Three-way formal concept clustering technique for matrix completion in recommender system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chemmalar Selvi G. ◽  
Lakshmi Priya G.G.
Keyword(s):  
Recommender Systems ◽  
Error Rate ◽  
Prediction Accuracy ◽  
Matrix Completion ◽  
Sparse Data ◽  
Data Matrix ◽  
Formal Concept ◽  
Data Loss ◽  
Content Type ◽  
The Matrix

Purpose In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information. Design/methodology/approach To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix. Findings The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score. Social implications The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products. Originality/value This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

scholarly journals Scalable Probabilistic Matrix Factorization with Graph-Based Priors

2020 ◽  
Vol 34 (04) ◽  
pp. 5851-5858
Author(s):  
Jonathan Strahl ◽  
Jaakko Peltonen ◽  
Hirsohi Mamitsuka ◽  
Samuel Kaski
Keyword(s):  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Side Information ◽  
Matrix Completion ◽  
Real Data ◽  
Data Matrix ◽  
Laptop Computer ◽  
Completion Problem ◽  
Graphical Lasso ◽  
The Matrix

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these contested edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.

A Recovery Method of Data Lost in Network Communication

2019 ◽  
Vol 23 (2) ◽  
pp. 248-252
Author(s):  
Bo Zhang ◽  
Keyword(s):  
Matrix Completion ◽  
Low Rank ◽  
Network Communication ◽  
Data Loss ◽  
Recovery Method ◽  
Random Loss ◽  
The Matrix ◽  
Rank Decomposition ◽  
Low Rank Decomposition ◽  
Recovery Problem

At present, ScanDisk is used to recover the data lost in network communication. But this method is limited in scope, and once the lost data is covered, it’s difficult or impossible to recover it, which results in low recovery degree. Accordingly, a recovery method for lost data in network communication based on RAID6 is proposed. Firstly, according to the mechanism of data loss in network communication, the missing data is divided into three categories: random loss, completely random loss and nonrandom loss, and then according to the results of classification, the recovery problem of the data loss in network communication is converted into the problem of matrix completion, finally, a low-rank decomposition model is proposed, according to the low rank characteristics of the matrix, the lost data in the matrix is recovered, thus the recovery of the lost data in network communication is finished. Experimental results show that the proposed method can easily recover the lost data in network communication with a simple operation, low computing complexity and strong applicability, and can be used as a universal recovery method for data lost in network communication.

scholarly journals Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1016
Author(s):  
Manel Kortas ◽  
Oussama Habachi ◽  
Ammar Bouallegue ◽  
Vahid Meghdadi ◽  
Tahar Ezzedine ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Data Gathering ◽  
Data Recovery ◽  
Data Matrix ◽  
Wireless Sensor ◽  
Time Period ◽  
Interpolation Technique ◽  
The Matrix ◽  
Long Time ◽  
Structured Approach

In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme.

Study on high-temperature wear and mechanism of Al-Si/graphite composites prepared by the die-casting process

2020 ◽  
Vol 72 (10) ◽  
pp. 1153-1158 ◽  
Author(s):  
Yafei Deng ◽  
Xiaotao Pan ◽  
Guoxun Zeng ◽  
Jie Liu ◽  
Sinong Xiao ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Wear Rate ◽  
Peer Review ◽  
Die Casting ◽  
Casting Machine ◽  
Injection Pressure ◽  
Casting Process ◽  
Matrix Alloy ◽  
Content Type ◽  
The Matrix

Purpose This paper aims to improve the tribological properties of aluminum alloys and reduce their wear rate. Design/methodology/approach Carbon is placed in the model at room temperature, pour 680°C of molten aluminum into the pressure chamber, and then pressed it into the mold containing carbon felt through a die casting machine, and waited for it to cool, which used an injection pressure of 52.8 MPa and held the same pressure for 15 s. Findings The result indicated that the mechanical properties of matrix and composite are similar, and the compressive strength of the composite is only 95% of the matrix alloy. However, the composite showed a low friction coefficient, the friction coefficient of Gr/Al composite is only 0.15, which just is two-third than that of the matrix alloy. Similarly, the wear rate of the composite is less than 4% of the matrix. In addition, the composite can avoid severe wear before 200°C, but the matrix alloy only 100°C. Originality/value This material has excellent friction properties and is able to maintain this excellent performance at high temperatures. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2019-0454/

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

Fatigue behavior of precipitation strengthened Cu–Ni–Si alloy modified by Cr and Zr addition

2020 ◽  
Vol 11 (6) ◽  
pp. 861-873
Author(s):  
Ş. Hakan Atapek ◽  
Spiros Pantelakis ◽  
Şeyda Polat ◽  
Apostolos Chamos ◽  
Gülşah Aktaş Çelik
Keyword(s):  
Design Methodology ◽  
Fatigue Behavior ◽  
Fatigue Tests ◽  
Lower Stress ◽  
Content Type ◽  
Hardened Alloy ◽  
Zr Addition ◽  
Nucleation Sites ◽  
The Matrix ◽  
Lower Stress Level

Purpose The purpose of this paper is to investigate the fatigue behavior of precipitation-strengthened Cu‒2.55Ni‒0.55Si alloy, modified by the addition of 0.25 Cr and 0.25 Zr (wt%), using mechanical and fractographical studies to reveal the effect of microstructural features on the fracture. Design/methodology/approach For strengthening, cast and hot forged alloy was subjected to solution annealing at 900°C for 60 min, followed by quenching in water and then aging at 490°C for 180 min. Precipitation-hardened alloy was exposed to fatigue tests at R=−1 and different stress levels. All fracture surfaces were examined within the frame of fractographical analysis. Findings Fine Ni-rich silicides responsible for the precipitation strengthening were observed within the matrix and their interactions with the dislocations at lower stress level resulted in localized shearing and fine striations. Although, by the addition of Cr and Zr, the matrix consisted of hard Ni, Zr-rich and Cr-rich silicides, these precipitates adversely affected the fatigue behavior acting as nucleation sites for cracks. Originality/value These findings contribute to the present knowledge by revealing the effect of microstructural features on the mechanical behavior of precipitation-hardened Cu‒Ni‒Si alloy modified by Cr and Zr addition.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

A study on tribological behaviour and analysis of ZnO reinforced AA6061 matrix composites fabricated by stir casting route

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sakthi Sadhasivam RM ◽  
Ramanathan K. ◽  
Bhuvaneswari B.V. ◽  
Raja R.
Keyword(s):  
Wear Rate ◽  
Sliding Wear ◽  
Stir Casting ◽  
Industrial Applications ◽  
Wear Behaviour ◽  
Matrix Composites ◽  
Tribological Behaviour ◽  
Content Type ◽  
Zno Particles ◽  
The Matrix

Purpose The most promising replacements for the industrial applications are particle reinforced metal matrix composites because of their good and combined mechanical properties. Currently, the need of matrix materials for industrial applications is widely satisfied by aluminium alloys. The purpose of this paper is to evaluate the tribological behaviour of the zinc oxide (ZnO) particles reinforced AA6061 composites prepared by stir casting route. Design/methodology/approach In this study, AA6061 aluminium alloy matrix reinforced with varying weight percentages (3%, 4.5% and 6%) of ZnO particles, including monolithic AA6061 alloy samples, is cast by the most economical fabrication method, called stir casting. The prepared sample was subjected to X-ray photoelectron spectroscopy (XPS) analysis, experimental density measurement by Archimedian principle and theoretical density by rule of mixture and hardness test to investigate mechanical property. The dry sliding wear behaviour of the composites was investigated using pin-on-disc tribometer with various applied loads of 15 and 20 N, with constant sliding velocity and distance. The wear rate, coefficient of friction (COF) and worn surfaces of the composite specimens and their effects were also investigated in this work. Findings XPS results confirm the homogeneous distribution of ZnO microparticles in the Al matrix. The Vickers hardness result reveals that higher ZnO reinforced (6%) sample have 34.4% higher values of HV than the monolithic aluminium sample. The sliding wear tests similarly show that increasing the weight percentage of ZnO particles leads to a reduced wear rate and COF of 30.01% and 26.32% lower than unreinforced alloy for 15 N and 36.35% and 25% for 20 N applied load. From the worn surface morphological studies, it was evidently noticed that ZnO particles dispersed throughout the matrix and it had strong bonding between the reinforcement and the matrix, which significantly reduced the plastic deformation of the surfaces. Originality/value The uniqueness of this work is to use the reinforcement of ZnO particles with AA6061 matrix and preparing by stir casting route and to study and analyse the physical, hardness and tribological behaviour of the composite materials.

Enhancing the tensile strength of SiC reinforced aluminium- based functionally graded structure through the mixture design approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
M. Poornesh ◽  
Shreeranga Bhat ◽  
E.V. Gijo ◽  
Pavana Kumara Bellairu
Keyword(s):  
Tensile Strength ◽  
Composite Structure ◽  
Centrifugal Casting ◽  
Matrix Material ◽  
Stir Casting ◽  
Mixture Design ◽  
Functionally Graded ◽  
Content Type ◽  
Functionally Graded Composite ◽  
The Matrix

PurposeThis article aims to study the tensile properties of a functionally graded composite structure with Al–18wt%Si alloy as the matrix material and silicon carbide (SiC) particles as the reinforcing element. More specifically, the study's primary objective is to optimize the composition of the material elements using a robust statistical approach.Design/methodology/approachIn this research, the composite material is fabricated using a combination of stir casting and the centrifugal casting technique. Moreover, the test specimen required to study the tensile strength are prepared according to the ASTM (American Society for Testing and Materials) standards. Eventually, optimal composition to maximize the tensile property of the material is determined using the mixture design approach.FindingsThe investigation results imply that the addition of the SiC plays a crucial role in increasing the tensile strength of the composite. The optical microstructural images of the composite show the adequate distribution of the reinforcing particles with the matrix. The proposed regression model shows better predictability of tensile strength. In addition, the methodology aids in optimizing the mixture component values to maximize the tensile strength of the produced functionally graded composite structure.Originality/valueLittle work has been reported so far where a hypereutectic Al–Si alloy is considered the matrix material to produce the composite structure. The article attempts to make a composite structure by using a combination of stir casting and centrifugal casting. Furthermore, it employs the mixture design to optimize the composition and predict the model of the study, which is one of a kind in the field of material science.

Service quality function deployment by the C-shaped QFD 3D matrix

2018 ◽  
Vol 25 (9) ◽  
pp. 3386-3405 ◽  
Author(s):  
Maryam Hassani ◽  
Arash Shahin ◽  
Manouchehr Kheradmandnia
Keyword(s):  
Quality Function Deployment ◽  
Design Methodology ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Customer Requirements ◽  
Content Type ◽  
Process Characteristics ◽  
The Matrix ◽  
3D Matrix ◽  
Simultaneous Comparison

Purpose The purpose of this paper is to examine the application of C-shaped QFD 3D Matrix in comparing process characteristics (PC), performance aspects (PA) and customer requirements, simultaneously and to prioritize the first two sets, respectively. Design/methodology/approach A three dimensional matrix has been developed with three sets of PC, PA and customers’ requirements and C-shaped matrix has been applied for simultaneous comparison of the dimensions and prioritization of the subsets of PC and PA. The proposed approach has been examined in a post bank. Findings Findings confirm the possibility of simultaneous comparison and prioritization of the three sets of dimensions of this study in post bank services. In addition, “growth and learning” and “bilateral relationship with suppliers” had the first priorities among PA and PC, respectively. Research limitations/implications While the proposed approach has many advantages, filling the matrixes is time-consuming. Since illustrating the 3D matrix was not possible, the matrix was separated into five two-dimensional matrixes. Originality/value Compared to the studied literature, the proposed approach is practically new in the post bank services.

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