Pearson Similarity Research Articles

A Segmentation Model of ECU Excitation Signal Based on Characteristic Parameters.

According to the basic structure and working principle of the excitation signal sensors of a diesel engine electronic control unit (ECU), a segmentation model of an ECU excitation signal based on characteristic parameters (ESCP-SM) is proposed. In the ESCP-SM, the ECU excitation signal is divided into several parts, and each part has its characteristic parameters model. By using the same global parameters and strictly controlling each part’s proportional parameters, the ESCP-SM can achieve signal alignment and dynamic frequency modulation. Based on the simulation experiment, spectrum analysis proves that this modeling method ensures that the original signal’s effective information is not lost. Pearson similarity analysis shows that the similarity between the simulation signal and practical signal reaches 74%, exhibiting strong correlation. In addition, we set up a physical testing environment. ESCP-SM is realized based on virtual instrument technology, and provides excitation signals for a Komatsu 8 ECU. By modifying the parameter configuration, the ECU can drive the injector to work correctly.

SISTEM PEREKOMENDASI DENGAN SINGULAR VALUE DECOMPOSITION DAN TEKNIK SIMILARITAS PEARSON CORRELATION

The growth of e-commerce has resulted in massive product information and huge volumes of data. This results in data overload problems. In the case of e-commerce, consumers or users spend a lot of time choosing the goods they need. The urgent question to be answered at this time is how to provide solutions related to intelligent information restrictions so that the existing information is truly information that is by preferences and needs. This research performs information filtering by applying the singular value decomposition method and the Pearson similarity technique to the book recommendation system. The data used is the Book-Crossing Dataset which is the reference dataset for many research recommendation systems. The resulting recommendations are then compared with e-commerce recommendations such as amazom.com. Based on the results of the study obtained data that the results of the recommendations in this study are very good and accurate.

Uneven genotypic diversity of Escherichia coli in fecal sources limits the performance of a library-dependent method of microbial source tracking on the southwestern French Atlantic coast.

To develop a library-dependent method of tracking fecal sources of contamination of beaches on the Atlantic coast of southwestern France, a library of 6368 Escherichia coli isolates was constructed from samples of feces, from 40 known human or animal sources collected in the vicinity of Arcachon Bay in 2010, and in French Basque Country, Landes, and Béarn, between 2017 and 2018. Different schemes of source identification were tested: use of the complete or filtered reference library; characterization of the isolates by genotypic or proteomic profiling based on ERIC-PCR or MALDI-TOF mass spectrometry, respectively; isolate by isolate assignment using either classifiers based on the Pearson similarity or SVM (support vector machine). With the exception of one source identification scheme, which was discarded since it used self-assignment, all tested schemes resulted in low rates of correct classification (<35%) and significant rates of incorrect classification (>15%). The heterogeneous coverage of E. coli genotypic diversity between sources and the uneven distribution of E. coli genotypes in the library likely explain the difficulties encountered in identifying the sources of fecal contamination. Shannon diversity index of sources ranged from 0 for several wildlife species sampled once to 3.03 for sewage treatment plant effluents sampled on various occasions, showing discrepancies between sources. The uneven genotypic composition of the library was attested by the value of the Pielou index (0.54), the high proportion of nondiscriminatory genotypes (>91% of the isolates), and the very low proportion of discriminatory genotypes (<3%). Since efforts made to constitute such a library are not affordable for routine analyses, the results question the relevance of developing such a method for identifying sources of fecal contamination on such a coastline.

Uncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm

Uncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm

Intensification of data processing and obtaining new information on multidimensional signals of the "electronic nose"

This study describes the ways to optimize the stage of processing multidimensional data of simulation systems with an integrated analytical signal such as an electronic nose. Programming models are presented in Exel tables for calculating additional parameters of the qualitative composition of a mixture of gases and vapors. Programming spreadsheets greatly simplifies the processing of the initial data of a set of sensors and allows you to quickly get new parameters to characterize the composition of the smell of samples. The formulas for calculating 4 additional characteristics are presented: identification parameters of sorption, kinetic parameter, sorption parameter for 3 sensors, mass fraction of components, mainly sorbed on each sensor in the array of electronic nose, and Pearson's similarity parameter for sets of these characteristics in order to compare the multi-component composition of the odor analyzed samples. The example of analyzing the smell of human skin shows the possibility of developing software for personal devices. The software includes the calculation of the characteristics of the proposed models and the visualization of their sets for easy perception by untrained users. The software allows you to quickly process data from the device, to present the possible causes of the deviation of the state from the average statistical norms. For a set of identification parameters of sorption, the boundaries of numerical values are defined, which characterize the normal functioning of the organism as a whole, individual organs and systems. When a calculated parameter enters these boundaries in the state diagram, it is colored green. The numerical limits of parameters and for anomalous states are determined. When the values of the calculated parameters fall into these intervals, on the state sphere, the zones of the corresponding parameters are colored yellow or red.So, untrained users easily perceive information without complex processing of multi-dimensional data.

Recommender system for surplus stock clearance

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

An exponential similarity measure for collaborative filtering

In this paper, we propose two exponential similarity measures for collaborative filtering in recommender systems. The proposed similarity measures are used to estimate the distance between two users or items. Furthermore, an algorithm is proposed to use the distance obtained from similarity measures introduced in this paper to evaluate empty rating elements in the rating matrix and compare the results of these similarity measures with our exponential similarity measures. Therefore, we deduce that the algorithm using the exponential similarity measures works better than the algorithm using Manhattan, Euclidean, Jaccard, Cosine or Pearson similarity measures when taking K-Most-Similar-Users to evaluate the similarity in order to find ratings for the user that we want to predict ratings for.

Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve

The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.

Local Search-Based Recommender System for Computing the Similarity Matrix

Recommender systems reduce the users' effort in finding their favourite items among a great number of items. In collaborative-based RSs, there are different similarity measures like: genetic algorithms, Pearson and cosine-based similarity techniques. The number of items and personal attributes (e.g., environment, sex, job, religion, age, county, education, etc.) that are used by the similarity metric algorithms are increasing significantly which makes the recommendation task more difficult. In our project, we introduce a new RS that uses the local search algorithms to compute the similarity matrix. As far as we know, we have not found any work in the RS literature that uses local search algorithms techniques. We show that our new RS computes the similarity matrix and outperforms the other techniques like the Pearson correlation and cosine similarity and some of the recent genetic-based recommender systems.

Fusion of Pearson Similarity and Slope One Methods for QoS Prediction for Web Services

Web services have become the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single Web service, so QoS information is an important indicator for service selection. In reality, QoSs of some Web services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This paper attempts to settle this difficulty by fusing Pearson similarity and Slope One methods for QoS prediction. In this paper, the Pearson similarity is adopted between two services as the weight of their deviation. Meanwhile, some strategies like weight adjustment and SPC-based smoothing are also utilized for reducing prediction error. In order to evaluate the validity of the proposed algorithm, comparative experiments are performed on the real-world data set. The result shows that the proposed algorithm exhibits better prediction precision than both basic Slope One and the well-known WsRec algorithm in most cases. Meanwhile, the new approach has the strong ability of reducing the impact of noise data.

Feasibility study on the use of a portable micro near infrared spectroscopy device for the “in vineyard” screening of extractable polyphenols in red grape skins

There is substantial variation in levels of extractable phenolic compounds of red grapes (Vitis vinifera L.). Therefore, it could be desirable to known the aforesaid parameter at least for each vine. Nowadays, interest has shifted toward the development of portable vis/NIR systems, innovation in optical system design and miniaturization for its friendly use directly in the field.Spectra of intact grapes and grapes skins were recorded at harvest time in two different vintages (2016 and 2017 respectively) using a portable micro NIR spectrophotometer (908–1676 nm). A number of chemometric approaches have been used for spectral interrogation and evaluation of the aforesaid device. Spectral data have been correlated with red grape skin extractable polyphenols (total phenolic, anthocyanins and flavanols) by modified partial least squares regression (MPLS) using a number of spectral pretreatments. Moreover, different statistics strategies have been performed to develop a qualitative analysis of the data (linear discriminant analysis, discriminant partial least square analyses and Pearson's similarity index).After an exhaustive analysis of the obtained results in two different seasons, it can be concluded that the use of the portable micro NIR device for the “in vineyard” screening of extractable polyphenols in red grape skins is hampered by a number of factors. Environmental and physiological conditions should be considered to evaluate and remove factors that hamper a good sorting the berries according to their extractable polyphenol contents.

An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

Implementation of School-Enterprise Two-Sided Matching Based on Pearson and Top Trading Cycle

Due to a large number of graduates and employment information, two-sided matching is increasingly used in employment. However, the general recruitment websites are not rich in student data, so their recommendation will not be very accurate. In order to improve the efficiency of recommendation, a recommendation system based on Pearson and Top Trading Cycle is designed by using the real graduate data of our school. Through the application of Pearson and TTC (Top Trading Cycle), the stable match between the students and the enterprise is realized. We use Pearson similarity algorithm to calculate the similarity between students and sort them from large to small, then we arrange the similarity of order as user preferences in TTC algorithm. Take each of the students as the starting point and the end point, the similarity as the evaluation criteria, and the rest of the students to form a ring. We believe that all the enterprises of the past students in the ring area are the recommended enterprises to be allocated to the students in the ring area, and the students who are to be assigned are recommended students recommended by them.

A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems

Various clustering approaches have been widely adopted to improve the accuracy and scalability of collaborative filtering-based recommender systems as the major objectives. Recent research has experimentally disclosed the realization of such objectives at the expense of decreasing the recommendations coverage. In this paper, we prove that the rate of coverage shows a monotonic decreasing trend as the number of clusters increases. This proof establishes a foundation for the proposal of a 2D-Graph clustering method for partitioning a novel 2D-Graph in compliance with the minimum-acceptable-rate-of-coverage criterion. To reduce the negative impact of clustering on the coverage in the proposed method, in addition to the information of trust, Pearson similarity is incorporated to construct double-weighted connections between users in the 2D-Graph. In each cluster, to address the sparsity problem, the weights corresponding to a convex combination of Pearson, trust and Jaccard similarities are determined so that the precision is optimized. The experimental results on two real-world datasets illustrate that the proposed method outperforms the state-of-the-art methods for Cold Start users in terms of accuracy and coverage.

Efficient local monitoring approach for the task of background subtraction

We present in this paper a novel and efficient method that will significantly reduce GMM drawbacks in the presence of complex and dynamic scene. The main idea is to combine global and local features to remove local variations and the instant variations in the brightness that, in most cases, decrease the performance of background subtraction models. The first step is to divide the extracted frames into several equal size blocks. Then, we apply an adaptive local monitoring algorithm for each block to control local variation using Pearson similarity measurement. When a significant environment changes are detected in one or more blocks, the parameters of GMM assigned to these blocks are updated and the parameters of the rest remain the same. We also proposed merging adjacent and invariant blocks to reduce processing time and splitting the blocks that have an intense movement to improve accuracy. Experimental results on several datasets demonstrate that the proposed approach is effective and efficient under the common problems found in background modeling, outperforming the most referred state-of-the-art background subtraction methods.

Contribution Level Calculation to Similarity Measuring in Collaborative Filtering

Collaborative filtering is the well-known recommendation technique to make a prediction for the given user/item pair based on the rating of similar users/items. Hence, it largely relies on the similarity measure of the users/items. Pearson similarity and Cosine similarity are two of the most famous similarity measures but these measures just take into account the similarity of common ratings and thus have some defects when applying to the realistic application. In addiction to plain similarity measures, we propose a contribution level calculation to similarity measures and discuss the neighborhood selection of similar user/items to further improve the rating prediction. Experiments are evaluated over the classic datasets of recommendation problems and the result shows that the newly proposed measure on the similarity calculation is better than the original one.

An improved collaborative recommendation algorithm based on optimized user similarity

There are lots of issues existing in traditional collaborative filtering recommendation, such as data scarcities, cold start, recommendation accuracy and timeliness. And how to improve the efficiency and quality of recommendation is a key problem in collaborative recommendation. In the traditional collaborative filtering algorithms, the rating scale of different users for all projects sometimes may be neglected while calculating the similarity. Some algorithms such as adjusted cosine similarity algorithm and the Pearson similarity algorithm are proposed to optimize this problem, but there still exists the problem that the single rating scale is different for the same project with different users. It may result in similar resultant vector results when the users have significant differences for the score vectors on a common set. The substantial presence of this kind of phenomena has a direct impact on the accuracy of user similarity calculation. Furthermore, it will affect the target user's predicted score accuracy. To solve the problem, an improved collaborative recommendation algorithm based on optimized user similarity is proposed. A balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users. Also, the most appropriate balance factor threshold can be obtained by experiments, a series of reasonable experiments to validate the effectiveness of the proposed algorithm based on the threshold. Experimental results show that the proposed improved collaborative filtering algorithm based on user similarity can significantly optimize the accuracy of user similarity and get better recommendation results.

Data Sensitive Recommendation Based On Community Detection

Abstract Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. α is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considered in predicted rating mechanism. β is the control parameter. Moreover, in the constructed K-nearest neighbour set, both user-community similarity and user-user similarity are considered. The target user is either in the community or has some correlation to the community. Calculating the user-community similarity can cope with cold start problem. To calculate the recommendation, movielens data sets are used in the experiments. First, parameters α and β are tested and DSRCD is compared with traditional collaborative filtering recommendation algorithm (TCF) and Zhao’s algorithm. DSRCD always has better results than TCF. When K = 30, we have better performance results than Zhao’s algorithm.

Celebrity Endorsement Problem on Social Media: Formulation, Analysis and Recommendation Algorithm

In this paper, a new matching recommendation algorithm is proposed to help enterprises find one or more proper celebrities from social media for their product endorsement. The fans group of a celebrity, his impaction value in the social media, as well as the matching degree between the celebrity and the product are selected to measure a celebrity. The attribute similarities between target customers of the product and fans of the celebrity are calculated via the Pearson similarity formula. Then, considering the impaction value of the celebrity and the matching degree of the celebrity and the product which can be accessed on the website or usually available from the enterprise, an evaluation index is proposed. We use some data from Sina Micro-blog, which is the most popular social media platform in China, to show the effectiveness of our proposed matching recommendation algorithm. Moreover, the analysis shows that a particular celebrity may suitable to endorse different product on-line and off-line.

Early Cambrian palaeobiogeography of the Zhenba–Fangxian Block (South China): Independent terrane or part of the Yangtze Platform?

Early Cambrian small skeletal fossils (SSFs) are studied and revised from the Zhenba–Fangxian Block of the transitional zone between the Yangtze Block and the South Qinling Terrane. The study reveals a diverse fauna with 47 species of various biological affinities, including the new species Gapparodus gapparites sp. nov. The SSFs are assigned to the newly defined Cambroclavus fangxianensis–Rhombocorniculum cancellatum Assemblage Zone. Based on the investigated SSF fauna from Zhenba County, Southeast Shaanxi of China and published data, a palaeobiogeographic study is carried out for the Cambrian Stage 3 (equivalent to the Atdabanian–Botoman of Siberia). A hierarchical Pearson similarity cluster analysis of 295 species from 32 regions of the world indicates a distinct palaeobiogeographic pattern with seven faunal provinces. The result is mostly consistent with existing palaeogeographic reconstructions for the early Cambrian. However, it is also shown that the SSF assemblages of the Zhenba–Fangxian Block have low similarity with those of the Yangtze Block. Instead, they share high similarity with those from Armorica, Tarim and the Karatau–Naryn terranes (South Kazakhstan/North Kyrgyzstan). The Yangtze Block has a unique SSF assemblage dissimilar to most of other regions. The Terreneuvian–Cambrian Stage 3 sedimentary sequence of the Zhenba–Fangxian Block is more consistent with that of the South Qinling Terrane. Besides, sedimentary Ediacaran manganese ore deposits and Cambrian barite/witherite deposits have unique distribution pattern on the Zhenba–Fangxian Block. Derived from the profound dissimilarities in faunal composition, sedimentary sequence and distribution of sedimentary ore deposits, we hypothesize that during the Neoproterozoic–Cambrian transition, the Zhenba–Fangxian Block might have been an independent terrane and more distant from the Yangtze Block. The palaeobiogeographic analysis of SSFs also indicates a closer alliance between Avalonia and Siberia. It corroborates the palaeogeographic reconstruction of North China at the margin of Gondwana, in the vicinity of Australia, Antarctica, and Armorica.