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Fulltext A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

Alakbari FS, Mohyaldinn ME, Ayoub MA, Muhsan AS, Hussein IA

PLoS One, 2021;16(4):e0250466.
PMID: 33901240 DOI: 10.1371/journal.pone.0250466

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.

Matched MeSH terms: Fuzzy Logic*
A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application

Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA

Int J Med Inform, 2018 04;112:173-184.
PMID: 29500017 DOI: 10.1016/j.ijmedinf.2018.02.001

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.

Matched MeSH terms: Fuzzy Logic*
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation

Palaniappan R, Sundaraj K, Sundaraj S

Comput Methods Programs Biomed, 2017 Jul;145:67-72.
PMID: 28552127 DOI: 10.1016/j.cmpb.2017.04.013

BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.

Matched MeSH terms: Fuzzy Logic*
Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation

Peng P, Wu D, Huang LJ, Wang J, Zhang L, Wu Y, et al.

Interdiscip Sci, 2024 Mar;16(1):39-57.
PMID: 37486420 DOI: 10.1007/s12539-023-00580-0

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Matched MeSH terms: Fuzzy Logic*
A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation

Senanayake C, Senanayake SM

Comput Methods Biomech Biomed Engin, 2011 Oct;14(10):863-74.
PMID: 20924859 DOI: 10.1080/10255842.2010.499866

In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.

Matched MeSH terms: Fuzzy Logic
Fulltext Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder

Shamshirband S, Banjanovic-Mehmedovic L, Bosankic I, Kasapovic S, Abdul Wahab AW

PLoS One, 2016;11(5):e0155697.
PMID: 27219539 DOI: 10.1371/journal.pone.0155697

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Matched MeSH terms: Fuzzy Logic
Fractal characterization of bpn weights evolution

Gunasekaran S, Venkatesh B, Sagar BS

Int J Neural Syst, 2004 Apr;14(2):139-45.
PMID: 15112371

Training methodology of the Back Propagation Network (BPN) is well documented. One aspect of BPN that requires investigation is whether or not the BPN would get trained for a given training data set and architecture. In this paper the behavior of the BPN is analyzed during its training phase considering convergent and divergent training data sets. Evolution of the weights during the training phase was monitored for the purpose of analysis. The evolution of weights was plotted as return map and was characterized by means of fractal dimension. This fractal dimensional analysis of the weight evolution trajectories is used to provide a new insight to understand the behavior of BPN and dynamics in the evolution of weights.

Matched MeSH terms: Logic
Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques

Hoque MA, Pradhan B, Ahmed N, Sohel MSI

Sci Total Environ, 2020 Nov 17.
PMID: 33248778 DOI: 10.1016/j.scitotenv.2020.143600

Droughts are recurring events in Australia and cause a severe effect on agricultural and water resources. However, the studies about agricultural drought risk mapping are very limited in Australia. Therefore, a comprehensive agricultural drought risk assessment approach that incorporates all the risk components with their influencing criteria is essential to generate detailed drought risk information for operational drought management. A comprehensive agricultural drought risk assessment approach was prepared in this work incorporating all components of risk (hazard, vulnerability, exposure, and mitigation capacity) with their relevant criteria using geospatial techniques. The prepared approach is then applied to identify the spatial pattern of agricultural drought risk for Northern New South Wales region of Australia. A total of 16 relevant criteria under each risk component were considered, and fuzzy logic aided geospatial techniques were used to prepare vulnerability, exposure, hazard, and mitigation capacity indices. These indices were then incorporated to quantify agricultural drought risk comprehensively in the study area. The outputs depicted that about 19.2% and 41.7% areas are under very-high and moderate to high risk to agricultural droughts, respectively. The efficiency of the results is successfully evaluated using a drought inventory map. The generated spatial drought risk information produced by this study can assist relevant authorities in formulating proactive agricultural drought mitigation strategies.

Matched MeSH terms: Fuzzy Logic
Fulltext Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

Hossain M, Mekhilef S, Afifi F, Halabi LM, Olatomiwa L, Seyedmahmoudian M, et al.

PLoS One, 2018;13(4):e0193772.
PMID: 29702645 DOI: 10.1371/journal.pone.0193772

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Matched MeSH terms: Fuzzy Logic
Fulltext Design and development of eggplant harvester for gantry system

Wan Ishak, W.I., Kit, W.H., Awwal, M. A.

Pertanika Journal of Science & Technology, 2010;18(2):-.
MyJurnal

This paper describes the design and development of harvesting system for the gantry system to harvest eggplants. For this purpose, the harvesting robot was successfully designed and fabricated for the gantry system to harvest eggplants. The operation of the harvester was controlled by Programmable Logic Controller (PLC). Basically, the limit switches, DC motor, and relay are connected to the PLC. Meanwhile, a PLC ladder diagram was designed and developed to control the operation of the eggplant harvester. A visual basic programme was developed to interface the harvester with a greenhouse gantry control system. A videogrammetry method was employed to calculate the distance between the stems of eggplants and the cutter of robot end effector. The end effector used electric as its power source and it was controlled via Programmable Logic Controller (PLC). Visual Basic Programme was developed to interface the harvester with the gantry control system. The accuracy of the videogrammetry was tested to be 67.2% for X-axis, 88.2% for Y-axis and 84.7% for Z-axis. Meanwhile, the speed of the end effector for harvester is 2.4 km/h and it could lift up to 55 cm. In order to determine detachment force of eggplant, 16 samples of mature eggplants were tested in a greenhouse, and as a result, more than 22.76 N force was needed to detach a mature eggplant inside the gantry system.

Matched MeSH terms: Logic
Fulltext A new hybrid fuzzy time series model with an application to predict PM10 concentration

Alyousifi Y, Othman M, Husin A, Rathnayake U

Ecotoxicol Environ Saf, 2021 Dec 20;227:112875.
PMID: 34717219 DOI: 10.1016/j.ecoenv.2021.112875

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

Matched MeSH terms: Fuzzy Logic
Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique

S C, M V P, S V, M N, K P, Panda B, et al.

Environ Res, 2022 03;204(Pt A):111729.
PMID: 34478727 DOI: 10.1016/j.envres.2021.111729

This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.

Matched MeSH terms: Fuzzy Logic
Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst

Mohammed N, Palaniandy P, Shaik F, Mewada H, Balakrishnan D

Chemosphere, 2023 Feb;314:137665.
PMID: 36581118 DOI: 10.1016/j.chemosphere.2022.137665

In this approach, a batch reactor was employed to study the degradation of pollutants under natural sunlight using TiO2 as a photocatalyst. The effects of photocatalyst dosage, reaction time and pH were investigated by evaluating the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD) and biodegradability (BOD/COD). Design Expert-Response Surface Methodology Box Behnken Design (BBD) and MATLAB Artificial Neural Network - Adaptive Neuro Fuzzy Inference system (ANN-ANFIS) methods were employed to perform the statistical modelling. The experimental values of maximum percentage removal efficiencies were found to be TOC = 82.4, COD = 85.9, BOD = 30.9% and biodegradability was 0.070. According to RSM-BBD and ANFIS analysis, the maximum percentage removal efficiencies were found to be TOC = 90.3, 82.4; COD = 85.4, 85.9; BOD = 28.9, 30.9% and the biodegradability = 0.074, 0.080 respectively at the pH 7.5, reaction time 300 min and photocatalyst dosage of 4 g L-1. The study reveals both models found to be well predicted as compared with experimental values. The values of R2 for RSM-BBD (0.920) and for ANFIS (0.990) models were almost close to 1. The ANFIS model was found to be marginally better than that of RSM-BBD.

Matched MeSH terms: Fuzzy Logic
Fulltext Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Chong JWR, Tang DYY, Leong HY, Khoo KS, Show PL, Chew KW

Bioengineered, 2023 Dec;14(1):2244232.
PMID: 37578162 DOI: 10.1080/21655979.2023.2244232

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Matched MeSH terms: Fuzzy Logic
Fulltext An adaptive handover prediction scheme for seamless mobility based wireless networks

Sadiq AS, Fisal NB, Ghafoor KZ, Lloret J

ScientificWorldJournal, 2014;2014:610652.
PMID: 25574490 DOI: 10.1155/2014/610652

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Matched MeSH terms: Fuzzy Logic
Fulltext Dynamic electronic institutions in agent oriented cloud robotic systems

Nagrath V, Morel O, Malik A, Saad N, Meriaudeau F

Springerplus, 2015;4:103.
PMID: 25763310 DOI: 10.1186/s40064-015-0810-4

The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.

Matched MeSH terms: Logic
Fulltext Tuberculosis disease diagnosis using artificial immune recognition system

Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, et al.

Int J Med Sci, 2014;11(5):508-14.
PMID: 24688316 DOI: 10.7150/ijms.8249

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.

Matched MeSH terms: Fuzzy Logic
Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled

Jeyabalan V, Samraj A, Loo CK

Comput Methods Biomech Biomed Engin, 2010 Oct;13(5):617-23.
PMID: 20336561 DOI: 10.1080/10255840903405678

Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.

Matched MeSH terms: Fuzzy Logic
Fulltext Developing a digital intervention for cancer survivors: an evidence-, theory- and person-based approach

Bradbury K, Steele M, Corbett T, Geraghty AWA, Krusche A, Heber E, et al.

NPJ Digit Med, 2019;2:85.
PMID: 31508496 DOI: 10.1038/s41746-019-0163-4

This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review that identified cancer survivors' needs, including barriers and facilitators to intervention success. Review evidence (N = 49 papers) informed the intervention's Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews (N = 96) with cancer survivors and focus groups with NHS staff and cancer charity workers (N = 31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions.

Matched MeSH terms: Logic
Fulltext The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

Mamuda M, Sathasivam S

MATEMATIKA, 2017;33(1):11-19.
MyJurnal

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

Matched MeSH terms: Logic

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