The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategi...The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.展开更多
Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discoveri...Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.展开更多
Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the intro...Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.展开更多
In this paper,we propose an equal interval range approximation and expandinglearning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra-ditional BP algorithm,this learning rule requires...In this paper,we propose an equal interval range approximation and expandinglearning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra-ditional BP algorithm,this learning rule requires the output activations interval between themaximum target output node and other nodes to exceed a given equal interval range for eachtraining input pattern,thus it can train networks faster in much lower calculation cost andmay avoid the occurrences ot reversed target output and overlearning,hence it can improve thenetwork’s generalization abilities in pattern recognitions.Through gradually expanding of theinterval range,this learning rule can also enable the network to learn its targets more accuratelyin less additional training iterations.Finally,we apply this algorithm in network training inEEG detection,and the experimental results have shown the above advantages of the proposedalgorithm.展开更多
An operating rule classification system based on learning classifier system (LCS), which learns through credit assignment (bucket brigade algorithm, BBA) and rule discovery (genetic algorithm, GA), is establishe...An operating rule classification system based on learning classifier system (LCS), which learns through credit assignment (bucket brigade algorithm, BBA) and rule discovery (genetic algorithm, GA), is established to extract water-supply reservoir operating rules. The proposed system acquires an online identification rate of 95% for training samples and an offline rate of 85% for testing samples in a case study. The performances of the rule classification system are discussed from the rationality of the obtained rules, the impact of training samples on rule extraction, and a comparison between the rule classification system and the artificial neural network (ANN). The results indicate that the LCS is feasible and effective for the system to obtain the reservoir supply operating rules.展开更多
In the mobile learning system,it is important to adapt to mobile devices.Most of mobile learning systems are not quickly suitable for mobile devices.In order to provide adaptive mobile services,the approach for adapta...In the mobile learning system,it is important to adapt to mobile devices.Most of mobile learning systems are not quickly suitable for mobile devices.In order to provide adaptive mobile services,the approach for adaptation is proposed in this paper.Firstly,context of mobile devices and its influence on mobile learning system are analized and business rules based on these analysis are presented.Then,using the approach,the mobile learning system is constructed.The example implies this approach can adapt the mobile service to the mobile devices flexibly.展开更多
An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audio...An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.展开更多
AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a...AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.展开更多
Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the propo...Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.展开更多
<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human bei...<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human beings has been endangering by the spreading of </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">pneu</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">monia</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">learning” technique and develop</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">ed</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;"> a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;"> </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">server for the new predictor has been well established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/">http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/</a>, by which the majority of experimental scientists can easily get their desired data.</span> </p> </span>展开更多
Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, kno...Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.展开更多
<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span>...<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>展开更多
Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To ...Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.展开更多
The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological proc...The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.展开更多
The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral ...The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.展开更多
基金supported by the National Natural Science Foundation of China(62173333)Australian Research Council Discovery Program(DP200101199)。
文摘The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number RI-44-0444.
文摘Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.
基金National College Students’Training Programs of Innovation and Entrepreneurship,Grant/Award Number:S202210022060the CACMS Innovation Fund,Grant/Award Number:CI2021A00512the National Nature Science Foundation of China under Grant,Grant/Award Number:62206021。
文摘Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.
文摘In this paper,we propose an equal interval range approximation and expandinglearning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra-ditional BP algorithm,this learning rule requires the output activations interval between themaximum target output node and other nodes to exceed a given equal interval range for eachtraining input pattern,thus it can train networks faster in much lower calculation cost andmay avoid the occurrences ot reversed target output and overlearning,hence it can improve thenetwork’s generalization abilities in pattern recognitions.Through gradually expanding of theinterval range,this learning rule can also enable the network to learn its targets more accuratelyin less additional training iterations.Finally,we apply this algorithm in network training inEEG detection,and the experimental results have shown the above advantages of the proposedalgorithm.
文摘An operating rule classification system based on learning classifier system (LCS), which learns through credit assignment (bucket brigade algorithm, BBA) and rule discovery (genetic algorithm, GA), is established to extract water-supply reservoir operating rules. The proposed system acquires an online identification rate of 95% for training samples and an offline rate of 85% for testing samples in a case study. The performances of the rule classification system are discussed from the rationality of the obtained rules, the impact of training samples on rule extraction, and a comparison between the rule classification system and the artificial neural network (ANN). The results indicate that the LCS is feasible and effective for the system to obtain the reservoir supply operating rules.
文摘In the mobile learning system,it is important to adapt to mobile devices.Most of mobile learning systems are not quickly suitable for mobile devices.In order to provide adaptive mobile services,the approach for adaptation is proposed in this paper.Firstly,context of mobile devices and its influence on mobile learning system are analized and business rules based on these analysis are presented.Then,using the approach,the mobile learning system is constructed.The example implies this approach can adapt the mobile service to the mobile devices flexibly.
基金supported by the grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Chungbuk BIT Research-Oriented University Consortium)
文摘An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.
基金Supported by National Institute of General Medical Sciences of the National Institutes of Health,No.R01GM100387
文摘AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.
基金Supported by the National Natural Science Foundation of Chinathe Science foundation of Guangxi Educational Administration
文摘Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.
文摘<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human beings has been endangering by the spreading of </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">pneu</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">monia</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">learning” technique and develop</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">ed</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;"> a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;"> </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">server for the new predictor has been well established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/">http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/</a>, by which the majority of experimental scientists can easily get their desired data.</span> </p> </span>
文摘Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.
文摘<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>
文摘Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.
文摘The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.
文摘The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.