Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from ...Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.展开更多
One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the in...One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathematical idea of fractal image compression. Then this idea is applied by iterated function system, iterative system functions and grayscale iterated function system down to general transformation. Mathematical formulation form is also provided on the digital image space, which deals with the computer. Next, this process has been revised to reduce the time required for image compression by excluding some parts of the image that have a specific milestone. The neural network algorithms have been applied on the process of compression (encryption). The experimental results are presented and the performance of the proposed algorithm is discussed. Finally, the comparison between filtered ranges method and self-organizing method is introduced.展开更多
The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on s...The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.展开更多
Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Org...Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.展开更多
Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characte...Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characteristics of surface water in the North of Tehran Rivers(NTRs),an essential water resource in a rapidly urbanizing region,using advanced clustering techniques,including Hierarchical Clustering Analysis(HCA),Fuzzy CMeans(FCM),Genetic Algorithm Fuzzy C-Means(GAFCM),and Self-Organizing Map(SOM).The research aims to address the scientific challenge of understanding spatial and temporal variability in water quality,focusing on physicochemical parameters,hydrochemical facies,and contamination sources.Water samples from six rivers collected over four seasons in 2020 were analyzed and classified into distinct clusters based on their chemical composition,revealing significant seasonal and spatial differences.Results showed that FCM and GAFCM consistently categorized the NTRs into two clusters during winter and spring and three in summer and autumn.These findings were supported by HCA and SOM,which identified clusters corresponding to specific river segments and contamination levels.The primary hydrochemical processes identified were mineral dissolution and weathering,with calcite,dolomite,and aragonite significantly influencing water chemistry.Additionally,human activities,such as wastewater discharge,were shown to contribute to elevated sulfate,nitrate,and phosphate concentrations,further corroborated by microbial analyses.By integrating HCA,FCM,and GAFCM with an artificial neural network(ANN)-based clustering method(SOM),this study provides a robust framework for evaluating surface water quality.The findings,supported by Gibbs diagrams,Hounslow ion ratio,and saturation indices,highlight the dominance of rock weathering and human impacts in shaping the hydrochemical dynamics of the NTRs.These insights contribute to the scientific understanding of water quality dynamics and offer practical guidance for sustainable water resource management and environmental protection in developing urban areas.展开更多
The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based t...The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based techniques with Self-Organizing Maps.展开更多
目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,...目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,其中跑量<300 km/月的36例(中低跑量组),跑量≥300 km/月的12例(高跑量组)。所有受试者均进行单侧无症状踝关节的MRI扫描,扫描序列包括T2^(*)mapping多回波自旋回波(spin echo,SE)序列矢状位、质子密度加权成像脂肪抑制(proton density-weighted imaging fat-saturated,PDWI-FS)序列矢状位、冠状位、横轴位以及T1加权脂肪抑制成像(T1-weighted imaging fat-saturated,T1WI-FS)序列横轴位。沿关节软骨轮廓边缘勾画距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨作为感兴趣区(region of interest,ROI),获得相应的T2^(*)值。采用线性回归分析软骨T2^(*)值与年龄、BMI、跑龄的相关性,采用独立样本t检验分析不同跑量及不同性别间的软骨T2^(*)值差异。结果(1)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面及距骨面软骨T2^(*)值在性别上的差异均具有统计学意义(P=0.001、P<0.001、P=0.002、P=0.008、P=0.004);(2)高跑量组的距骨穹窿、后距下关节跟骨面软骨T2^(*)值高于中低跑量组(P=0.014、0.023),不同跑量的跟骰关节跟骨面及骰骨面、后距下关节距骨面软骨T2^(*)值的差异均无统计学意义(P=0.987、0.072、0.724);(3)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面、距骨面软骨T2^(*)值均与BMI呈正相关(r=0.376、0.384、0.300、0.422、0.455,P=0.005、0.004、0.019、0.001、0.001)。结论在业余马拉松运动员这一跑步群体中,与中低跑量相比,高跑量更有可能导致距骨穹窿、后距下关节跟骨面软骨损伤;而与较低的BMI相比,高BMI增加了距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨损伤的风险。展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage...Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.展开更多
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.
文摘One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathematical idea of fractal image compression. Then this idea is applied by iterated function system, iterative system functions and grayscale iterated function system down to general transformation. Mathematical formulation form is also provided on the digital image space, which deals with the computer. Next, this process has been revised to reduce the time required for image compression by excluding some parts of the image that have a specific milestone. The neural network algorithms have been applied on the process of compression (encryption). The experimental results are presented and the performance of the proposed algorithm is discussed. Finally, the comparison between filtered ranges method and self-organizing method is introduced.
文摘The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.
基金Researcher Supporting Project number(RSPD2025R582),King Saud University,Riyadh,Saudi Arabia.
文摘Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.
文摘Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characteristics of surface water in the North of Tehran Rivers(NTRs),an essential water resource in a rapidly urbanizing region,using advanced clustering techniques,including Hierarchical Clustering Analysis(HCA),Fuzzy CMeans(FCM),Genetic Algorithm Fuzzy C-Means(GAFCM),and Self-Organizing Map(SOM).The research aims to address the scientific challenge of understanding spatial and temporal variability in water quality,focusing on physicochemical parameters,hydrochemical facies,and contamination sources.Water samples from six rivers collected over four seasons in 2020 were analyzed and classified into distinct clusters based on their chemical composition,revealing significant seasonal and spatial differences.Results showed that FCM and GAFCM consistently categorized the NTRs into two clusters during winter and spring and three in summer and autumn.These findings were supported by HCA and SOM,which identified clusters corresponding to specific river segments and contamination levels.The primary hydrochemical processes identified were mineral dissolution and weathering,with calcite,dolomite,and aragonite significantly influencing water chemistry.Additionally,human activities,such as wastewater discharge,were shown to contribute to elevated sulfate,nitrate,and phosphate concentrations,further corroborated by microbial analyses.By integrating HCA,FCM,and GAFCM with an artificial neural network(ANN)-based clustering method(SOM),this study provides a robust framework for evaluating surface water quality.The findings,supported by Gibbs diagrams,Hounslow ion ratio,and saturation indices,highlight the dominance of rock weathering and human impacts in shaping the hydrochemical dynamics of the NTRs.These insights contribute to the scientific understanding of water quality dynamics and offer practical guidance for sustainable water resource management and environmental protection in developing urban areas.
文摘The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based techniques with Self-Organizing Maps.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金financially supported by National Natural Science Foundation of China(32301800,32301923 and 32072053)Wheat Industrial Technology System of Shandong Province(SDAIT-01-01)Key Research and Development Project of Shandong Province(2022LZG002-4,2023LZGC009-4-4).
文摘Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.
