The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi...The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.展开更多
BACKGROUND Due to the prolonged life expectancy and increased risk of colorectal cancer(CRC)among patients with human immunodeficiency virus(HIV)infection,the prognosis and pathological features of CRC in HIV-positive...BACKGROUND Due to the prolonged life expectancy and increased risk of colorectal cancer(CRC)among patients with human immunodeficiency virus(HIV)infection,the prognosis and pathological features of CRC in HIV-positive patients require examination.AIM To compare the differences in oncological features,surgical safety,and prognosis between patients with and without HIV infection who have CRC at the same tumor stage and site.METHODS In this retrospective study,we collected data from HIV-positive and-negative patients who underwent radical resection for CRC.Using random stratified sampling,24 HIV-positive and 363 HIV-negative patients with colorectal adenocarcinoma after radical resection were selected.Using propensity score matching,we selected 72 patients,matched 1:2(HIV-positive:negative=24:48).Differences in basic characteristics,HIV acquisition,perioperative serological indicators,surgical safety,oncological features,and long-term prognosis were compared between the two groups.RESULTS Fewer patients with HIV infection underwent chemotherapy compared to patients without.HIV-positive patients had fewer preoperative and postoperative leukocytes,fewer preoperative lymphocytes,lower carcinoembryonic antigen levels,more intraoperative blood loss,more metastatic lymph nodes,higher node stage,higher tumor node metastasis stage,shorter overall survival,and shorter progression-free survival compared to patients who were HIV-negative.CONCLUSION Compared with CRC patients who are HIV-negative,patients with HIV infection have more metastatic lymph nodes and worse long-term survival after surgery.Standard treatment options for HIV-positive patients with CRC should be explored.展开更多
Feature selection is an important problem in pattern classification systems. High dimension fisher criterion（HDF） is a good indicator of class separability. However, calculating the high dimension fisher ratio is di...Feature selection is an important problem in pattern classification systems. High dimension fisher criterion（HDF） is a good indicator of class separability. However, calculating the high dimension fisher ratio is difficult. A new feature selection method, called fisher-and-correlation （FC）, is proposed. The proposed method is combining fisher criterion and correlation criterion based on the analysis of feature relevance and redundancy. The proposed methodology is tested in five different classification applications. The presented resuits confirm that FC performs as well as HDF does at much lower computational complexity.展开更多
Five density functionals, CAM-B3LYP, LC-ωPBE, MN12SX, N12SX and ωB97XD, in connection with the Def2TZVP basis set were assessed together with the SMD solvation model for the calculation of the molecular and chemical...Five density functionals, CAM-B3LYP, LC-ωPBE, MN12SX, N12SX and ωB97XD, in connection with the Def2TZVP basis set were assessed together with the SMD solvation model for the calculation of the molecular and chemical reactivity properties of the Cholecystokinin peptide hormone (CCK-8) in the presence of water. All the chemical reactivity descriptors for the systems were calculated via Conceptual Density Functional Theory (CDFT). The potential bioavailability and druggability as well as the bioactivity scoresfor CCK-8 were predicted through different methodologies already reported in the literature which have been previously validated during the study of different peptidic systems. The conclusion was that the CCK-8 peptide will be moderately bioactive regarding all the interactions.展开更多
In order to reduce redundant empty bin capacity arrangement mechanism for mean shift tracking objects in the probability representation, we present a new color feature In the proposed mechanism, the important optimal ...In order to reduce redundant empty bin capacity arrangement mechanism for mean shift tracking objects in the probability representation, we present a new color feature In the proposed mechanism, the important optimal color, or we call it optimal color vector, is clustered by closing Euclidean distance which happens inside the original RGB color 3-D spatial domain. After obtaining clustering colors from the reference image RGB spatial domain, novel clustering groups substitute for original color data. So the new color substitution distribution is as similar as the original one. And then target region in the candidate frame is mapped by the constructed optimal clustering colors and the cluster Indices. In the final, mean shift algorithm gives a performance in the new optimal color distribution. Comparison under the same circumstance between the proposed algorithm and conventional mean shift algorithm shows that the former has a certain advantage in computation cost.展开更多
In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manua...In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.展开更多
Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the mach...Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.展开更多
For realizing of long text information hiding and covert communication, a binary watermark sequence was obtained firstly from a text file and encoded by a redundant encoding method. Then, two neighboring blocks were s...For realizing of long text information hiding and covert communication, a binary watermark sequence was obtained firstly from a text file and encoded by a redundant encoding method. Then, two neighboring blocks were selected at each time from the Hilbert scanning sequence of carrier image blocks, and transformed by 1-level discrete wavelet transformation (DWT). And then the double block based JNDs (just noticeable difference) were calculated with a visual model. According to the different codes of each two watermark bits, the average values of two corresponding detail sub-bands were modified by using one of JNDs to hide information into carrier image. The experimental results show that the hidden information is invisible to human eyes, and the algorithm is robust to some common image processing operations. The conclusion is that the algorithm is effective and practical.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154,62202147)the key Research and Development Program of Hubei Province,China(Grant No.2023BEB024).
文摘The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.
基金Supported by General Plan of the Future Medical Youth Innovation Team Development Support Plan of Chongqing Medical University,No.03030299QC-W0007.
文摘BACKGROUND Due to the prolonged life expectancy and increased risk of colorectal cancer(CRC)among patients with human immunodeficiency virus(HIV)infection,the prognosis and pathological features of CRC in HIV-positive patients require examination.AIM To compare the differences in oncological features,surgical safety,and prognosis between patients with and without HIV infection who have CRC at the same tumor stage and site.METHODS In this retrospective study,we collected data from HIV-positive and-negative patients who underwent radical resection for CRC.Using random stratified sampling,24 HIV-positive and 363 HIV-negative patients with colorectal adenocarcinoma after radical resection were selected.Using propensity score matching,we selected 72 patients,matched 1:2(HIV-positive:negative=24:48).Differences in basic characteristics,HIV acquisition,perioperative serological indicators,surgical safety,oncological features,and long-term prognosis were compared between the two groups.RESULTS Fewer patients with HIV infection underwent chemotherapy compared to patients without.HIV-positive patients had fewer preoperative and postoperative leukocytes,fewer preoperative lymphocytes,lower carcinoembryonic antigen levels,more intraoperative blood loss,more metastatic lymph nodes,higher node stage,higher tumor node metastasis stage,shorter overall survival,and shorter progression-free survival compared to patients who were HIV-negative.CONCLUSION Compared with CRC patients who are HIV-negative,patients with HIV infection have more metastatic lymph nodes and worse long-term survival after surgery.Standard treatment options for HIV-positive patients with CRC should be explored.
基金the Ministerial Level Advanced Research Foundation(66830202)
文摘Feature selection is an important problem in pattern classification systems. High dimension fisher criterion（HDF） is a good indicator of class separability. However, calculating the high dimension fisher ratio is difficult. A new feature selection method, called fisher-and-correlation （FC）, is proposed. The proposed method is combining fisher criterion and correlation criterion based on the analysis of feature relevance and redundancy. The proposed methodology is tested in five different classification applications. The presented resuits confirm that FC performs as well as HDF does at much lower computational complexity.
文摘Five density functionals, CAM-B3LYP, LC-ωPBE, MN12SX, N12SX and ωB97XD, in connection with the Def2TZVP basis set were assessed together with the SMD solvation model for the calculation of the molecular and chemical reactivity properties of the Cholecystokinin peptide hormone (CCK-8) in the presence of water. All the chemical reactivity descriptors for the systems were calculated via Conceptual Density Functional Theory (CDFT). The potential bioavailability and druggability as well as the bioactivity scoresfor CCK-8 were predicted through different methodologies already reported in the literature which have been previously validated during the study of different peptidic systems. The conclusion was that the CCK-8 peptide will be moderately bioactive regarding all the interactions.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-C1090-1121-0010)The Brain Korea21Project in 2012
文摘In order to reduce redundant empty bin capacity arrangement mechanism for mean shift tracking objects in the probability representation, we present a new color feature In the proposed mechanism, the important optimal color, or we call it optimal color vector, is clustered by closing Euclidean distance which happens inside the original RGB color 3-D spatial domain. After obtaining clustering colors from the reference image RGB spatial domain, novel clustering groups substitute for original color data. So the new color substitution distribution is as similar as the original one. And then target region in the candidate frame is mapped by the constructed optimal clustering colors and the cluster Indices. In the final, mean shift algorithm gives a performance in the new optimal color distribution. Comparison under the same circumstance between the proposed algorithm and conventional mean shift algorithm shows that the former has a certain advantage in computation cost.
基金This work was supported by Taif University Researchers Supporting Project(TURSP)under number(TURSP-2020/73)Taif University,Taif,Saudi Arabia。
文摘In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.
文摘Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.
文摘For realizing of long text information hiding and covert communication, a binary watermark sequence was obtained firstly from a text file and encoded by a redundant encoding method. Then, two neighboring blocks were selected at each time from the Hilbert scanning sequence of carrier image blocks, and transformed by 1-level discrete wavelet transformation (DWT). And then the double block based JNDs (just noticeable difference) were calculated with a visual model. According to the different codes of each two watermark bits, the average values of two corresponding detail sub-bands were modified by using one of JNDs to hide information into carrier image. The experimental results show that the hidden information is invisible to human eyes, and the algorithm is robust to some common image processing operations. The conclusion is that the algorithm is effective and practical.
