasitoid across different regions of Kenya, and in different seasons, under both current and future climatic scenarios.

Privacy and Trust in eHealth: A Fuzzy Linguistic Solution for Calculating the Merit of Service

The use of eHealth and healthcare services are becoming increasingly common across networks and ecosystems. Identifying the quality and health impact of these services is a big problem that in many cases it is difficult determine. Health ecosystems are seldom designed with privacy and trust in mind, and the service user has almost no way of knowing how much trust to place in the service provider and other stakeholders using his or her personal health information (PHI). In addition, the service user cannot rely on privacy laws, and the ecosystem is not a trustworthy system. This demonstrates that, in real life, the user does not have significant privacy. Therefore, before starting to use eHealth services and subsequently disclosing personal health information (PHI), the user would benefit from tools to measure the level of privacy and trust the ecosystem can offer. For this purpose, the authors developed a solution that enables the service user to calculate a Merit of Service (Fuzzy attractiveness rating (FAR)) for the service provider and for the network where PHI is processed. A conceptual model for an eHealth ecosystem was developed. With the help of heuristic methods and system and literature analysis, a novel proposal to identify trust and privacy attributes focused on eHealth was developed. The FAR value is a combination of the service network’s privacy and trust features, and the expected health impact of the service. The computational Fuzzy linguistic method was used to calculate the FAR. For user friendliness, the Fuzzy value of Merit was transformed into a linguistic Fuzzy label. Finally, an illustrative example of FAR calculation is presented.

A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.

A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.

High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system

Bubbly flow behavior simulation in two-phase chemical reactors such bubble column type reactors is widely employed for chemical industry purposes. The computational fluid dynamics (CFD) approach has been employed by engineers and researchers for modeling these types of chemical reactors. In spite of the CFD robustness for simulating transport phenomena and chemical reactions in these reactors, this approach has been known as expensive for modeling such turbulent complex flows. Artificial intelligence (AI) algorithm of the adaptive network-based fuzzy inference system (ANFIS) are largely understood and utilized for the CFD approach optimization. In this hybrid approach, the CFD findings are learned by AI algorithms like ANFIS to save computational time and expenses. Once the pattern of the CFD results have been captured by the AI model, this hybrid model can be then used for process simulation and optimization. As such, there is no need for further simulations of new conditions. The objective of this paper is to obviate the need for expensive CFD computations for two-phase flows in chemical reactors via coupling CFD data to an AI algorithm, i.e., differential evolution based fuzzy inference system (DEFIS). To do so, air velocity as the output and the values of the x, and y coordinates, water velocity, and time step as the inputs are inputted the AI model for learning the flow pattern. The effects of cross over as the DE parameter and also the number of inputs on the best intelligence are investigated. Indeed, DEFIS correlates the air velocity to the nodes coordinates, time, and liquid velocity and then after the CFD modeling could be replaced with the simple correlation. For the first time, a comparison is made between the ANFIS and the DEFIS performances in terms of the prediction capability of the gas (air) velocity. The results released that both ANFIS and DEFIS could accurately predict the CFD pattern. The prediction times of both methods were obtained to be equal. However, the learning time of the DEFIS was fourfold of ANFIS.

Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients

OBJECTIVES

In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision-making process; therefore, FL-based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL-based CDSS for the evaluation of renal function in posttransplant patients.

METHOD

Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR).

RESULTS

The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors.

CONCLUSIONS

We propose that the CDSSs for the assessment and follow-up of kidney-transplanted patients built in this study are applicable to clinical practice.

Health Information Systems in the Digital Health Ecosystem-Problems and Solutions for Ethics, Trust and Privacy

Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject's perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors' objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user's privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems.

Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N₂ adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R²>0.99). The maximum monolayer adsorption capacity (q_m) of NiO/ACNC was 1428.57mg/L. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R²>0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.

Fuzzy expert system for diagnosing diabetic neuropathy

AIM

To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy.

METHODS

The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system.

RESULTS

The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%).

CONCLUSION

The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients.

A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy

The prevalence of peripheral neuropathy in general population is ever increasing. The diagnosis and classification of peripheral neuropathies is often difficult as it involves careful clinical and electro-diagnostic examination by an expert neurologist. In developing countries a large percentage of the disease remains undiagnosed due to lack of adequate number of experts. In this study a novel clinical decision support system has been developed using a fuzzy expert system. The study was done to provide a solution to the demand of systems that can improve health care by accurate diagnosis in limited time, in the absence of specialists. It employs a graphical user interface and a fuzzy logic controller with rule viewer for identification of the type of peripheral neuropathy. An integrated medical records database is also developed for the storage and retrieval of the data. The system consists of 24 input fields, which includes the clinical values of the diagnostic test and the clinical symptoms. The output field is the disease diagnosis, whether it is Motor (Demyelinating/Axonopathy) neuropathy, sensory (Demyelinating/Axonopathy) neuropathy, mixed type or a normal case. The results obtained were compared with the expert's opinion and the system showed 93.27 % accuracy. The study aims at showing that Fuzzy Expert Systems may prove useful in providing diagnostic and predictive medical opinions. It enables the clinicians to arrive at a better diagnosis as it keeps the expert knowledge in an intelligent system to be used efficiently and effectively.

Fuzzy inference game approach to uncertainty in business decisions and market competitions

The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF)

In the present paper, a three factor, three-level response surface design based on Box-Behnken design (BBD) was developed for maximizing lead removal from aqueous solution using micellar-enhanced ultrafiltration (MEUF). Due to extremely complexity and nonlinearity of membrane separation processes, fuzzy logic (FL) models have been driven to simulate MEUF process under a wide range of initial and hydrodynamic conditions. Instead of using mathematical model, fuzzy logic approach provides a simpler and easier approach to describe the relationships between the processing variables and the metal rejection and permeation flux. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 91% for all cases. The results show that predicted values obtained from the fuzzy model were in very good agreement with the reported experimental data.