Articles de revues sur le sujet « Locally weighted information gain »
Pour voir les autres types de publications sur ce sujet consultez le lien suivant : Locally weighted information gain.
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les 50 meilleurs articles de revues pour votre recherche sur le sujet « Locally weighted information gain ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Parcourez les articles de revues sur diverses disciplines et organisez correctement votre bibliographie.
1
Huang, Dong, Chang-Dong Wang et Jian-Huang Lai. « Locally Weighted Ensemble Clustering ». IEEE Transactions on Cybernetics 48, no 5 (mai 2018) : 1460–73. http://dx.doi.org/10.1109/tcyb.2017.2702343.
Texte intégral
2
Carter, Brad, et Claus Rinner. « Locally weighted linear combination in a vector geographic information system ». Journal of Geographical Systems 16, no 3 (30 novembre 2013) : 343–61. http://dx.doi.org/10.1007/s10109-013-0194-3.
Texte intégral
3
Nielsen, Jens Perch, et Bjørn Lunding Sandqvist. « Credibility Weighted Hazard Estimation ». ASTIN Bulletin 30, no 2 (novembre 2000) : 405–17. http://dx.doi.org/10.2143/ast.30.2.504643.
Texte intégralRésumé :
AbstractCredibility weighting is helpful in many insurance applications where sparse data crave information from other sources of data. In this paper we aim at estimating a hazard curve using the nonparametric kernel method, where a credibility weighting principle is used locally, so that areas of sparse data for one subgroup can be alleviated by available information from other subgroups. The credibility estimator is found through a Hilbert space projection formulation of Buhlmann-Straub's credibility approach.
4
Li, Xiaoyan, Lefei Zhang et Jane You. « Locally Weighted Discriminant Analysis for Hyperspectral Image Classification ». Remote Sensing 11, no 2 (9 janvier 2019) : 109. http://dx.doi.org/10.3390/rs11020109.
Texte intégralRésumé :
A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20 % for Indian Pines and 17 % for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.
5
Syed Ibrahim et Chandran. « Compact Weighted Class Association Rule Mining Using Information Gain ». International Journal of Data Mining & ; Knowledge Management Process 1, no 6 (30 novembre 2011) : 1–13. http://dx.doi.org/10.5121/ijdkp.2011.1601.
Texte intégral
6
Singer, Gonen, Roee Anuar et Irad Ben-Gal. « A weighted information-gain measure for ordinal classification trees ». Expert Systems with Applications 152 (août 2020) : 113375. http://dx.doi.org/10.1016/j.eswa.2020.113375.
Texte intégral
7
Li, Yafang, Caiyan Jia, Xiangnan Kong, Liu Yang et Jian Yu. « Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering ». IEEE Transactions on Cybernetics 49, no 1 (janvier 2019) : 247–60. http://dx.doi.org/10.1109/tcyb.2017.2771496.
Texte intégral
8
Jiang, Zhengkai, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang et Chunhong Pan. « Video Object Detection with Locally-Weighted Deformable Neighbors ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 8529–36. http://dx.doi.org/10.1609/aaai.v33i01.33018529.
Texte intégralRésumé :
Deep convolutional neural networks have achieved great success on various image recognition tasks. However, it is nontrivial to transfer the existing networks to video due to the fact that most of them are developed for static image. Frame-byframe processing is suboptimal because temporal information that is vital for video understanding is totally abandoned. Furthermore, frame-by-frame processing is slow and inefficient, which can hinder the practical usage. In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks. LWDN can latently align the high-level features between keyframes and keyframes or nonkeyframes. Inspired by (Zhu et al. 2017a) and (Hetang et al. 2017) who propose to aggregate features between keyframes and keyframes, we adopt brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes. We call this process Memory-Guided Propagation. With such a memory mechanism, the discriminative ability of features in keyframes and non-keyframes are both enhanced, which helps to improve the detection accuracy. Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU.
9
Chen, Yubao, Xiao Li et Elsayed Orady. « Integrated diagnosis using information-gain-weighted radial basis function neural networks ». Computers & ; Industrial Engineering 30, no 2 (avril 1996) : 243–55. http://dx.doi.org/10.1016/0360-8352(95)00169-7.
Texte intégral
10
Zhou, Jiandong, Xiang Li, Xin Wang, Yunpeng Chai et Qingpeng Zhang. « Locally weighted factorization machine with fuzzy partition for elderly readmission prediction ». Knowledge-Based Systems 242 (avril 2022) : 108326. http://dx.doi.org/10.1016/j.knosys.2022.108326.
Texte intégral
11
Schaal, Stefan, et Christopher G. Atkeson. « Constructive Incremental Learning from Only Local Information ». Neural Computation 10, no 8 (1 novembre 1998) : 2047–84. http://dx.doi.org/10.1162/089976698300016963.
Texte intégralRésumé :
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model, as well as the parameters of the locally linear model itself, are learned independently, that is, without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross-validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness toward negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system that profits from combining independent expert knowledge on the same problem. This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.
12
JIANG, LIANGXIAO, CHAOQUN LI et ZHIHUA CAI. « DECISION TREE WITH BETTER CLASS PROBABILITY ESTIMATION ». International Journal of Pattern Recognition and Artificial Intelligence 23, no 04 (juin 2009) : 745–63. http://dx.doi.org/10.1142/s0218001409007296.
Texte intégralRésumé :
Traditionally, the performance of a classifier is measured by its classification accuracy or error rate. In fact, probability-based classifiers also produce the class probability estimation (the probability that a test instance belongs to the predicted class). This information is often ignored in classification, as long as the class with the highest class probability estimation is identical to the actual class. In many data mining applications, however, classification accuracy and error rate are not enough. For example, in direct marketing, we often need to deploy different promotion strategies to customers with different likelihood (class probability) of buying some products. Thus, accurate class probability estimations are often required to make optimal decisions. In this paper, we firstly review some state-of-the-art probability-based classifiers and empirically investigate their class probability estimation performance. From our experimental results, we can draw a conclusion: C4.4 is an attractive algorithm for class probability estimation. Then, we present a locally weighted version of C4.4 to scale up its class probability estimation performance by combining locally weighted learning with C4.4. We call our improved algorithm locally weighted C4.4, simply LWC4.4. We experimentally test LWC4.4 using the whole 36 UCI data sets selected by Weka. The experimental results show that LWC4.4 significantly outperforms C4.4 in terms of class probability estimation.
13
Azizah, Rizky Adinda, Fitra Bachtiar et Sigit Adinugroho. « Klasifikasi Kinerja Akademik Siswa Menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain ». Jurnal Teknologi Informasi dan Ilmu Komputer 9, no 3 (20 juin 2022) : 605. http://dx.doi.org/10.25126/jtiik.2022935751.
Texte intégralRésumé :
<p class="Abstrak">Kinerja akademik siswa merupakan indikator kesuksesan dari pembelajaran di sekolah. Mengukur kinerja akademik siswa dapat membantu tenaga didik mengembangkan pembelajaran yang sesuai untuk siswa sehingga meningkatkan keberhasilan pembelajaran sekolah. Kinerja akademik siswa dapat diamati melalui suatu <em>Learning Management System</em> bernama Kalboard 360 yaitu sistem yang berhubungan dengan perilaku siswa menggunakan alat pelacak aktivitas siswa yang memantau aktivitas pembelajaran. Data sekunder dari aktivitas tersebut dapat digunakan untuk mengetahui kinerja siswa dengan salah satu caranya adalah klasifikasi. Klasifikasi menggunakan metode <em>Neighbor Weighted K-Nearest Neighbor</em> dengan seleksi fitur <em>Information Gain</em> diterapkan pada penelitian ini untuk membantu klasifikasi kinerja siswa karena metode NWKNN mempunyai kelebihan memperhitungkan metode pembobotan kelas dan mengatasi data tidak seimbang. Seleksi fitur dengan <em>Information Gain</em> digunakan agar dapat mengoptimalkan hasil kerja <em>classifier</em>. Berdasarkan pengujian dan analisis penelitian, didapatkan nilai akurasi terbaik sebesar 0,604, dengan nilai <em>precision</em> adalah 0,719, nilai <em>recall</em> sebesar 0,676, dan nilai <em>f-measure</em> diperoleh adalah 0,661. Nilai tersebut dihasilkan saat menggunakan 9 fitur yaitu <em>VisitedResource, StudentAbsenceDay, RaisedHands, AnnouncementsView, Relation, ParentsAnsweringSurvey</em>, <em>Discussion, NationalITy, </em>dan <em>PlaceofBirth</em> dimana fitur tersebut memperoleh nilai <em>Gain</em> tertinggi dari urutan <em>Gain</em> keseluruhan fitur, dengan nilai <em>Gain </em>≥ 0,1182 dan menggunakan nilai parameter optimal yaitu nilai <em>E </em>=<em> </em>6, dan nilai <em>K</em> = 45.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The academic performance of students is an indicator of the success of learning in school. Measuring and understanding student performance can help for improving learning systems that are suitable for students so the success of school learning will increase. Student academic performance can be observed via Learning Management System (LMS) named Kalboard 360 dealing with student behavior through a student activity tracking device so it can monitor learning activities. In this research, the secondary data is used to determine student performance through a classification. Neighbor Weighted K-Nearest Neighbor algorithm with Information Gain method will be applied to this study to help predict student performance. NWKNN method has advantages in calculating the weight of classes and overcoming unbalanced data. Information Gain is used to optimize the classifier. Based on the research analysis, the accuracy value is 0,604, with precision value obtained is 0,719, recall value obtained is 0,676, and the f-measure value obtained is 0,661. That values is generated when using 9 features with the highest order value of all features namely VisitedResource, StudentAbsenceDay, RaisedHands, AnnouncementsView, Relation, ParentsAnsweringSurvey, Discussion, NationalITy, dan PlaceofBirth, which have Gain value≥0,1182 and using optimal parameters value, that is E = 6 and K = 45.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
14
PANDEY, S. S. « TIME-FREQUENCY LOCALIZATIONS FOR MODULATION SPACES ON LOCALLY COMPACT ABELIAN GROUPS ». International Journal of Wavelets, Multiresolution and Information Processing 02, no 02 (juin 2004) : 149–63. http://dx.doi.org/10.1142/s0219691304000421.
Texte intégralRésumé :
In the present paper we define weighted modulation spaces on a LCA group [Formula: see text] with respect to a window function drawn from a suitable Banach space of test functions and prove a theorem to establish uncertainty principle for these modulation spaces. Also, using the concept of Zak transform, we generalize an earlier result of Heil (1990) on the Balian–Low theorem for the Wiener amalgam space [Formula: see text]. Our theorems include the corresponding results on Euclidean spaces as particular cases.
15
Leng, Ming, Ling-yu Sun et Kai-qiang Guo. « Multi-Level Refinement Algorithm of Weighted Hypergraph Partitioning Problem ». Journal of Intelligent Systems 26, no 3 (26 juillet 2017) : 407–20. http://dx.doi.org/10.1515/jisys-2015-0058.
Texte intégralRésumé :
AbstractThe formal description of weighted hypergraph partitioning problem is presented. We describe the solution of the weighted hypergraph partitioning problem based on the multi-level method. We propose the multi-level discrete particle swarm optimization refinement algorithm, whose each particle’s position in |V|-dimensional can be considered as the corresponded partitioning. During the refinement process of the uncoarsening phase, the algorithm projects successively each particle’s corresponded partitioning back to the next-level finer hypergraph, and the degree of particle’s freedom increases with the increase in solution space’s dimension. The algorithm also regards the gain of vertex as particle information for the heuristic search and successfully searches the solution space based on the intelligent behavior between individuals’ collaboration. Furthermore, the improved compressed storage format of weighted hypergraph is presented and the two-dimensional auxiliary array is designed for counting the vertices of each hypergraph in different partitions. The rapid method of calculating the vertex’s gain and the cut’s size are proposed to avoid traversing each vertex of hyperedge and reduce the algorithm’s time complexity and space complexity. Experimental results show that the algorithm not only can find the better partitioning of weighted hypergraph than the move-based method but also can improve the search capability of the refinement algorithm.
16
Yusri, Sarah, Ahmed Elfana, Weam Elbattawy et Karim M. Fawzy El-Sayed. « Effect of locally delivered adjunctive antibiotics during surgical periodontal therapy : a systematic review and meta-analysis ». Clinical Oral Investigations 25, no 9 (20 juillet 2021) : 5127–38. http://dx.doi.org/10.1007/s00784-021-04056-7.
Texte intégralRésumé :
Abstract Aim The present study aimed to systematically assess current evidence on effects of locally delivered antibiotics during periodontal surgery compared to periodontal surgery alone on clinical attachment level (CAL) gain, probing pocket depth (PPD) reduction, recession depth (RD) changes, gingival index (GI), bleeding on probing (BOP), and plaque index (PI). Methodology MEDLINE-PubMed, Cochrane-CENTRAL and Scopus databases were searched up to April 2021 for randomized clinical trials (RCT), evaluating effects of locally delivered antibiotics during periodontal surgery. CAL gain served as primary, while PPD reduction, RD changes, GI and PI as secondary outcomes. The Cochrane Risk of Bias Tool was used to assess possible bias. Data were extracted, and meta-analysis was performed where appropriate. Result Screening of 2314 papers resulted in nine eligible studies. No adverse events were reported. Data on outcome variables were pooled and analyzed using generic inverse variance model and presented as weighted mean difference (WMD) and 95% confidence interval (95% CI). Statistically significant improvements in favor of antibiotics’ delivery were observed in studies with follow-up of ≤6 months for CAL gain (WMD = 0.61 mm (95% CI [0.07, 1.14]; p = 0.03), PPD reduction (WMD = 0.41 mm (95% CI [0.02, 0.80]; p = 0.04)) and BOP (WMD = −28.47% (95% CI [−33.00, −23.94]); p < 0.001), while for GI improvements were notable for >6 to 12 months (WMD = −0.27 (95% CI [−0.49, −0.06]; p = 0.01)). Conclusion Within the current review’s limitations, locally delivered antibiotics during surgical periodontal therapy results in post-surgical improvements for CAL, PPD, and BOP (≤6 months) with a longer-lasting GI improvement. Further randomized controlled trials are needed with true periodontal end-points to assess the ideal antibiotic agent, dosage, and delivery methods. Clinical relevance Local delivery of antibiotics during periodontal surgery improved clinical parameters for up to 6-month follow-up, with beneficial longer effects on gingival inflammation. Within the current study’s limitation, the presented evidence could support the elective usage of locally delivered antibiotics during surgical periodontal therapy.
17
AlAttar, Ahmad, et Petar Kormushev. « Kinematic-Model-Free Orientation Control for Robot Manipulation Using Locally Weighted Dual Quaternions ». Robotics 9, no 4 (24 septembre 2020) : 76. http://dx.doi.org/10.3390/robotics9040076.
Texte intégralRésumé :
Conventional control of robotic manipulators requires prior knowledge of their kinematic structure. Model-learning controllers have the advantage of being able to control robots without requiring a complete kinematic model and work well in less structured environments. Our recently proposed Encoderless controller has shown promising ability to control a manipulator without requiring any prior kinematic model whatsoever. However, this controller is only limited to position control, leaving orientation control unsolved. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to handle orientation control to manipulate a robotic arm without requiring any prior model of the robot or any joint angle information during control. This paper presents a novel method to simultaneously control the position and orientation of a robot’s end effector using locally weighted dual quaternions. The proposed novel controller is also scaled up to control three-degrees-of-freedom robots.
18
Touati, Habib Chawki, et Fateh Boutekkouk. « Reliable Weighted Globally Congestion Aware Routing for Network on Chip ». International Journal of Embedded and Real-Time Communication Systems 11, no 3 (juillet 2020) : 48–66. http://dx.doi.org/10.4018/ijertcs.2020070103.
Texte intégralRésumé :
With the ability to incorporate hundreds of communicating cores on a single chip, thanks to the continuous shrinkage in sizes, communication became of the utmost importance. Consequently, the reduction of transmission delays became unavoidably necessary. All of which is achieved by means of routing algorithms, responsible for selecting the most appropriate routes for data packets, by avoiding congested regions in the network between any pair of source and destination nodes. In this article, two moderately distinct versions in terms of weight distribution of a minimal, fully adaptive, congestion-aware routing scheme in mesh-based network on chip and its accompanying congestion propagation network, are presented. The algorithm does not rely solely on local congestion information nor on irrelevant global information, and provides somewhat a compromise between locally and globally aware routing. The experimental results showcase the proposed scheme's superiority over state of the art NoC routing algorithms.
19
Grimble, M. J. « H ∞ Observations Weighted Control Law ». Journal of Dynamic Systems, Measurement, and Control 112, no 3 (1 septembre 1990) : 337–48. http://dx.doi.org/10.1115/1.2896150.
Texte intégralRésumé :
A H∞ optimal control problem is considered which leads to a particularly simple controller structure. The expression for the optimal controller only requires the solution of one diophantine equation which makes it very appropriate for self-tuning control applications. The controller developed is related to the Observations Weighted control law which involves the minimization of tracking error and control signal variances. The H∞ criterion minimized may be expressed in a signal spectrum minimization form or in a sensitivity minimization form. The system can be guaranteed to be stable under low control weighting (high gain) conditions even for non-minimum phase open-loop unstable plants. The design of the controller depends upon the specification of the cost weighting transfer-functions and this is illustrated in a design example.
20
Yuan, Jidong, Ahlame Douzal-Chouakria, Saeed Varasteh Yazdi et Zhihai Wang. « A large margin time series nearest neighbour classification under locally weighted time warps ». Knowledge and Information Systems 59, no 1 (22 mars 2018) : 117–35. http://dx.doi.org/10.1007/s10115-018-1184-z.
Texte intégral
21
Gharieb, R. R., G. Gendy et H. Selim. « A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation ». International Journal of Pattern Recognition and Artificial Intelligence 32, no 04 (13 décembre 2017) : 1850012. http://dx.doi.org/10.1142/s021800141850012x.
Texte intégralRésumé :
In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.
22
He, Zi Fen, et Yin Hui Zhang. « A Method of Digital Halftoning through Approximated Optimization of Scale-Related ». Advanced Materials Research 846-847 (novembre 2013) : 999–1002. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.999.
Texte intégralRésumé :
We gain the scale-related characterization of the original image using the discrete wavelet transform. The boundary information of the image target is fused by the wavelet coefficients of the correlation between wavelet transform layer, which to increase the pixel resolution scale. We apply the inter-scale fusion method to gain fusion coefficient of the fine-scale, which take into account the detail of the image and approximate information, which the fusion coefficient are referred to as the weighting operator and to establish the boundary energy function. In the halftone process, each clustering uses the weighted least-squares method through energy minimization via Direct Binary Search algorithm, which to gain halftoning image. Simulation results on typical test images further confirm the performance of the new approach.
23
Zhang, Rong, Andrew G. Alleyne et Don E. Carter. « Generalized Multivariable Gain Scheduling With Robust Stability Analysis ». Journal of Dynamic Systems, Measurement, and Control 127, no 4 (4 avril 2005) : 668–87. http://dx.doi.org/10.1115/1.2101843.
Texte intégralRésumé :
In this work we introduce a methodology for the design of multivariable gain-scheduled controllers for nonlinear systems and an approach for determining the local stability of a nonlinear closed loop system. The gain-scheduled global control is designed by scheduling different local controllers using a Local Controller Network. The individual local controllers are assumed to be LTI MIMO controllers that can be designed via some user-specified multivariable method. In this paper, different portions of outputs from different local controllers are combined into the total control by using interpolation-weighting functions. The variation in the control behavior as a result of the scheduling variable is posed in a robust control framework. The dynamics of the scheduling variables are incorporated into the global control framework as an unstructured uncertainty. This allows the use of computational tools to analyze the stability of the overall global system and verify whether or not a given gain-scheduled approach will remain stable locally. To demonstrate the practical significance of the method, a multivariable electrohydraulic earthmoving powertrain problem is solved using the approach. The nonlinear power train was locally modeled as an LTI MIMO system and a local LTI MIMO controller was designed at each operating point using an H∞ algorithm. The analysis approach introduced is utilized to verify system stability and is supported closely by experimental results.
24
Qu, Jiahui, Yunsong Li, Qian Du, Wenqian Dong et Bobo Xi. « Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix ». Remote Sensing 11, no 9 (27 avril 2019) : 1005. http://dx.doi.org/10.3390/rs11091005.
Texte intégralRésumé :
Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.
25
Kaur, Wandeep, Vimala Balakrishnan et Kok-Seng Wong. « IMPROVING MULTI-LABEL TEXT CLASSIFICATION USING WEIGHTED INFORMATION GAIN AND CO-TRAINED MULTINOMIAL NAÏVE BAYES CLASSIFIER ». Malaysian Journal of Computer Science 35, no 1 (31 janvier 2022) : 21–36. http://dx.doi.org/10.22452/mjcs.vol35no1.2.
Texte intégralRésumé :
Over recent years, the emergence of electronic text processing systems has generated a vast amount of structured and unstructured data, thus creating a challenging situation for users to rummage through irrelevant information. Therefore, studies are continually looking to improve the classification process to produce more accurate results that would benefit users. This paper looks into the weighted information gain method that re-assigns wrongly classified features with new weights to provide better classification. The method focuses on the weights of the frequency bins, assuming every time a certain word frequency bin is iterated, it provides information on the target word feature. Therefore, the more iteration and re-assigning of weight occur within the bin, the more important the bin becomes, eventually providing better classification. The proposed algorithm was trained and tested using a corpus extracted from dedicated Facebook pages related to diabetes. The weighted information gain feature selection technique is then fed into a co-trained Multinomial Naïve Bayes classification algorithm that captures the labels' dependencies. The algorithm incorporates class value dependencies since the dataset used multi-label data before converting string vectors that allow the sparse distribution between features to be minimised, thus producing more accurate results. The results of this study show an improvement in classification to 61%.
26
Utami, Putri Fajar, Agus Rusgiyono et Dwi Ispriyanti. « PEMODELAN SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PNEUMONIA BALITA PROVINSI JAWA TENGAH ». Jurnal Gaussian 10, no 2 (31 mai 2021) : 250–58. http://dx.doi.org/10.14710/j.gauss.v10i2.30945.
Texte intégralRésumé :
Geographical and inter-regional differences have contributed to the diversity of child pneumonia cases in Central Java, so a spatial regression modelling is formed that is called Geographically Weighted Regression (GWR). GWR is a development of linear regression by involving diverse factors geographical location, so that local parameters are produced. Sometimes, there are non-local GWR parameters. To overcome some non-local parameters, Semiparametric Geographically Weighted Regression (SGWR) is formed to develop a GWR model with local and global influences simultaneously. SGWR Model is used to estimate the model of percentage of children with pneumonia in Central Java with population density, average temperature, percentage of children with severe malnutrition, percentage of children with under the red line weight, percentage of households behave in clean and healthy lives, and percentage of children who measles immunized. SGWR models on percentage of children with pneumonia in Central Java produce locally significant variables that is population density, average temperature, and percentage of households behave in clean and healthy lives. Variable that globally significant is percentage of children with severe malnutrition. Based on Akaike Information Criterion (AIC), SGWR is a better model to analize percentage of children with pneumonia in Central Java because of smallest AIC. Keywords: Akaike Information Criterion, Geographically Weighted Regression, Semiparametric Geographically Weighted Regression
27
Kurniawan, Indra. « KOMBINASI MEDIAN WEIGHTED INFORMATION GAIN DENGAN K-NEAREST NEIGHBOR PADA DATASET LABEL MONTHS SOFTWARE EFFORT ESTIMATION ». Jurnal Teknoinfo 14, no 2 (15 juillet 2020) : 138. http://dx.doi.org/10.33365/jti.v14i2.647.
Texte intégral
28
Di Maio, Francesco, Roberta Rossetti et Enrico Zio. « Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps ». Science and Technology of Nuclear Installations 2017 (2017) : 1–14. http://dx.doi.org/10.1155/2017/2709109.
Texte intégralRésumé :
Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).
29
Luyckx, Fabrice, Bernhard Spitzer, Annabelle Blangero, Konstantinos Tsetsos et Christopher Summerfield. « Selective Integration during Sequential Sampling in Posterior Neural Signals ». Cerebral Cortex 30, no 8 (7 mars 2020) : 4454–64. http://dx.doi.org/10.1093/cercor/bhaa039.
Texte intégralRésumé :
Abstract Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as “selective integration” can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers’ decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.
30
Song, Yueli, et Minglun Ren. « A Novel Just-in-Time Learning Strategy for Soft Sensing with Improved Similarity Measure Based on Mutual Information and PLS ». Sensors 20, no 13 (7 juillet 2020) : 3804. http://dx.doi.org/10.3390/s20133804.
Texte intégralRésumé :
In modern industrial process control, just-in-time learning (JITL)-based soft sensors have been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor modeling since it is not only the basis for selecting the nearest neighbor samples but also determines sample weights. In recent years, JITL similarity measure methods have been greatly enriched, including methods based on Euclidean distance, weighted Euclidean distance, correlation, etc. However, due to the different influence of input variables on output, the complex nonlinear relationship between input and output, the collinearity between input variables, and other complex factors, the above similarity measure methods may become inaccurate. In this paper, a new similarity measure method is proposed by combining mutual information (MI) and partial least squares (PLS). A two-stage calculation framework, including a training stage and a prediction stage, was designed in this study to reduce the online computational burden. In the prediction stage, to establish the local model, an improved locally weighted PLS (LWPLS) with variables and samples double-weighted was adopted. The above operations constitute a novel JITL modeling strategy, which is named MI-PLS-LWPLS. By comparison with other related JITL methods, the effectiveness of the MI-PLS-LWPLS method was verified through case studies on both a synthetic Friedman dataset and a real industrial dataset.
31
Indra Kurniawan, Dwi Retna Sulistyowati et Yasa Maulana. « Penerapan Seleksi Fitur Median Weighted Information Gain dengan K-NN pada Dataset Label Hours Software Effort Estimation ». Jurnal CoSciTech (Computer Science and Information Technology) 3, no 2 (17 août 2022) : 52–57. http://dx.doi.org/10.37859/coscitech.v3i2.3876.
Texte intégralRésumé :
Estimasi pengembangan perangkat lunak menjadi kebutuhan dikarenakan setiap pengembangan perangkat lunak memiliki keterbatasan biaya dan waktu dalam menyelesaikan sebuah proyek. Secara spesifik hasil percobaan diperoleh model ML lebih unggul dari pada model non-ML. Dengan hasil secara spesifik yaitu 66% (52 dari 79) menunjukkan keunggulan model ML sedangkan hanya 34% (27 dari 79) hasil percobaan mengunggulkan model non-ML. Beberapa studi yang sudah teridentifikasi, masalah utama yang paling sering ditemui dalam dataset estimasi usaha perangkat lunak yaitu masalah kategori fitur seperti fitur yang tidak relavan. Berdasarkan latar belakang tersebut maka dalam penelitian ini akan dilakukan penerapan metode seleksi fitur Median WIG dengan algoritma k-NN yang akan berfokus pada dataset label hours Software Effort Estimation dan dibandingkan dengan algoritma k-NN. Hasil perbandingan yang diperoleh dalam seluruh pengujian dataset Software Effort Estimation diketahui bahwa metode usulan seleksi fitur Median-WIG dengan k-NN terbukti mampu meningkatkan akurasi dalam proses estimasi dilihat dari penurunan nilai RMSE yang signifikan pada seluruh dataset yaitu Albrecht sebesar 5.958, Miyazaki sebesar 62.363 dan Kemerer sebesar 131.027. Terjadinya penurunan nilai RMSE pada seluruh dataset ini memperlihatkan peningkatan kinerja setelah dilakukannya metode seleksi fitur pada dataset Software Effort Estimation Terjadinya penurunan nilai RMSE ini memperlihatkan peningkatan kinerja setelah menggunakan metode seleksi fitur.
32
BARROS, FLÁVIA A., EDUARDO F. A. SILVA, RICARDO B. C. PRUDÊNCIO, VALMIR M. FILHO et ANDRÉ C. A. NASCIMENTO. « COMBINING TEXT CLASSIFIERS AND HIDDEN MARKOV MODELS FOR INFORMATION EXTRACTION ». International Journal on Artificial Intelligence Tools 18, no 02 (avril 2009) : 311–29. http://dx.doi.org/10.1142/s0218213009000147.
Texte intégralRésumé :
In this paper, we propose a hybrid machine learning approach to Information Extraction by combining conventional text classification techniques and Hidden Markov Models (HMM). A text classifier generates a (locally optimal) initial output, which is refined by an HMM, providing a globally optimal classification. The proposed approach was evaluated in two case studies and the experiments revealed a consistent gain in performance through the use of the HMM. In the first case study, the implemented prototype was used to extract information from bibliographic references, reaching a precision rate of 87.48% in a test set with 3000 references. In the second case study, the prototype extracted information from author affiliations, reaching a precision rate of 90.27% in a test set with 300 affiliations.
33
Guo, Hongwei, Xiangsuo Fan et Lei Min. « Euclidean Distance-Based Weighted Prediction for Merge Mode in HEVC ». Advances in Multimedia 2019 (22 septembre 2019) : 1–7. http://dx.doi.org/10.1155/2019/8202385.
Texte intégralRésumé :
Merge mode can achieve a considerable coding gain because of reducing the cost of coding motion information in video codecs. However, the simple adoption of the motion information from the neighbouring blocks may not achieve the optimal performance as the motion correlation between the pixels and the neighbouring block decreases with their distance increasing. To address this problem, the paper proposes a Euclidean distance-based weighted prediction algorithm as an additional candidate in the merge mode. First, several predicted blocks are generated by motion compensation prediction (MCP) with the motion information from available neighbouring blocks. Second, an additional predicted block is generated by a weighted average of the predicted blocks above, where the weighted coefficient is related to Euclidean distances from the neighbouring candidate to the pixel points in the current block. Finally, the best merge mode is selected by the rate distortion optimization (RDO) among the original merge candidates and the additional candidate. Experimental results show that, on the joint exploration test model 7.0 (JEM 7.0), the proposed algorithm achieves better coding performance than the original merge mode under all configurations including random access (RA), low delay B (LDB), and low delay P (LDP), with a slight coding complexity increase. Especially for the LDP configuration, the proposed method achieves 1.50% bitrate saving on average.
34
Chen, P. X., et C. Z. Li. « Local distinguishability of quantum states and the distillation of entanglement ». Quantum Information and Computation 3, no 3 (mai 2003) : 203–10. http://dx.doi.org/10.26421/qic3.3-2.
Texte intégralRésumé :
This paper tries to probe the relation between the local distinguishability of orthogonal quantum states and the distillation of entanglement. An new interpretation for the distillation of entanglement and the distinguishability of orthogonal quantum states in terms of information is given, respectively. By constraining our discussion on a special protocol we give a necessary and sufficient condition for the local distinguishability of the orthogonal pure states, and gain the maximal yield of the distillable entanglement. It is shown that the information entropy, the locally distinguishability of quantum states and the distillation of entanglement are closely related.
35
Aqil, Chakir, Ismail Akharraz et Abdelaziz Ahaitouf. « A New Reliability Ratio Weighted Bit Flipping Algorithm for Decoding LDPC Codes ». Wireless Communications and Mobile Computing 2021 (16 mai 2021) : 1–11. http://dx.doi.org/10.1155/2021/6698602.
Texte intégralRésumé :
In this study, we propose a “New Reliability Ratio Weighted Bit Flipping” (NRRWBF) algorithm for Low-Density Parity-Check (LDPC) codes. This algorithm improves the “Reliability Ratio Weighted Bit Flipping” (RRWBF) algorithm by modifying the reliability ratio. It surpasses the RRWBF in performance, reaching a 0.6 dB coding gain at a Binary Error Rate (BER) of 10−4 over the Additive White Gaussian Noise (AWGN) channel, and presents a significant reduction in the decoding complexity. Furthermore, we improved NRRWBF using the sum of the syndromes as a criterion to avoid the infinite loop. This will enable the decoder to attain a more efficient and effective decoding performance.
36
Zhang, Li. « A Feature Selection Algorithm Integrating Maximum Classification Information and Minimum Interaction Feature Dependency Information ». Computational Intelligence and Neuroscience 2021 (28 décembre 2021) : 1–10. http://dx.doi.org/10.1155/2021/3569632.
Texte intégralRésumé :
Feature selection is the key step in the analysis of high-dimensional small sample data. The core of feature selection is to analyse and quantify the correlation between features and class labels and the redundancy between features. However, most of the existing feature selection algorithms only consider the classification contribution of individual features and ignore the influence of interfeature redundancy and correlation. Therefore, this paper proposes a feature selection algorithm for nonlinear dynamic conditional relevance (NDCRFS) through the study and analysis of the existing feature selection algorithm ideas and method. Firstly, redundancy and relevance between features and between features and class labels are discriminated by mutual information, conditional mutual information, and interactive mutual information. Secondly, the selected features and candidate features are dynamically weighted utilizing information gain factors. Finally, to evaluate the performance of this feature selection algorithm, NDCRFS was validated against 6 other feature selection algorithms on three classifiers, using 12 different data sets, for variability and classification metrics between the different algorithms. The experimental results show that the NDCRFS method can improve the quality of the feature subsets and obtain better classification results.
37
Kong, Mingjun, Xunhao Zhang et Faqiang Xu. « Optimization of Stock Price Time Series Prediction Model based on Karhunen-Loève Expansion and Information Gain Weighted Integrated Regression ». BCP Business & ; Management 33 (20 novembre 2022) : 341–48. http://dx.doi.org/10.54691/bcpbm.v33i.2774.
Texte intégralRésumé :
Time series prediction model plays an important role in stock price prediction, such as ARIMA, LSTM neural network. However, due to the need for stationary assumption of time series itself and the problems of high dimension and high noise, the common time series prediction methods have limitations. Based on this, this paper propose a framework for the optimization of the stock price time series prediction model. The proposed method uses the intra-day price as the auxiliary variable and obtains the function feature information based on Karhunen-Loève expansion. Considering that the feature variables after dimension reduction still have problems such as information loss and irrelevant noise. This paper use data enhancement method to improve the effective information of feature variables and reduce the influence of irrelevant noise. Then, since the potential model structure between the characteristic variable and the residual sequence is unknown, this paper develop a weighted ensemble regression method based on information gain to balance the variance and deviation of the prediction model, thereby improving the prediction accuracy. The actual data analysis results show that the proposed method can greatly improve the fitting accuracy of ARIMA and LSTM neural networks for stock prices. Finally, the optimization framework can also be used for the prediction of average temperature, air quality and port cargo flow.
38
Arsil, Poppy, Kusmantoro Eddy Sularso et Altri Mulyani. « Factors Influencing Consumer Preferences for Locally Produced Food : a Comparison between Traditional and Modern Markets ». International Journal of Engineering & ; Technology 7, no 3.30 (24 août 2018) : 535. http://dx.doi.org/10.14419/ijet.v7i3.30.18426.
Texte intégralRésumé :
Traditional and modern markets are both potential distribution channels for the distribution of locally produced food in Indonesia. The aim of the current study is to gain insight into consumer preferences for locally produced food when shopping at both markets. A total of 300 respondents were interviewed using stratified purposive sampling in traditional markets and supermarkets in Banyumas regency. Factor analysis was used to group consumer towards their preferences for local food. The overall KMO values were 0.746 for consumers who shopped in traditional markets and 0.835 for those who purchased locally produced food in modern markets, with a significance level of 0.000 for both segments of consumers. All individual MSAs also emerged as over 0.06. The results show that consumer preferences for locally grown products are very similar in both markets. Five factors were found considerably to influence consumer preferences in both markets, namely habit, food quality, product availability, the tendency to support local food, and the availability of information and knowledge. What makes the difference between markets is that supermarket shoppers have enthusiasm and proud eating local food product. The total variance for the six factors was 64.245% and 65.705% for traditional and modern markets, respectively.
39
Wadhvani, Rajesh, et Sanyam Shukla. « Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve ». Wind Engineering 43, no 3 (5 juin 2018) : 225–32. http://dx.doi.org/10.1177/0309524x18780398.
Texte intégralRésumé :
Wind turbine power curve provides technical specification of the wind turbine in the form of nominal wind power readings. This information may used to monitor the performance of the power system, estimate the power produced by the turbine, optimize the operational cost, and improve the reliability of the power system. However, this information is not sufficient to accomplish these tasks. To accomplish these tasks, the accurate modeling of the wind power curve is required. In this article, various curve fitting techniques, namely polynomial regression, locally weighted polynomial regression, spline regression, piecewise polynomial regression, and smoothing spline, have been applied to model the power curve of wind turbine. All these techniques have been used to model the power curve on National Renewable Energy Laboratory (NREL) 2012 dataset with site-id 124693.
40
Thompson, David F. « Gain-Bandwidth Optimal Design for the New Formulation Quantitative Feedback Theory ». Journal of Dynamic Systems, Measurement, and Control 120, no 3 (1 septembre 1998) : 401–4. http://dx.doi.org/10.1115/1.2805416.
Texte intégralRésumé :
Recent work in Quantitative Feedback Theory (QFT) has extended the methodology to include a more mathematically tractable formulation which allows consideration of both parametric and non-parametric uncertainty. An issue that remains largely unsolved is that of constructive existence conditions for optimal QFT controllers. The methodology developed in Thompson and Nwokah (1994) was to build upon the classical Bode analysis approach to loop shaping by defining the structure and parameters of an acceptable initial QFT controller a priori; locally optimal controllers of this structure could then be computed via a constrained nonlinear programming algorithm. The primary purpose of this paper is to extend this methodology to the sensitivity-based, “new formulation” QFT bounds. This technique is demonstrated for the nonminimum-phase problem of Nordgren et al. (1994).
41
Chang, R. J. « Optimal Linear Feedback Control for a Class of Nonlinear Nonquadratic Non-Gaussian Problems ». Journal of Dynamic Systems, Measurement, and Control 113, no 4 (1 décembre 1991) : 568–74. http://dx.doi.org/10.1115/1.2896459.
Texte intégralRésumé :
An optimal linear feedback controller designed for a class of nonlinear stochastic systems with nonquadratic performance criteria by a non-Gaussian approach is presented. The non-Gaussian method is developed through expressing the unknown stationary output density function as a weighted sum of the Gaussian densities with undetermined parameters. With the aid of a Gaussian-sum density, the optimal feedback gain for a control system with complete state information is derived. By assuming that the separation principle is valid for the class of stochastic systems, a nonlinear precomputed-gain filter is then implemented. The method is illustrated by a Duffing-type control system and the performance of a linear feedback controller designed through both quadratic and nonquadratic performance indices is compared.
42
Mehrban, Hossein, Masoumeh Naserkheil, Deuk Hwan Lee, Chungil Cho, Taejeong Choi, Mina Park et Noelia Ibáñez-Escriche. « Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle ». Genes 12, no 2 (12 février 2021) : 266. http://dx.doi.org/10.3390/genes12020266.
Texte intégralRésumé :
The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.
43
A. Ghaleb, Fuad, Faisal Saeed, Mohammad Al-Sarem, Bander Ali Saleh Al-rimy, Wadii Boulila, A. E. M. Eljialy, Khalid Aloufi et Mamoun Alazab. « Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET ». Electronics 9, no 9 (1 septembre 2020) : 1411. http://dx.doi.org/10.3390/electronics9091411.
Texte intégralRésumé :
Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.
44
Hu, Xiaogang, et Karl M. Newell. « Aging, visual information, and adaptation to task asymmetry in bimanual force coordination ». Journal of Applied Physiology 111, no 6 (décembre 2011) : 1671–80. http://dx.doi.org/10.1152/japplphysiol.00760.2011.
Texte intégralRésumé :
This study investigated the coordination and control strategies that the elderly adopt during a redundant finger force coordination task and how the amount of visual information regulates the coordination patterns. Three age groups (20–24, 65–69, and 75–79 yr) performed a bimanual asymmetric force task. Task asymmetry was manipulated via imposing different coefficients on the finger forces such that the weighted sum of the two index finger forces equaled the total force. The amount of visual information was manipulated by changing the visual information gain of the total force output. Two hypotheses were tested: the reduced adaptability hypothesis predicts that the elderly show less degree of force asymmetry between hands compared with young adults in the asymmetric coefficient conditions, whereas the compensatory hypothesis predicts that the elderly exhibit more asymmetric force coordination patterns with asymmetric coefficients. Under the compensatory hypothesis, two contrasting directions of force sharing strategies (i.e., more efficient coordination strategy and minimum variance strategy) are expected. A deteriorated task performance (high performance error and force variability) was found in the two elderly groups, but enhanced visual information improved the task performance in all age groups. With low visual information gain, the elderly showed reduced adaptability (i.e., less asymmetric forces between hands) to the unequal weighting coefficients, which supported the reduced adaptability hypothesis; however, the elderly revealed the same degree of adaptation as the young group under high visual gain. The findings are consistent with the notion that the age-related reorganization of force coordination and control patterns is mediated by visual information and, more generally, the interactive influence of multiple categories of constraints.
45
Burkholz, Rebekka, et John Quackenbush. « Cascade Size Distributions : Why They Matter and How to Compute Them Efficiently ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 8 (18 mai 2021) : 6840–49. http://dx.doi.org/10.1609/aaai.v35i8.16844.
Texte intégralRésumé :
Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or inaccurate. As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well, scales to large networks, and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data.
46
Moussaoui, Nouha, et Mohamed Achache. « A Weighted-path Following Interior-point Algorithm for Convex Quadratic Optimization Based on Modified Search Directions ». Statistics, Optimization & ; Information Computing 10, no 3 (25 juin 2022) : 873–89. http://dx.doi.org/10.19139/soic-2310-5070-1385.
Texte intégralRésumé :
Getting a perfectly centered initial point for feasible path-following interior-point algorithms is a hard practical task. Therefore, it is worth to analyze other cases when the starting point is not necessarily centered. In this paper, we propose a short-step weighted-path following interior-point algorithm (IPA) for solving convex quadratic optimization (CQO). The latter is based on a modified search direction which is obtained by the technique of algebraically equivalent transformation (AET) introduced by a new univariate function to the Newton system which defines the weighted-path. At each iteration, the algorithm uses only full-Newton steps and the strategy of the central-path for tracing approximately the weighted-path. We show that the algorithm is well-defined and converges locally quadratically to an optimal solution of CQO. Moreover, we obtain the currently best known iteration bound, namely, $\mathcal{O}\left(\sqrt{n}\log \dfrac{n}{\epsilon}\right)$ which is as good as the bound for linear optimization analogue. Some numerical results are given to evaluate the efffficiency of the algorithm.
47
Page, Hector J. I., Daniel M. Walters, Rebecca Knight, Caitlin E. Piette, Kathryn J. Jeffery et Simon M. Stringer. « A theoretical account of cue averaging in the rodent head direction system ». Philosophical Transactions of the Royal Society B : Biological Sciences 369, no 1635 (5 février 2014) : 20130283. http://dx.doi.org/10.1098/rstb.2013.0283.
Texte intégralRésumé :
Head direction (HD) cell responses are thought to be derived from a combination of internal (or idiothetic) and external (or allothetic) sources of information. Recent work from the Jeffery laboratory shows that the relative influence of visual versus vestibular inputs upon the HD cell response depends on the disparity between these sources. In this paper, we present simulation results from a model designed to explain these observations. The model accurately replicates the Knight et al. data. We suggest that cue conflict resolution is critically dependent on plastic remapping of visual information onto the HD cell layer. This remap results in a shift in preferred directions of a subset of HD cells, which is then inherited by the rest of the cells during path integration. Thus, we demonstrate how, over a period of several minutes, a visual landmark may gain cue control. Furthermore, simulation results show that weaker visual landmarks fail to gain cue control as readily. We therefore suggest a second longer term plasticity in visual projections onto HD cell areas, through which landmarks with an inconsistent relationship to idiothetic information are made less salient, significantly hindering their ability to gain cue control. Our results provide a mechanism for reliability-weighted cue averaging that may pertain to other neural systems in addition to the HD system.
48
Wu, Simeng, Jun Gong, Fei Liu et Laizong Huang. « Multi-step locally expansion detection method using dispersed seeds for overlapping community ». ITM Web of Conferences 47 (2022) : 02008. http://dx.doi.org/10.1051/itmconf/20224702008.
Texte intégralRésumé :
The local expansion method is a novel and promising community detection algorithm. Just based on part of network information, it can detect overlapping communities effectively, but some problems exist such as seed node aggregation, poor quality and inaccurate community coverage. Therefore, we propose a local expansion overlapping community detection algorithm based on dispersed seeds. There are four essential parts of this algorithm: 1) We firstly generate non-overlapping partitions of the network, and locate seed nodes with the largest influence in their own partition by using a new index of node influence, which combines the information centrality of nodes and the number of k-order neighbors. 2) Secondly, on the condition of the neighborhood overlap measure maximization, seed nodes merge unseeded nodes to generate a preliminary seed community; 3) Then based on the community conductance gain, the allocated nodes are screened and the free nodes are assigned to the seed community; 4) In the end, a node-community similarity based on common connection edge is proposed to re-allocate new free nodes and obtain the final community structure. This method can make the community distribution more proper and the coverage more reasonable. The experimental results on some artificial data and real network data show that the algorithm performs well on overlapping community indicators such as EQ and ONMI, while the community detection results are more stable.
49
Wang, Nan, et Evangelos Katsamakas. « A Network Data Science Approach to People Analytics ». Information Resources Management Journal 32, no 2 (avril 2019) : 28–51. http://dx.doi.org/10.4018/irmj.2019040102.
Texte intégralRésumé :
The best companies compete with people analytics. They maximize the business value of their people to gain competitive advantage. This article proposes a network data science approach to people analytics. Using data from a software development organization, the article models developer contributions to project repositories as a bipartite weighted graph. This graph is projected into a weighted one-mode developer network to model collaboration. Techniques applied include centrality metrics, power-law estimation, community detection, and complex network dynamics. Among other results, the authors validate the existence of power-law relationships on project sizes (number of developers). As a methodological contribution, the article demonstrates how network data science can be used to derive a broad spectrum of insights about employee effort and collaboration in organizations. The authors discuss implications for managers and future research directions.
50
Grosser, Malte, Susanne Gellißen, Patrick Borchert, Jan Sedlacik, Jawed Nawabi, Jens Fiehler et Nils D. Forkert. « Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets ». PLOS ONE 15, no 11 (5 novembre 2020) : e0241917. http://dx.doi.org/10.1371/journal.pone.0241917.
Texte intégralRésumé :
Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.