Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services...Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.展开更多
Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults ...Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.展开更多
BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst ...BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst is evaluated by microscopic inspection,and the result is only about 30%-40%,and the method of predicting live birth from the blastocyst image is unknown.Live births correlate with several clinical conventional embryo evaluation parameters(CEE),such as maternal age.Therefore,it is necessary to develop artificial intelligence(AI)that combines blastocyst images and CEE to predict live births.AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option.We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.RESULTS The live birth rate was 0.387(=1587/4104 cases).The number of independent clinical information for predicting live birth is 10,which significantly avoids multicollinearity.A single AI classifier is composed of ten layers of convolutional neural networks,and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value.The accuracy,sensitivity,specificity,negative predictive value,positive predictive value,Youden J index,and area under the curve values for predicting live birth are 0.743,0.638,0.789,0.831,0.573,0.427,and 0.740,respectively.The optimal cut-off point of the receiver operator characteristic curve is 0.207.CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict.Artificial intelligence may make progress in assisted reproductive technology.展开更多
The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live sel...The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.展开更多
目的:利用机器学习算法预测影响脑卒中患者日常生活自理能力(activities of daily living,ADL)的风险因素,为其ADL管理决策提供参考。方法:对2015年1月—2019年2月在南京医科大学附属第一医院康复医学中心治疗的423例脑卒中患者进行回...目的:利用机器学习算法预测影响脑卒中患者日常生活自理能力(activities of daily living,ADL)的风险因素,为其ADL管理决策提供参考。方法:对2015年1月—2019年2月在南京医科大学附属第一医院康复医学中心治疗的423例脑卒中患者进行回顾性分析。根据Barthel指数(Barthel index,BI)评定量表,将患者分为ADL较好组(BI≥60分)和ADL较差组(BI<60分),并进行数据预处理。采用共线性诊断及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)筛选特征变量。选择逻辑回归、支持向量机、随机森林(random forest,RF)、极限梯度提升及K最近邻5种机器学习算法进行预测建模,十倍交叉验证后,使用受试者工作特征曲线、受试者工作特征曲线下面积(area under curve,AUC)、精确召回率曲线、精确召回率曲线下的面积(area under the precision recall curve,PRAUC)、准确率、灵敏度、特异度分别对模型进行综合评估,引入Shapley加性解释(Shapley additive explanation,SHAP)对最优机器学习模型进行可解释化处理。结果:经LASSO回归分析后,确定16个特征变量用于构建机器学习模型。RF模型具有最高的AUC(0.74)、PRAUC(0.64)、准确率(0.97)、灵敏度(0.75)和特异度(0.97)。SHAP模型解释性分析显示,对ADL贡献度前5的特征中,Brunnstrom分期(下肢)的影响最为显著,其次是Brunnstrom分期(上肢)、D-二聚体、血清白蛋白水平及年龄。结论:RF模型预测脑卒中患者ADL的效能最优,为脑卒中患者ADL管理决策提供了有价值的参考。展开更多
基金The authors extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group no KSRG-2022-030.
文摘Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.
基金supported by theResearchers Supporting Project No.RSP-2021/14,King Saud University,Riyadh,Saudi Arabia.
文摘Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.
文摘BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst is evaluated by microscopic inspection,and the result is only about 30%-40%,and the method of predicting live birth from the blastocyst image is unknown.Live births correlate with several clinical conventional embryo evaluation parameters(CEE),such as maternal age.Therefore,it is necessary to develop artificial intelligence(AI)that combines blastocyst images and CEE to predict live births.AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option.We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.RESULTS The live birth rate was 0.387(=1587/4104 cases).The number of independent clinical information for predicting live birth is 10,which significantly avoids multicollinearity.A single AI classifier is composed of ten layers of convolutional neural networks,and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value.The accuracy,sensitivity,specificity,negative predictive value,positive predictive value,Youden J index,and area under the curve values for predicting live birth are 0.743,0.638,0.789,0.831,0.573,0.427,and 0.740,respectively.The optimal cut-off point of the receiver operator characteristic curve is 0.207.CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict.Artificial intelligence may make progress in assisted reproductive technology.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R77)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR52).
文摘The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.
文摘目的:利用机器学习算法预测影响脑卒中患者日常生活自理能力(activities of daily living,ADL)的风险因素,为其ADL管理决策提供参考。方法:对2015年1月—2019年2月在南京医科大学附属第一医院康复医学中心治疗的423例脑卒中患者进行回顾性分析。根据Barthel指数(Barthel index,BI)评定量表,将患者分为ADL较好组(BI≥60分)和ADL较差组(BI<60分),并进行数据预处理。采用共线性诊断及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)筛选特征变量。选择逻辑回归、支持向量机、随机森林(random forest,RF)、极限梯度提升及K最近邻5种机器学习算法进行预测建模,十倍交叉验证后,使用受试者工作特征曲线、受试者工作特征曲线下面积(area under curve,AUC)、精确召回率曲线、精确召回率曲线下的面积(area under the precision recall curve,PRAUC)、准确率、灵敏度、特异度分别对模型进行综合评估,引入Shapley加性解释(Shapley additive explanation,SHAP)对最优机器学习模型进行可解释化处理。结果:经LASSO回归分析后,确定16个特征变量用于构建机器学习模型。RF模型具有最高的AUC(0.74)、PRAUC(0.64)、准确率(0.97)、灵敏度(0.75)和特异度(0.97)。SHAP模型解释性分析显示,对ADL贡献度前5的特征中,Brunnstrom分期(下肢)的影响最为显著,其次是Brunnstrom分期(上肢)、D-二聚体、血清白蛋白水平及年龄。结论:RF模型预测脑卒中患者ADL的效能最优,为脑卒中患者ADL管理决策提供了有价值的参考。
