Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition by Aiping Zhang, Guang Ren, Yejin Lin, Baozhu Jia, Hui Cao, Jundong Zhang, Shubin Zhang

“…Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. …”
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Exact Receptance Function of Cracked Beams and Its Application for Crack Detection by Khoa Viet Nguyen, Mai Van Cao

“…This might be useful for the detection of cracks. In this paper, the derivation of exact receptance of the beam with general boundary conditions is presented, and the numerical simulations are provided.…”
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Linear SVM-Based Android Malware Detection for Reliable IoT Services by Hyo-Sik Ham, Hwan-Hee Kim, Myung-Sup Kim, Mi-Jung Choi

“…In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. …”
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Development of New Tools to Detect Colistin-Resistance among Enterobacteriaceae Strains by Lucie Bardet, Jean-Marc Rolain

“…The large number of mechanisms and genes involved in colistin resistance limits the access to rapid detection by molecular biology. It is therefore necessary to implement well-defined protocols using specific tools to detect all colistin-resistant bacteria. …”
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Luminescence and a new approach for detecting heat treatment of geuda sapphire by T. Pluthametwisute, T. Pluthametwisute, L. Nasdala, C. Chanmuang N., M. Wildner, E. Libowitzky, G. Giester, E. G. Zoysa, C. Jakkawanvibul, W. Suwanmanee, T. Sripoonjan, T. Tengchaisri, B. Wanthanachaisaeng, C. Sutthirat

“…The present study offers new aspects that support the detection of heat treatment of sapphire. Natural geuda sapphire exhibits orange to red luminescence under longwave ultraviolet (LWUV; 365 nm) light, while heated geuda sapphire shows blue luminescence under shortwave ultraviolet (SWUV; 225 nm) light. …”
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A novel feature selection technique: Detection and classification of Android malware by Sandeep Sharma, Prachi, Rita Chhikara, Kavita Khanna

“…In light of this, the objective of this research is to uncover a few features that can significantly enhance the detection of Android malware through utilization of feature engineering. …”
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The Assessment of Cholinesterase from the Liver of Puntius Javanicus as Detection of Metal Ions by Mohd Khalizan Sabullah, Mohd Rosni Sulaiman, Mohd Yunus Abd Shukor, Mohd Arif Syed, Nor Aripin Shamaan, Ariff Khalid, Siti Aqlima Ahmad

“…The results showed that P. javanicus ChE has the potential to be used as a biosensor for the detection of metal ions.…”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The proposed spam detection framework SD-FSL-CLSTM used the fusion of spammer behavior features and linguistic features which automatically detect and classify the spam reviews. …”
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Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images by Wenbo Pang, Yi Ma, Huiyan Jiang, Qiming Yu

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Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform by Bo Chen, Zhi-wei Chen, Gan-jun Wang, Wei-ping Xie

“…To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. …”
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Information Processing Features Can Detect Behavioral Regimes of Dynamical Systems by Rick Quax, Gregor Chliamovitch, Alexandre Dupuis, Jean-Luc Falcone, Bastien Chopard, Alfons G. Hoekstra, Peter M. A. Sloot

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Adenoma Detection Rates Decline with Increasing Procedural Hours in an Endoscopist’s Workload by Majid A Almadi, Maida Sewitch, Alan N Barkun, Myriam Martel, Lawrence Joseph

“…BACKGROUND: Operator fatigue may negatively influence adenoma detection (AD) during screening colonoscopy.…”
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On Fault Detection in Linear Discrete-Time, Periodic, and Sampled-Data Systems by P. Zhang, S. X. Ding

“…This paper gives a review of some standard fault-detection (FD) problem formulations in discrete linear time-invariant systems and the available solutions. …”
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Cognitive Phenotypes, Brain Morphometry and the Detection of Cognitive Decline in Preclinical AD by Mark W. Jacobson, Linda K. McEvoy, Anders Dale, Christine Fennema-Notestine

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Method and device for detection of insulation breakdown on hull in power circuits of locomotives by A. N. Kalyakulin

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Navigating ALICE: Advancements in Deployable Docking and Precision Detection for AUV Operations by Yevgeni Gutnik, Nir Zagdanski, Sharon Farber, Tali Treibitz, Morel Groper

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A case of stromal sarcoma of the prostate with the incidental detection of prostate adenocarcinoma by Shang Xu, Xinning Wang, Wei Jiao

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Construction and application of march tests for pattern sensitive memory faults detection by V. N. Yarmolik, V. A. Levantsevich, D. V. Demenkovets, I. Mrozek

“…In addition, the estimates of the mean value of the multiplicity of the MATS++, March C− and March PS tests, obtained on the basis of a mathematical model describing the problem of the coupon collector, and ensuring the detection of all k2k PNPSFk faults are given. The validity of analytical estimates is experimentally shown and the high efficiency of PNPSFk fault detection is confirmed by tests of the March PS type.…”
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Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies by Lisa Kessler, Klaus Bogenberger

“…This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. …”
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Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning by Minjuan Zhong, Zhenjin Li, Shengzong Liu, Bo Yang, Rui Tan, Xilong Qu

“…The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. …”
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