Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which ar...Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.展开更多
为精简IDS产生的海量告警信息、降低IDS的误报率,提出一种基于主动D-S理论分类器的告警校验方法。该方法反映影响校验结果的各因素之间的概率关系,有效地解决了传统校验方法中存在的校验过于教条的问题,并能够对攻击行为进行学习来提高...为精简IDS产生的海量告警信息、降低IDS的误报率,提出一种基于主动D-S理论分类器的告警校验方法。该方法反映影响校验结果的各因素之间的概率关系,有效地解决了传统校验方法中存在的校验过于教条的问题,并能够对攻击行为进行学习来提高校验的准确性。使用MIT Lincoln Lab提供的DARPA 2000入侵检测攻击场景数据集LLDOS1.0对该方法进行性能测试,实验结果验证了该方法的有效性。展开更多
针对传统D-S证据理论中基于识别率和误识率构造的基本概率赋值函数(Basic Probability Assignment,BPA)没有考虑训练样本分布的缺点,提出了一种将整体错误率分配给除了正确判别命题以外各个焦元的BPA构造新方法.针对传统D-S证据理论中...针对传统D-S证据理论中基于识别率和误识率构造的基本概率赋值函数(Basic Probability Assignment,BPA)没有考虑训练样本分布的缺点,提出了一种将整体错误率分配给除了正确判别命题以外各个焦元的BPA构造新方法.针对传统D-S证据理论中所采用的基于正交和运算的合成规则不能融合矛盾证据的缺陷,提出一种能融合矛盾证据的大概率赋值法.在此改进D-S证据理论的基础上,给出了两分类器决策层融合流程和多分类器决策层融合系统.在ORL和Yale数据库上的实验结果表明,对几种典型分类器的决策层融合提高了系统人脸识别的正确率,且改进D-S证据理论比传统D-S和投票融合方法的正确率更高.展开更多
针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击...针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击的可能性和用户的合法性,提高了检测的准确性。实验及分析表明上述方法具有较好的准确性,能够针对SPIT进行有效的检测。展开更多
文摘Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.
文摘为精简IDS产生的海量告警信息、降低IDS的误报率,提出一种基于主动D-S理论分类器的告警校验方法。该方法反映影响校验结果的各因素之间的概率关系,有效地解决了传统校验方法中存在的校验过于教条的问题,并能够对攻击行为进行学习来提高校验的准确性。使用MIT Lincoln Lab提供的DARPA 2000入侵检测攻击场景数据集LLDOS1.0对该方法进行性能测试,实验结果验证了该方法的有效性。
文摘针对传统D-S证据理论中基于识别率和误识率构造的基本概率赋值函数(Basic Probability Assignment,BPA)没有考虑训练样本分布的缺点,提出了一种将整体错误率分配给除了正确判别命题以外各个焦元的BPA构造新方法.针对传统D-S证据理论中所采用的基于正交和运算的合成规则不能融合矛盾证据的缺陷,提出一种能融合矛盾证据的大概率赋值法.在此改进D-S证据理论的基础上,给出了两分类器决策层融合流程和多分类器决策层融合系统.在ORL和Yale数据库上的实验结果表明,对几种典型分类器的决策层融合提高了系统人脸识别的正确率,且改进D-S证据理论比传统D-S和投票融合方法的正确率更高.
文摘针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击的可能性和用户的合法性,提高了检测的准确性。实验及分析表明上述方法具有较好的准确性,能够针对SPIT进行有效的检测。
