Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as diabetes.Current dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several limitations.These limitations are due to the existence of a special user group and an imbalance of non-simple attributes.Making use of traditional dietary recommendation algorithm researches,we combine the Adaboost classifier with probabilistic matrix factorization.We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics.From this,we can make use of those features with strong influence while discarding those with little influence.After incorporating these changes into our approach,we evaluated our algorithm’s performance.Our results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a result.The algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of foods.Considering many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.

A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

In this paper,the clustering analysis is applied to the satellite image segmentation,and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power.The method firstly adopts the fuzzy C-means clustering(FCM)to obtain the satellite cloud image segmentation.Secondly,in the cloud image,we dispose the‘high-density connected’pixels in the same cloud clusters and the‘low-density connected’pixels in different cloud clusters.Therefore,we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge.Finally,using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters,thunderstorm cloud clusters are quickly and accurately identified.The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.

A Revised Satellite Cloud-Derived Wind Inversion Algorithm Based on Computer Cluster

In view of the satellite cloud-derived wind inversion has the characteristics of large scale,intensive computing and time-consuming serial inversion algorithm is very difficult to break through the bottleneck of efficiency.We proposed a parallel acceleration scheme of cloud-derived wind inversion algorithm based on MPI cluster parallel technique in this paper.The divide-and-conquer idea,assigning winds vector inversion tasks to each computing unit,is identified according to a certain strategy.Each computing unit executes the assigned tasks in parallel,namely divide-and-rule the inversion task,so as to reduce the efficiency bottleneck of long inversion time caused by serial time accumulation.In the scheme of parallel acceleration based on MPI cluster,an algorithm based on performance prediction is proposed to effectively implement load balance of MPI clusters.Through the comparative analysis of experiment data using the parallel scheme of this parallel technology framework,it shows that this parallel technology has a certain acceleration effect on the cloud-derived wind inversion algorithm.The speedup of the MPI-based parallel algorithm reaches 14.96,which achieved the expected estimate.At the same time,this paper also proposes an efficiency optimization algorithm for cloud-derived wind inversion.In the case that the inversion of wind vector accuracy loss is minimal,the optimized algorithm execution time can be up to 13 times faster.

Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning.This paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text data.Unsupervised K-Means and Affinity Propagation(AP)are two classical clustering algorithms.The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range.Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster analysis.Several semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect.The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.