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International Journal of Digital Earth 2013年第1期6卷 51-75页

作者： Mohammad H.Vahidnia Ali A.Alesheikh Saeed Behzadi Sara Salehi Geodesy and Geomatics Engineering Department of Geospatial Information SystemK.N.Toosi University of TechnologyTehranIran Faculty of Geodesy and Geomatics Engineering Department of PhotogrammetryK.N.Toosi University of TechnologyTehranIran

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital *** modeling,which is one of the most helpful analyses in the geographic information system(GIS),plays a prominent role in meeting this *** study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable spatio-temporal in cellular automata(CA)***,artificial intelligence tools such as adaptive neuro-fuzzy inference system(ANFIS)and genetic algorithm(GA)were used in accordance with the objectives of knowledge discovery and *** conditions in updating states are considered so traditional CA transition rules can be accompanied with the impact of fuzzy discovered knowledge and the solution of spread *** focused on the estimation of forest fire growth as an important case study for decision makers.A two-dimensional cellular representation of the combustion of heterogeneous fuel types and density on non-flat terrain were successfully linked with dynamic wind and slope *** validation of the simulation on experimental data indicated a relatively realistic head-fire *** investigations showed that the results obtained using the dynamic controlling with GA in the absence of static modeling with ANFIS were unacceptable.

关键词： geographic information system adaptive neuro-fuzzy inference system cellular automata genetic algorithm spatio-temporal spread forest fire digital earth

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

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Computers, Materials & Continua 2024年第6期79卷 3783-3803页

作者： Yi-Chun Lai Shu-Yin Chiang Yao-Chiang Kan Hsueh-Chun Lin Department of Public Health China Medical UniversityTaichung406040Taiwan Department of Information and Telecommunications Engineering Ming Chuan UniversityGui-ShanTaoyuan333Taiwan Department of Electrical Engineering Yuan Ze UniversityChung-Li32003Taiwan Department and Institute of Health Service Administrations China Medical UniversityTaichung406040Taiwan

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation *** study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in *** analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the ***,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and *** ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data *** contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature ***,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model *** best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,*** analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,***,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research *** conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

关键词： Human activity recognition artificial intelligence support vector machine random forest adaptive neuro-fuzzy inference system convolution neural network recursive feature elimination

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

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Computers, Materials & Continua 2021年第4期67卷 641-657页

作者： Mavra Mehmood Ember Ayub Fahad Ahmad Madallah Alruwaili Ziyad AAlrowaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Tahir Alyas Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Physics Department College of ScienceJouf UniversitySakakaAljouf72341Saudi Arabia Division of Computer Science&Information Technology University of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

Clinical image processing plays a signicant role in healthcare systems and is currently a widely used *** carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early *** carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and *** number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling *** processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing *** this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our *** this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor *** detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram *** resulting images’tumor portions are then isolated by a segmentation process,such as threshold ***,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of *** classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal ***,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the *** detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these *** results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies

关键词： Image processing tumor segmentation dilation erosion machine learning classication support vector machine adaptive neuro-fuzzy inference system

On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters

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Information Processing in Agriculture 2018年第2期5卷 205-223页

作者： S.M.Shafaei M.Loghavi S.Kamgar Department of Biosystems Engineering School of AgricultureShiraz UniversityShiraz 71441-65186Iran

Tractor fuel efficiency parameters(TFEPs)(fuel consumption per working hour(FCWH),fuel consumption per tilled area(FCTA)and specific volumetric fuel consumption(SVFC))were intelligently simulated.A neurocomputing based simulation strategy(adaptive neurofuzzy inference system(ANFIS))was used to simulate the TFEPs.A comparison was also made between results of the best ANFIS environment and those of another neurocomputing based simulation strategy,artificial neural network(ANN).Field experiments were conducted at plowing depths of 10,20 and 30(cm)and forward speeds of 2,4 and 6(km/h)using a disk plow *** descriptor parameters applied to evaluate simulation environments indicated that the best simulation environment of both ANFIS and ANN were able to perfectly predict the ***,the best comprehensive ANN simulation environment with a simple architecture of 2-6-3 was easier to use than three individual ANFIS simulation *** ANN results revealed that simultaneous increase of forward speed from 2 to 6(km/h)and plowing depth from 10 to 30(cm)led to nonlinear increment of the FCWH from 5.29 to 14.89(L/h)and nonlinear decrement of the SVFC from 2.95 to 0.67(L/h kW).Meanwhile,forward speed increment along with plowing depth decrement resulted in nonlinear decrement of the FCTA from 28.13 to 12.24(L/ha).Interaction of forward speed and plowing depth on the FCWH and SVFC was congruent,while it was incongruent for the *** is suggested to employ the ANN environment in developing future fuel planning schemes of tractor during tillage operations.

关键词： adaptive neuro-fuzzy inference system Artificial neural network Fuel consumption per working hour Fuel consumption per tilled area Specific volumetric fuel consumption

Secure Dengue Epidemic Prediction system: Healthcare Perspective

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Computers, Materials & Continua 2022年第10期73卷 1723-1745页

作者： Abdulaziz Aldaej Tariq Ahamed Ahanger Mohammed Yousuf Uddin Imdad Ullah College of Computer Engineering and Science Prince Sattam bin Abdulaziz University Al-Kharj11942Saudi Arabia

Viral diseases transmitted by mosquitoes are emerging public health problems across the *** is considered to be the most significant mosquito-oriented ***,the present study provides an effective architecture for Dengue Virus Infection *** proposed system involves a 4-level architecture for the prediction and prevention of dengue infection *** architectural levels including Dengue Information Acquisition level,Dengue Information Classification level,DengueMining and Extraction level,and Dengue-Prediction and Decision Modeling level enable an individual to periodically monitor his/her probabilistic dengue fever *** prediction process is carried out so that proactive measures are taken *** predictive purposes,probabilistic analysis in terms of Level of Dengue Fever(LoDF)was carried out using the adaptive neurofuzzy inference *** on the Self-Organized Mapping procedure,the presence of LoDF is *** simulations on datasets of 16 individuals cumulating to 32,255 instances were conducted to test the effectiveness of the presented *** comparison to other decision-modeling methods,significantly improved results in form of classification efficacy,a temporal delay,prediction effectiveness,reliability,and stability were reported for the presented model.

关键词： Internet of things smart air real-time healthcare adaptive neuro-fuzzy inference system

Investigation of factors affecting rural drinking water consumption using intelligent hybrid models

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Water Science and Engineering 2023年第2期16卷 175-183页

作者： Alireza Mehrabani Bashar Hamed Nozari Safar Marofi Mohamad Mohamadi Ahad Ahadiiman Department of Water Science and Engineering Faculty of AgricultureBu-Ali Sina UniversityHamedan 65178-38695Iran University of Kurdistan Sanandaj 66177-15175Iran Sustainable Development Office of Hamedan Water and Wastewater Company Hamedan 65159-14478Iran

Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking water demand were studied using the adaptive neuro-fuzzy inference system (ANFIS) and hybrid models, such as the ANFIS-genetic algorithm (GA), ANFIS-particle swarm optimization (PSO), and support vector machine (SVM)-simulated annealing (SA). The rural areas of Hamadan Province in Iran were selected for the case study. Five drinking water consumption factors were selected for the assessment according to the literature, data availability, and the characteristics of the study area (such as precipitation, relative humidity, temperature, the number of subscribers, and water price). The results showed that the standard errors of ANFIS, ANFIS-GA, ANFIS-PSO, and SVM-SA were 0.669, 0.619, 0.705, and 0.578, respectively. Therefore, the hybrid model SVM-SA outperformed other models. The sensitivity analysis showed that of the parameters affecting drinking water consumption, the number of subscribers significantly affected the water consumption rate, while the average temperature was the least significant factor. Water price was a factor that could be easily controlled, but it was always one of the least effective parameters due to the low water fee.

关键词： ANFIS Water distribution network Simulated annealing algorithm Support vector machine adaptive neuro-fuzzy inference system

Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage systems

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Computers, Materials & Continua 2022年第11期73卷 3595-3611页

作者： Belqasem Aljafari Eydhah Almatrafi Sudhakar Babu Thanikanti Sara A.Ibrahim Mohamed A.Enany Marwa M.Ahmed Electrical Engineering Department College of EngineeringNajran UniversityNajran11001Saudi Arabia Mechanical Engineering Department College of Engineering-RabighKing Abdulaziz UniversityJeddahSaudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Desalination Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of TechnologyHyderabad500075India Electrical Power and Machines Department Faculty of EngineeringZagazig UniversityZagazig44519Egypt Electrical Engineering Department Faculty of EngineeringKing Abdul-Aziz UniversityJeddah80204Saudi Arabia

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking(MPPT)techniques for Photo-Voltaic based Battery Storage systems(PV-BSS).To have a full comparative study in terms of the dynamic response,battery state of charge(SOC),and oscillations around the Maximum Power Point(MPP)of the PV-BSS to variations in climate conditions,these techniques are simulated in Matlab/*** introduced methodologies are classified into two types;the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb&Observe and Incremental Conductance *** second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre-build adaptive neuro-fuzzy inference system(ANFIS)model to predict DC–DC converter optimum duty cycle to track *** evaluation study is introduced for conventional and proposed On-Line MPPT *** comparative study can be useful in specifying the appropriateness of the MPPT techniques for *** the introduced model can be used as a valued reference model for future research related to Soft Computing(SC)MPPT techniques.A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

关键词： Photo-voltaic based battery storage systems adaptive neuro-fuzzy inference system maximum power point tracking perturb&observe technique incremental conductance technique state of charge

Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

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Energy and AI 2023年第4期14卷 398-414页

作者： Heba-Allah I.El-Azab R.A.Swief Noha H.El-Amary H.K.Temraz Faculty of Engineering Ahram Canadian University(ACU)GizaEgypt Faculty of Engineering Ain Shams UniversityCairo 11517Egypt Arab Academy for Science Technology and Maritime Transport(AASTMT)Cairo 2033Egypt

In this paper,multiple featured machine learning algorithms and deep learning algorithms are applied in fore-casting the electric vehicles charging load profile from real datasets of Spain’s electrical *** study aims to provide realistic datasets of electric vehicle load profiles to cope with the potential increase in electric vehicle penetration taking into consideration the seasonality *** issues are caused by the distribution network of the electricity grid;such as the huge charging power and stochastic charging behaviors of the drivers of electric vehicles due to the mass rollout of electric *** electric vehicles’load profile is necessary to face challenges to solve the problem of the potential mass rollout of electric vehicles ***,Electric vehicle is considered one of the most promising solutions that develops faster than other stochastic renewable solution to reduce greenhouse *** seasonality effect is one of the huge chal-lenges on electrical loads,so it is investigated by creating four separate forecasting networks to increase system accuracy and studying the effect of seasonal factors such as temperature fluctuation in the four seasons affecting the electric vehicles’battery in charging and draining *** factors are affecting the accuracy of the forecasting *** featured algorithms are *** Neural Networks and adaptive neuro-fuzzy inference systems are applied as machine learning algorithms,and Long Short-Term Memory and the Gated Recurrent Units are also utilized as deep learning *** Gated Recurrent Units model performs slightly better than the long short-term memory employed on the hourly average daily historical data of charging electric *** the adaptive neuro-fuzzy inference system gathers both Artificial Neural Network and fuzzy inference system advantages.

关键词： adaptive neuro-fuzzy inference system Deep learning Electric vehicles Gated recurrent units Long short-term memory Neural network Short-term load forecasting

Element yield rate prediction in ladle furnace based on improved GA-ANFIS

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Journal of Central South University 2012年第9期19卷 2520-2527页

作者：徐喆毛志忠 School of Information Science and Engineering Northeastern University State Key Laboratory of Integrated Automation for Process Industries(Northeastern University)

The traditional prediction methods of element yield rate can be divided into experience method and data-driven *** in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always *** at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS *** facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local *** experimental and practical testing results show that the proposed method is more accurate than other prediction methods.

关键词： genetic algorithm adaptive neuro-fuzzy inference system ladle furnace element yield rate prediction

Reconfiguration decision making in cognitive wireless network

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Science Bulletin 2012年第Z2期 3713-3722页

作者： ZHANG Ping,HE Qian,FENG ZhiYong * & ZHANG QiXun Wireless Technology Innovation Institute(WTI),Key Laboratory of Universal Wireless Communications of Ministry of Education,Beijing University of Posts and Telecommunications,Beijing 100876,China

Facing the challenges of dynamic adaptation capabilities in the time-varying environment of cognitive wireless networks(CWNs),we introduce reconfiguration capabilities that flexibly and dynamically adapt to changing wireless environments and service *** an essential characteristic of CWNs,the cognitive reconfiguration can meet user requirements,realize interoperability between heterogeneous networks,make full use of radio resources and adapt to time-varying environments to achieve end-to-end ***,the reconfiguration implementation is still challenging due to the need for complex environment cognition,multi-objective optimization,autonomic decision-making and end-to-end requirement *** an intelligent technology for solving complex issues,we apply adaptive neuro-fuzzy inference system(ANFIS) techniques in this paper to address these challenges in cognitive reconfiguration for self-learning and optimal decision making based on multi-domain cognition ***,this paper designs a generic ANFIS cognitive reconfiguration system including three functional entities,which are the context management module,multi-domain database and ANFIS optimization ***,numerous results prove the effective performance improvements of the ANFIS based reconfiguration solution in CWN for global end-to-end goals.

关键词： cognitive reconfiguration cognitive wireless network adaptive neuro-fuzzy inference system