时间: 2019-12-04 10:17
近日，全球第一大科技图书出版公司施普林格（Springer）发布高被引文章精选。在人工智能领域，共有九篇文章上榜，其中两篇发表于2018年，其余七篇发表于2017年。此次所选文章均出自国际著名科学期刊，其中包括《Artificial Intelligence Review》、《Applied Intelligence》、《Knowledge and Information Systems》、《Machine Vision and Applications》等。快随学术君一起了解下这些文章吧。
文章题目：《A survey of decision making methods based on certain hybrid soft set models》
来源：《Artificial Intelligence Review》- April 2017, Volume 47, Issue 4, pp 507–530
论文作者：Xueling Ma、Qi Liu、Jianming Zhan
论文摘要 Abstract：Fuzzy set theory, rough set theory and soft set theory are all generic mathematical tools for dealing with uncertainties. There has been some progress concerning practical applications of these theories, especially, the use of these theories in decision making problems. In the present article, we review some decision making methods based on (fuzzy) soft sets, rough soft sets and soft rough sets. In particular, we provide several novel algorithms in decision making problems by combining these kinds of hybrid models. It may be served as a foundation for developing more complicated soft set models in decision making.
关键词 Keywords：Fuzzy set、Soft set、Rough set、Rough soft set、Soft rough set、Decision making
文章题目：《Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering
来源：《The Journal of Supercomputing》- November 2017, Volume 73, Issue 11, pp 4773–4795
论文作者：Laith Mohammad Abualigah、Ahamad Tajudin Khader
论文摘要 Abstract：The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.
关键词 Keywords：Unsupervised text feature selection、Particle swarm optimization、Genetic operators、K-mean text clustering、Hybridization
文章题目：《Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems》
来源：《Applied Intelligence》- January 2017, Volume 46, Issue 1, pp 79–95
论文作者：Seyedali Mirjalili、Pradeep Jangir、Shahrzad Saremi
论文摘要 Abstract：This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of solutions as antlions to guide ants towards promising regions of multi-objective search spaces. To prove the effectiveness of the algorithm proposed, a set of standard unconstrained and constrained test functions is employed. Also, the algorithm is applied to a variety of multi-objective engineering design problems: cantilever beam design, brushless dc wheel motor design, disk brake design, 4-bar truss design, safety isolating transformer design, speed reduced design, and welded beam deign. The results are verified by comparing MOALO against NSGA-II and MOPSO. The results of the proposed algorithm on the test functions show that this algorithm benefits from high convergence and coverage. The results of the algorithm on the engineering design problems demonstrate its applicability is solving challenging real-world problems as well.
关键词 Keywords：Ant lion optimizer、Multi-objective optimization、Optimization、Evolutionary algorithm、Multi-criterion optimization、Heuristic、Algorithm、Meta-heuristic Engineering optimization
文章题目：《A survey of methods for time series change point detection》
来源：《Knowledge and Information Systems》- May 2017, Volume 51, Issue 2, pp 339–367
论文作者：Samaneh Aminikhanghahi、Diane J. Cook
论文摘要 Abstract：Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
关键词 Keywords：Change point detection、Time series data、Segmentation、Machine learning、Data mining
文章题目：《Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measuresopen access》
来源：《Machine Vision and Applications》- May 2017, Volume 28, Issue 3–4, pp 361–371
论文作者：Kaelon Lloyd、Paul L. Rosin、David Marshall、Simon C. Moore
论文摘要 Abstract：The severity of sustained injury resulting from assault-related violence can be minimised by reducing detection time. However, it has been shown that human operators perform poorly at detecting events found in video footage when presented with simultaneous feeds. We utilise computer vision techniques to develop an automated method of abnormal crowd detection that can aid a human operator in the detection of violent behaviour. We observed that behaviour in city centre environments often occurs in crowded areas, resulting in individual actions being occluded by other crowd members. We propose a real-time descriptor that models crowd dynamics by encoding changes in crowd texture using temporal summaries of grey level co-occurrence matrix features. We introduce a measure of inter-frame uniformity and demonstrate that the appearance of violent behaviour changes in a less uniform manner when compared to other types of crowd behaviour. Our proposed method is computationally cheap and offers real-time description. Evaluating our method using a privately held CCTV dataset and the publicly available Violent Flows, UCF Web Abnormality and UMN Abnormal Crowd datasets, we report a receiver operating characteristic score of 0.9782, 0.9403, 0.8218 and 0.9956, respectively.
文章题目：《Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier》
来源：《World Wide Web》- March 2017, Volume 20, Issue 2, pp 135–154(Internet and Web Information Systems)
论文作者：Asha S Manek、P Deepa Shenoy、M Chandra Mohan、Venugopal K R
论文摘要 Abstract：With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.
关键词 Keywords：Gini Index、Feature selection、Reviews、Sentiment、Support Vector Machine (SVM)
文章题目：《Scalable process discovery and conformance checking open access》
来源：《Software and Systems Modeling》- May 2018, Volume 17, Issue 2, pp 599–631
论文作者：Sander J. J. Leemans、Dirk Fahland、Wil M. P. van der Aalst
论文摘要 Abstract：Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.
关键词 Keywords：Big data、Scalable process mining、Block-structured process discovery、Directly-follows graphs、Algorithm evaluation、Rediscoverability、Conformance checking
文章题目：《Speech recognition with improved support vector machine using dual classifiers and cross fitness validation》
来源：《Personal and Ubiquitous Computing》- October 2018, Volume 22, Issue 5–6, pp 1083–1091
论文作者：B. Kanisha、S. Lokesh、Priyan Malarvizhi Kumar、P. Parthasarathy、Gokulnath Chandra Babu
论文摘要 Abstract：In this research, a new speech recognition method based on improved feature extraction and improved support vector machine (ISVM) is developed. A Gaussian filter is used to denoise the input speech signal. The feature extraction method extracts five features such as peak values, Mel frequency cepstral coefficient (MFCC), tri-spectral features, discrete wavelet transform (DWT), and the difference values between the input and the standard signal. Next, these features are scaled using linear identical scaling (LIS) method with the same scaling method and the same scaling factors for each set of features in both training and testing phases. Following this, to accomplish the training process, an ISVM is developed with best fitness validation. The ISVM consists of two stages: (i) linear dual classifier that finds the same class attributes and different class attributes simultaneously and (ii) cross fitness validation (CFV) method to prevent over fitting problem. The proposed speech recognition method offers 98.2% accuracy.
关键词 Keywords：Speech recognition、Mel frequency cepstral coefficients、Tri-spectral feature、Discrete wavelet transforms、Linear identical scaling、Cross fitness validation
文章题目：《Blind inpainting using the fully convolutional neural network》
来源：《The Visual Computer》(International Journal of Computer Graphics)-February 2017, Volume 33, Issue 2, pp 249–261
论文作者：Nian Cai、Zhenghang Su、Zhineng Lin、Han Wang、Zhijing Yang、Bingo Wing-Kuen Ling
论文摘要 Abstract：Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. However, in many applications, such information may not be readily available. In this paper, we propose a novel blind inpainting method based on a fully convolutional neural network. We term this method as blind inpainting convolutional neural network (BICNN). It purely cascades three convolutional layers to directly learn an end-to-end mapping between a pre-acquired dataset of corrupted/ground truth subimage pairs. Stochastic gradient descent with standard backpropagation is used to train the BICNN. Once the BICNN is learned, it can automatically identify and remove the corrupting patterns from a corrupted image without knowing the specific regions. The learned BICNN takes a corrupted image of any size as input and directly produces a clean output by only one pass of forward propagation. Experimental results indicate that the proposed method can achieve a better inpainting performance than the existing inpainting methods for various corrupting patterns.
关键词 Keywords：Image processing、Blind inpainting、Deep learning、Convolutional neural network