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Hybrid classifier for fault location in active distribution networks

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Protection and Control of Modern Power Systems 2020年第1期5卷 194-202页

作者： Sadegh Jamali Alireza Bahmanyar Siavash Ranjbar Centre of Excellence for Power System Automation and Operation School of Electrical EngineeringIran University of Science and TechnologyP.O.Box:16846-13114TehranIran

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation(DG)and *** method uses the voltage and current data from the measurement points at the main substation,and the connection points of DG and *** data is used in a single feedforward artificial neural network(ANN)to estimate the distances to fault from all the measuring points.A k-nearest neighbors(KNN)classifier then interprets the ANN outputs and estimates a single fault *** results validate the accuracy of the fault location method under different fault conditions including fault types,fault points,and fault *** performance is also validated for non-synchronized measurements and measurement errors.

关键词： artificial neural networks Distributed generation Distribution networks Fault location K-nearest neighbors

Predictive model for *** mortality after breast cancer surgery in Taiwan residents

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Chinese Journal of Cancer 2017年第4期36卷 184-192页

作者： Su-Hsin Huang Joon-Khim Loh Jinn-Tsong Tsai Ming-Feng Houg Hon-Yi Shi Department of Nursing Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Medical University Kaohsiung Taiwan China Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University 100.Shih.Chun 1st Road Kaohsiung Taiwan China Divison of Neurosurgery Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan China Department of Surgery Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Taiwan China. Department of Computer Science National Pingtung University Pingtung Taiwan China Division of General & Gastroenterological Surgery Department of Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan China Cancer Center Kaohsiung Medical University Hospital and Institute of Clinical Medicine Kaohsiung Medical University Kaohsiung Taiwan China Department of Business Management National Sun Yat-sen University Kaohsiung Taiwan China

Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 *** study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model,multiple logistic regression(MLR)model,and Cox regression ***:This study compared the MLR,Cox,and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and *** estimation dataset was used to train the model,and a validation dataset was used to evaluate model *** sensitivity analysis was also used to assess the relative signifi?cance of input variables in the prediction ***:The ANN model significantly outperformed the MLR and Cox models in predicting 5?year mortality,with higher overall performance *** results indicated that the 5?year postoperative mortality of breast cancer patients was significantly associated with age,Charlson comorbidity index(CCI),chemotherapy,radiotherapy,hormone therapy,and breast cancer surgery volumes of hospital and surgeon(all P<0.05).Breast cancer surgery volume of surgeon was the most influential(sensitive)variable affecting 5?year mortality,followed by breast cancer surgery volume of hospital,age,and ***:Compared with the conventional MLR and Cox models,the ANN model was more accurate in predict?ing 5?year mortality of breast cancer patients who underwent *** mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.

关键词： Breast cancer surgery artificial neural networks Multiple logistic regression Cox regression 5-year mortality

Application of four machine-learning methods to predict short-horizon wind energy

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Global Energy Interconnection 2023年第6期6卷 726-737页

作者： Doha Bouabdallaoui Touria Haidi Faissal Elmariami Mounir Derri El Mehdi Mellouli Laboratory LAGES Ecole Hassania des Travaux Publics(EHTP)Casablanca 20230Morocco Laboratory LESE Ecole Nationale Supérieure d’Electricitéet de Mécanique(ENSEM)Casablanca 20100Morocco Laboratory LESA School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco

Renewable energy has garnered attention due to the need for sustainable energy *** power has emerged as an alternative that has contributed to the transition towards cleaner *** the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance *** intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of *** study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this *** main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.

关键词： Wind Energy Prediction Support Vector Machines Decision Trees Adaptive Neuro-Fuzzy Inference Systems artificial neural networks

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

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Computers, Materials & Continua 2022年第2期70卷 2971-2989页

作者： Jose Escorcia-Gutierrez Jordina Torrents-Barrena Margarita Gamarra Natasha Madera Pedro Romero-Aroca Aida Valls Domenec Puig Electronic and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Ophthalmology Service Universitari Hospital Sant JoanInstitut de Investigacio Sanitaria Pere VirgiliReus43201Spain Departament d’Enginyeria Informàtica i Matemàtiques Escola Tècnica Superior d’EnginyeriaUniversitat Rovira i VirgiliTarragona43007Spain

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational *** method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

关键词： Diabetic retinopathy artificial neural networks decision trees support vector machines feature selection retinal vasculature segmentation

Swarm Optimization and Machine Learning for Android Malware Detection

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Computers, Materials & Continua 2022年第12期73卷 6327-6345页

作者： K.Santosh Jhansi P.Ravi Kiran Varma Sujata Chakravarty Centurion University of Technology and Management ParalakhemundiOdishaIndia Maharaj Vijayaram Gajapathi Raj College of Engineering VizianagaramIndia Centurion University of Technology and Management BhubaneswarOdishaIndia

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security *** Programming Interfaces(API)calls contain valuable information that can help with malware *** malware analysis with reduced feature space helps for the efficient identification of *** goal of this research is to find the most informative features of API calls to improve the android malware detection *** swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential *** nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid artificial Neuronal Classifier(ANC)is proposed for improving the classification of android *** experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.

关键词： Android malware API calls auto-encoders ant lion optimization cuckoo search optimization firefly optimization artificial neural networks artificial neuronal classifier

Modeling of daily pan evaporation using partial least squares regression

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Science China(Technological Sciences) 2011年第1期54卷 163-174页

作者： ABUDU Shalamu CUI ChunLiang J. Phillip KING Jimmy MORENO A. Salim BAWAZIR Xinjiang Water Resources Research Institute Urumqi 830049 China Civil Engineering Department New Mexico State University Las Cruces New Mexico 88001 USA

This study presented the application of partial least squares regression （PLSR） in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks （ANN） models with the same input variables. The resuits showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models.

关键词： modeling daily pan evaporation partial least squares regression artificial neural networks meteorological data

Performance Comparison of artificial neural Network Models for Daily Rainfall Prediction

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International Journal of Automation and computing 2016年第5期13卷 417-427页

作者： S.Renuga Devi P.Arulmozhivarman C.Venkatesh Pranay Agarwal School of Electronics Engineering VIT UniversityVellore 632 014Tamil NaduIndia Faculty of Engineering EBET Group of InstitutionsKangayamTamil NaduIndia

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

关键词： Rainfall prediction artificial neural networks distributed time delay neural network cascade-forward back propagation network nonlinear autoregressive exogenous network.

The superior fault tolerance of artificial neural network training with a fault/noise injection- based genetic algorithm

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Protein & Cell 2016年第10期7卷 735-748页

作者： Feng Su Peijiang Yuan Yangzhen Wang Chen Zhang Robotics Institute Beihang University Beijing 100191 China State Key Laboratory of Membrane Biology School of Life Sciences Beijing 100871 China PKU-IDG/McGovern Institute for Brain Research Peking University Beijing100871 China

artificial neural networks （ANNs） are powerful compu- tational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance （FT）, an important property of ANNs, ensures their reliability when signifi- cant portions of a network are lost. In this paper, a fault/ noise injection-based （FIB） genetic algorithm （GA） is proposed to construct fault-tolerant ANNs. The FT per- formance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algo- rithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR （XOR） problem and the overlapping clas- sification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore Indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.

关键词： artificial neural networks fault tolerance,genetic algorithm

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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Computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming