Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation...Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified fro...In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.展开更多
A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages ...A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31~33 dB with a rate of 0.2 bpp.展开更多
Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analys...Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.展开更多
In order to disclose present situation and problem of classified collection of municipal solid waste in Wanghua District of Fushun and ana- lyze its practicability, questionnaire was designed in this paper, random res...In order to disclose present situation and problem of classified collection of municipal solid waste in Wanghua District of Fushun and ana- lyze its practicability, questionnaire was designed in this paper, random research was adopted in Wanghua District, and statistic analysis of investi- gation result was conducted. This investigation could provide basis for popularizing classified collection of municipal solid waste in the whole nation.展开更多
For a long time,legal entities have developed and used crime prediction methodologies.The techniques are frequently updated based on crime evaluations and responses from scientific communities.There is a need to devel...For a long time,legal entities have developed and used crime prediction methodologies.The techniques are frequently updated based on crime evaluations and responses from scientific communities.There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level.Child maltreatment is not adequately addressed because children are voiceless.As a result,the possibility of developing a model for predicting child abuse was investigated in this study.Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events.The data set was balanced using the Borderline-SMOTE technique,and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse.The proposed approach successfully predicted crime types with 93%of accuracy,precision,recall,and F1-Score.The AUC value of the same was 0.989.However,when compared to the Extra Trees model(17.55),which is the second best,the proposed model’s execution time was significantly longer(476.63).We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse.The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model(BS-SC Model)would be effective in the real-time child abuse prediction and prevention process.展开更多
In this paper, based on hourly precipitation observations in 1977e2013 in the Beijing area, China, hourly precipitation in summer (June?August) is classified into three categories: light (below the 50th percentile val...In this paper, based on hourly precipitation observations in 1977e2013 in the Beijing area, China, hourly precipitation in summer (June?August) is classified into three categories: light (below the 50th percentile values), moderate (the 50th to 95th percentile values), and heavy (above the 95th percentile values). Results reveal that both light and moderate precipitation decreased significantly during the research period and thereby caused the decrease in summer totals. By contrast, pronounced trends failed to be detected in the heavy category. Since 2004, the contribution of heavy rainfall to the summer total precipitation in the urban area increased as compared to the suburban area, which is opposite to light rainfall. There are obvious differences in the diurnal variations of classified precipitation. Light precipitation shows a double peak structure in the early morning and at night, while moderate and heavy rainfall show a single peak at night. Light precipitation at the early morning peak time decreased significantly in the whole Beijing area. Compared with the suburban area, light precipitation in the urban area occurred less frequently whereas heavy precipitation occurred more frequently at evening peak time after 2004. The asymmetry of the rainfall is obvious, especially, for heavy precipitation. The asymmetry of heavy precipitation events in the urban area exhibits a significant increasing trend.展开更多
Protected areas contain most of Burkina Faso’s plant biodiversity which confer different benefits for the communities. However, the composition of some of them remains unknown. In a context of overexploitation and cl...Protected areas contain most of Burkina Faso’s plant biodiversity which confer different benefits for the communities. However, the composition of some of them remains unknown. In a context of overexploitation and climate change, it is important to have a detailed knowledge of the vegetation of forests that have not been studied, such as Péni Classified Forest (PCF) to develop better preservation protocols. The aim of this study is to contribute to the knowledge of the flora of Burkina Faso. Phytosociological surveys were carried out in 213 plots, have identified 475 species distributed in 321 genera and 87 families. We identified during this study 201 woody species representing 38% of the woody flora of Burkina Faso. 64% of this flora is confined to shrub savannahs and 61% to tree savannahs. Among the vegetation units, shrub savannahs and tree savannahs have respectively 56.21% and 44.67% of very rare species. Poaceae (11.90%), Fabaceae-Faboideae (11.27%) and Rubiaceae (6.26%) are the most dominant families. The dominant biological types of the flora are phanerophytes (42.32%) and therophytes (30.32%), and Sudanian species (20.63%) are the best represented. Logging is the most frequent disturbance factor (100%) in the PCF. The PCF is a particular ecosystem with a great diversity but subject to many disturbances. Actions to strengthen its protection are necessary.展开更多
Over the past years,with the increasing enrollment of high school,vocational schools are facing great challenge for their existence and development,concerning the low proficiency of the students and great gap among th...Over the past years,with the increasing enrollment of high school,vocational schools are facing great challenge for their existence and development,concerning the low proficiency of the students and great gap among them.The traditional English teaching mode which employs the same teaching contents,same teaching methods and teaching aims cannot satisfy students with different English levels.Therefore,in order to change the present situation,this paper proposes a new English teaching mode:classified English teaching.In the new mode,different students will be taught by different materials,different methods and with different aims.It can stimulate students'enthusiasm in English learning,and make every student develop appropriately.展开更多
Based on the data of the cases of severe convection weather such as hail,thunderstorm(thunderstorm gale)and short-time heavy precipitation in recent 10 years,the spatial and temporal distribution characteristics of di...Based on the data of the cases of severe convection weather such as hail,thunderstorm(thunderstorm gale)and short-time heavy precipitation in recent 10 years,the spatial and temporal distribution characteristics of different types of severe convection weather were analyzed.The results show that the frequency of severe convection weather tended to increase,of which short-time heavy precipitation and thunderstorm weather rose,and hail and thunderstorm gale weather decreased.Severe convection weather began to extend in late spring and early autumn.Typical cases were selected to analyze the evolution mechanism,and the conceptual models of severe convective weather caused by cold advection forcing,warm advection forcing and baroclinic frontogenesis were obtained.The key predictors for the potential prediction of severe convection weather were proposed,such as CAPE(convective available potential energy)for hail weather,UH index(maximum ascending helicity)for thunderstorm gale and PWV(precipitable water vapor)for short-time heavy precipitation.ERA5 data were used to get the forecast threshold of the key factor of classified severe convection weather,and it was verified that the threshold was available.Meanwhile,the causes of the error of failure cases were analyzed.For instance,the larger deviation of CAPE was caused by the 2 m deviation of temperature.Supplementary correction method and threshold were given to provide a reference for the objective forecast and early warning of severe convection weather.展开更多
Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better cl...Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However,current methods fail to identify precise solutions for constructing an ensemble classifier.In this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low error.In addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to forman ensemble classifier.Experiments on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.展开更多
Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and ...Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.展开更多
Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients.However,the highly complex cell origin involved in osteosarcoma(OS)limits the...Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients.However,the highly complex cell origin involved in osteosarcoma(OS)limits the utility of traditional bulk RNA sequencing for OS subclassification.Single-cell RNA sequencing(sc RNA-seq)holds great promise for identifying cell heterogeneity.However,this technique has rarely been used in the study of tumor subclassification.By analyzing sc RNA-seq data for six conventional OS and nine cancellous bone(CB)samples,we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell(CSC)-like subset,which allowed us to classify OS samples into three groups.The classification model was further examined using the TARGET dataset.Each subgroup of OS had different prognoses and possible drug sensitivities,and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment.In addition,we verified the classification model through IHC staining in 138 OS samples,revealing a worse prognosis for Group B patients.Furthermore,we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS.These findings provide a novel subclassification method based on sc RNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.展开更多
A telecommunication network used for the transmission of astronomical observation data,telescope remote control and other astronomical research purposes is a critical infrastructure.The monitoring and analysis of netw...A telecommunication network used for the transmission of astronomical observation data,telescope remote control and other astronomical research purposes is a critical infrastructure.The monitoring and analysis of network traffic,which help improve the network performance and the utilization of network resources,are a challenging task.The accurate identification of the astronomical data traffic will effectively improve transmission efficiency.In this paper,a classification method applied to types of traffic containing astronomical data using deep learning is proposed.The advantages of a convolutional neural network model in image classification are exploited to classify types of traffic containing astronomical data.The objective is to identify the mixed traffic in the network and accurately identify types of traffic containing astronomical data.The effectiveness of the model in improving classification accuracy is also discussed.Actual traffic data captured by Tcpdump and Wireshark are tested,and the experimental results indicate that the proposed method can accurately classify types of traffic containing astronomical data.展开更多
Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints...Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.展开更多
Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,...Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,the nodes within the communication range are involved in data transmission.Even the nodes that lie outside of the communication range are involved in the transmission of relay messages.However,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats inMANET.Hence,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data transmission.This paper proposed a security framework for the MANET network signature escrow scheme.The proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic technique.The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and classification.The developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep Learning.Furthermore,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper alert.The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model.The proposed model uses the CICIDS dataset for attack detection and classification.The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision Tree.The performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.展开更多
One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approac...One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.展开更多
文摘Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
文摘In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.
文摘A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31~33 dB with a rate of 0.2 bpp.
基金This work was supported by Tianjin Natural Science Foundation (No. 11JCYBJC06000) and Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20100032120019).
文摘Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.
文摘In order to disclose present situation and problem of classified collection of municipal solid waste in Wanghua District of Fushun and ana- lyze its practicability, questionnaire was designed in this paper, random research was adopted in Wanghua District, and statistic analysis of investi- gation result was conducted. This investigation could provide basis for popularizing classified collection of municipal solid waste in the whole nation.
文摘For a long time,legal entities have developed and used crime prediction methodologies.The techniques are frequently updated based on crime evaluations and responses from scientific communities.There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level.Child maltreatment is not adequately addressed because children are voiceless.As a result,the possibility of developing a model for predicting child abuse was investigated in this study.Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events.The data set was balanced using the Borderline-SMOTE technique,and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse.The proposed approach successfully predicted crime types with 93%of accuracy,precision,recall,and F1-Score.The AUC value of the same was 0.989.However,when compared to the Extra Trees model(17.55),which is the second best,the proposed model’s execution time was significantly longer(476.63).We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse.The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model(BS-SC Model)would be effective in the real-time child abuse prediction and prevention process.
基金This work was supported by the National Natural Science Foundation of China (41575094) and Special Scientific Research Fund of Meteorological Public Welfare Profession of China (GYHY201506014).
文摘In this paper, based on hourly precipitation observations in 1977e2013 in the Beijing area, China, hourly precipitation in summer (June?August) is classified into three categories: light (below the 50th percentile values), moderate (the 50th to 95th percentile values), and heavy (above the 95th percentile values). Results reveal that both light and moderate precipitation decreased significantly during the research period and thereby caused the decrease in summer totals. By contrast, pronounced trends failed to be detected in the heavy category. Since 2004, the contribution of heavy rainfall to the summer total precipitation in the urban area increased as compared to the suburban area, which is opposite to light rainfall. There are obvious differences in the diurnal variations of classified precipitation. Light precipitation shows a double peak structure in the early morning and at night, while moderate and heavy rainfall show a single peak at night. Light precipitation at the early morning peak time decreased significantly in the whole Beijing area. Compared with the suburban area, light precipitation in the urban area occurred less frequently whereas heavy precipitation occurred more frequently at evening peak time after 2004. The asymmetry of the rainfall is obvious, especially, for heavy precipitation. The asymmetry of heavy precipitation events in the urban area exhibits a significant increasing trend.
文摘Protected areas contain most of Burkina Faso’s plant biodiversity which confer different benefits for the communities. However, the composition of some of them remains unknown. In a context of overexploitation and climate change, it is important to have a detailed knowledge of the vegetation of forests that have not been studied, such as Péni Classified Forest (PCF) to develop better preservation protocols. The aim of this study is to contribute to the knowledge of the flora of Burkina Faso. Phytosociological surveys were carried out in 213 plots, have identified 475 species distributed in 321 genera and 87 families. We identified during this study 201 woody species representing 38% of the woody flora of Burkina Faso. 64% of this flora is confined to shrub savannahs and 61% to tree savannahs. Among the vegetation units, shrub savannahs and tree savannahs have respectively 56.21% and 44.67% of very rare species. Poaceae (11.90%), Fabaceae-Faboideae (11.27%) and Rubiaceae (6.26%) are the most dominant families. The dominant biological types of the flora are phanerophytes (42.32%) and therophytes (30.32%), and Sudanian species (20.63%) are the best represented. Logging is the most frequent disturbance factor (100%) in the PCF. The PCF is a particular ecosystem with a great diversity but subject to many disturbances. Actions to strengthen its protection are necessary.
文摘Over the past years,with the increasing enrollment of high school,vocational schools are facing great challenge for their existence and development,concerning the low proficiency of the students and great gap among them.The traditional English teaching mode which employs the same teaching contents,same teaching methods and teaching aims cannot satisfy students with different English levels.Therefore,in order to change the present situation,this paper proposes a new English teaching mode:classified English teaching.In the new mode,different students will be taught by different materials,different methods and with different aims.It can stimulate students'enthusiasm in English learning,and make every student develop appropriately.
基金Supported by the Open-end Funds of Key Laboratory for Disaster Prevention and Mitigation of Qinghai Province(QFZ-2021-Z04)。
文摘Based on the data of the cases of severe convection weather such as hail,thunderstorm(thunderstorm gale)and short-time heavy precipitation in recent 10 years,the spatial and temporal distribution characteristics of different types of severe convection weather were analyzed.The results show that the frequency of severe convection weather tended to increase,of which short-time heavy precipitation and thunderstorm weather rose,and hail and thunderstorm gale weather decreased.Severe convection weather began to extend in late spring and early autumn.Typical cases were selected to analyze the evolution mechanism,and the conceptual models of severe convective weather caused by cold advection forcing,warm advection forcing and baroclinic frontogenesis were obtained.The key predictors for the potential prediction of severe convection weather were proposed,such as CAPE(convective available potential energy)for hail weather,UH index(maximum ascending helicity)for thunderstorm gale and PWV(precipitable water vapor)for short-time heavy precipitation.ERA5 data were used to get the forecast threshold of the key factor of classified severe convection weather,and it was verified that the threshold was available.Meanwhile,the causes of the error of failure cases were analyzed.For instance,the larger deviation of CAPE was caused by the 2 m deviation of temperature.Supplementary correction method and threshold were given to provide a reference for the objective forecast and early warning of severe convection weather.
基金supported in part by the Anhui Provincial Natural Science Founda-tion[1908085QG298,1908085MG232]the National Nature Science Foundation of China[91546108,61806068]+5 种基金the National Social Science Foundation of China[21BTJ002]the Anhui Provincial Science:and Technology Major Projects Grant[201903a05020020]the Fundamental Research Funds for the Central Universities[Z2019HGTA0053,JZ2019HG BZ0128]the Humanities and Social Science Fund of Ministry of Education of China[20YJA790021]the Major Project of Philosophy and Social Science Planning of Zhejiang Province[22YJRC07ZD]the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-Making(Hefei University of Technology),Ministry of Education.
文摘Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However,current methods fail to identify precise solutions for constructing an ensemble classifier.In this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low error.In addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to forman ensemble classifier.Experiments on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.
文摘Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.
基金National Natural Science Foundation of China(Nos.31970663 and 82173028 to J.X.,No.81874180 to T.W.,No.81201556 to W.Z.,No.82072971 to H.W.and No.81972505 to Z.W.)。
文摘Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients.However,the highly complex cell origin involved in osteosarcoma(OS)limits the utility of traditional bulk RNA sequencing for OS subclassification.Single-cell RNA sequencing(sc RNA-seq)holds great promise for identifying cell heterogeneity.However,this technique has rarely been used in the study of tumor subclassification.By analyzing sc RNA-seq data for six conventional OS and nine cancellous bone(CB)samples,we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell(CSC)-like subset,which allowed us to classify OS samples into three groups.The classification model was further examined using the TARGET dataset.Each subgroup of OS had different prognoses and possible drug sensitivities,and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment.In addition,we verified the classification model through IHC staining in 138 OS samples,revealing a worse prognosis for Group B patients.Furthermore,we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS.These findings provide a novel subclassification method based on sc RNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.
基金supported by the National Key R&D Program of China(Nos.2021YFC2203502 and 2022YFF0711502)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)+6 种基金the National Natural Science Foundation of China(NSFC)(12173077 and 12003062)the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region(2022D14020)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(Grant No.PTYQ2022YZZD01)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)supported by Astronomical Big Data Joint Research Centerco-founded by National Astronomical Observatories,Chinese Academy of Sciences。
文摘A telecommunication network used for the transmission of astronomical observation data,telescope remote control and other astronomical research purposes is a critical infrastructure.The monitoring and analysis of network traffic,which help improve the network performance and the utilization of network resources,are a challenging task.The accurate identification of the astronomical data traffic will effectively improve transmission efficiency.In this paper,a classification method applied to types of traffic containing astronomical data using deep learning is proposed.The advantages of a convolutional neural network model in image classification are exploited to classify types of traffic containing astronomical data.The objective is to identify the mixed traffic in the network and accurately identify types of traffic containing astronomical data.The effectiveness of the model in improving classification accuracy is also discussed.Actual traffic data captured by Tcpdump and Wireshark are tested,and the experimental results indicate that the proposed method can accurately classify types of traffic containing astronomical data.
文摘Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.
基金Deanship of Scientific Research at Umm Al-Qura University,Grant Code,funds this research:22UQU4281768DSR05.
文摘Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,the nodes within the communication range are involved in data transmission.Even the nodes that lie outside of the communication range are involved in the transmission of relay messages.However,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats inMANET.Hence,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data transmission.This paper proposed a security framework for the MANET network signature escrow scheme.The proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic technique.The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and classification.The developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep Learning.Furthermore,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper alert.The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model.The proposed model uses the CICIDS dataset for attack detection and classification.The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision Tree.The performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.
文摘One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.