目的探讨照护质量靶心模式对宗教信仰肿瘤患者生活质量的影响。方法对336例有宗教信仰肿瘤患者采用照护质量靶心模式进行护理,采用癌症患者生活质量测定量表(European organization for research and treatment of cancer quality of li...目的探讨照护质量靶心模式对宗教信仰肿瘤患者生活质量的影响。方法对336例有宗教信仰肿瘤患者采用照护质量靶心模式进行护理,采用癌症患者生活质量测定量表(European organization for research and treatment of cancer quality of life questionnaire core 30,EORTC-QLQ-C30)测评实施照护靶心模式前(入院时)后患者的生活质量。结果实施照护靶心模式后患者EORTC-QLQ-C30中的整体生活质量、功能量表得分高于实施前;症状量表、癌症症状维度得分低于实施前,实施前后比较,差异均有统计学意义(P<0.01)。结论在宗教信仰肿瘤患者护理中采用照护靶心模式可提高患者的生活质量。展开更多
In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network...In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.展开更多
An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-v...An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.展开更多
Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted...Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performa展开更多
For one class of Low-Density Parity-Check(LDPC)codes with low row weight in theirparity check matrix,a new Syndrome Decoding(SD)based on the heuristic Beam Search(BS),labeledas SD-BS,is put forward to improve the erro...For one class of Low-Density Parity-Check(LDPC)codes with low row weight in theirparity check matrix,a new Syndrome Decoding(SD)based on the heuristic Beam Search(BS),labeledas SD-BS,is put forward to improve the error performance.First,two observations are made andverified by simulation results.One is that in the SNR region of interest,the hard-decision on thecorrupted sequence yields only a handful of erroneous bits.The other is that the true error pattern forthe nonzero syndrome has a high probability to survive the competition in the BS,provided sufficientbeam width.Bearing these two points in mind,the decoding of LDPC codes is transformed into seekingan error pattern with the known decoding syndrome.Secondly,the effectiveness of SD-BS dependsclosely on how to evaluate the bit reliability.Enlightened by a bit-flipping definition in the existingliterature,a new metric is employed in the proposed SD-BS.The strength of SD-BS is demonstrated viaapplying it on the corrupted sequences directly and the decoding failures of the Belief Propagation(BP),respectively.展开更多
文摘目的探讨照护质量靶心模式对宗教信仰肿瘤患者生活质量的影响。方法对336例有宗教信仰肿瘤患者采用照护质量靶心模式进行护理,采用癌症患者生活质量测定量表(European organization for research and treatment of cancer quality of life questionnaire core 30,EORTC-QLQ-C30)测评实施照护靶心模式前(入院时)后患者的生活质量。结果实施照护靶心模式后患者EORTC-QLQ-C30中的整体生活质量、功能量表得分高于实施前;症状量表、癌症症状维度得分低于实施前,实施前后比较,差异均有统计学意义(P<0.01)。结论在宗教信仰肿瘤患者护理中采用照护靶心模式可提高患者的生活质量。
基金supported by the National Key Research and Development Program of China(2017YFB1401300,2017YFB1401304)the National Natural Science Foundation of China(61702211,L1724007,61902203)+3 种基金Hubei Provincial Science and Technology Program of China(2017AKA191)the Self-Determined Research Funds of Central China Normal University(CCNU)from the Colleges’Basic Research(CCNU17QD0004,CCNU17GF0002)the Natural Science Foundation of Shandong Province(ZR2017QF015)the Key Research and Development Plan–Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020101)。
文摘In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.
基金supported by Inha University Research Grant,Korea
文摘An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.
基金This research is financially supported by the Deanship of Scientific Research at King Khalid University under research grant number(RGP.2/202/43).
文摘Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performa
基金the National Science Foundation of China(No.60472104).
文摘For one class of Low-Density Parity-Check(LDPC)codes with low row weight in theirparity check matrix,a new Syndrome Decoding(SD)based on the heuristic Beam Search(BS),labeledas SD-BS,is put forward to improve the error performance.First,two observations are made andverified by simulation results.One is that in the SNR region of interest,the hard-decision on thecorrupted sequence yields only a handful of erroneous bits.The other is that the true error pattern forthe nonzero syndrome has a high probability to survive the competition in the BS,provided sufficientbeam width.Bearing these two points in mind,the decoding of LDPC codes is transformed into seekingan error pattern with the known decoding syndrome.Secondly,the effectiveness of SD-BS dependsclosely on how to evaluate the bit reliability.Enlightened by a bit-flipping definition in the existingliterature,a new metric is employed in the proposed SD-BS.The strength of SD-BS is demonstrated viaapplying it on the corrupted sequences directly and the decoding failures of the Belief Propagation(BP),respectively.
