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Smart Care: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones

Tsinghua Science and Technology 2015年第4期20卷 348-363页

作者： Hui Liu Rui Li Sicong Liu Shibian Tian Junzhao Du School of Software and Institute of Software Engineering Xidian Universityand Science and Technology on Infomation Transmission and Dissemination in Communication Networks Lab.

Lack of physical activity is becoming a killer of our healthy life. As a solution for this negative impact, we propose SmartCare to help users to set up a healthy physical activity habit. SmartCare can monitor a user＇s activities over a long time, and then provide activity quality assessment and suggestion. SmartCare consists of three parts, activity recognition, energy saving, and health feedback. Activity recognition can recognize nine kinds of daily activities. A hybrid classifier that uses less power and memory with satisfactory accuracy was designed and implemented by utilizing the periodicity of target activity. In addition, a learning-based energy saver was introduced to reduce energy consumption by adjusting sampling rates and the set of features adaptively. Based on the type and duration of the activity recorded, health feedback in terms of the calorie burned was given. The system could provide quantitative activity quality assessment and recommend future physical activity plans. Through extensive real-life testing, the system is shown to achieve an average recognition accuracy of 98.0% with a minimized energy expenditure.

关键词： physical activity tracking hybrid classifier health feedback

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Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

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Computer Systems Science & Engineering 2023年第5期45卷 1985-2000页

作者： S.Sudha A.Srinivasan T.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonam612001TamilnaduIndia

The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the *** feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent ***,this type of disorder can be only screened and identified through the processing of fundus *** different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of *** the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion *** CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)*** this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

关键词： CNN networking segmentation hybrid classifier data set crossvalidation fundus image

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Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

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Computer Systems Science & Engineering 2024年第6期48卷 1513-1536页

作者： Suneel Kumar Rath Madhusmita Sahu Shom Prasad Das Junali Jasmine Jena Chitralekha Jena Baseem Khan Ahmed Ali Pitshou Bokoro Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswar752054India Department of Computer Science and Engineering Birla Global UniversityBhubaneswar751029India School of Computer Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India School of Electrical Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India Department of Electrical and Computer Engineering Hawassa UniversityHawassaP.O.Box 05Ethiopia Center for Renewable Energy and Microgrids Huanjiang LaboratoryZhejiang UniversityShaoxing311816China Department of Electrical and Electronic Engineering Technology University of JohannesburgJohannesburg2028South Africa

Redundancy,correlation,feature irrelevance,and missing samples are just a few problems that make it difficult to analyze software defect ***,it might be challenging to maintain an even distribution of data relating to both defective and non-defective *** latter software class’s data are predominately present in the dataset in the majority of experimental *** objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect *** the successful feature selection approach,a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance,providing robustness in the classification *** overcome these problems and lessen their impact on the fault classification performance,authors carefully integrate effective feature selection with ensemble learning *** selection demonstrates that a significant area under the receiver operating curve(ROC)can be attributed to only a small subset of *** Greedy forward selection(GFS)technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the *** learners,such as random forests(RF)and the proposed average probability ensemble(APE),demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines(W-SVMs)and extreme learning machines(ELM).Furthermore,in the case of the NASA and Java datasets,the enhanced average probability ensemble model,which incorporates the Greedy forward selection technique with the average probability ensemble model,achieved remarkably high accuracy for the area under the *** approached a value of 1.0,indicating exceptional *** review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged *** addition,t

关键词： Ensemble classifier hybrid classifier software reliability prediction

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