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838 results on '"True positive rate"'

2. Diagnosing Osteoporosis in Postmenopausal Females Using Machine Learning and AdaBoostM1 Algorithm Based on Bone Mineral Density.

Author

Jabbar, Sohail, Ahmad, Awais, and Tariq, Saba

Subjects
*BONE density, *MACHINE learning, *OSTEOPOROSIS in women, *POSTMENOPAUSE, *BONE fractures, *EARLY diagnosis
Abstract

Osteoporosis, also known as low bone mineral density (BMD), is a serious health concern, especially for women who have gone through menopause in community settings. This condition weakens the bones and increases the risk of fractures. Women who have gone through menopause are more susceptible to osteoporosis due to hormonal changes. Therefore, it is crucial to identify the condition early to start preventive treatments and reduce the risk of fractures. To address the challenges of diagnosing low BMD in postmenopausal women in community settings, this study proposes a method that combines machine learning with the AdaBoostM1 algorithm, which has shown promising results. Data acquisition, data preprocessing, data training, model testing, and model prediction and evaluation are integral phases of the operational dynamics of our model in osteoporosis diagnosis. This approach recommends increasing screening initiatives and educating patients as strategies to improve early detection and management of the disease. The analysis method used achieved an impressive accuracy rate of approximately 88.8% on the dataset it was applied to. The area under the curve was 0.87, the true positive rate was 88%, and the F1 measure was 0.88. By using accurate diagnostic techniques and providing proactive community care, the incidence of osteoporotic fractures can be significantly reduced, thus improving the quality of life for this vulnerable population. [ABSTRACT FROM AUTHOR]

Published
2024
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3. MSRFNet for skin lesion segmentation and deep learning with hybrid optimization for skin cancer detection.

Author

Baskaran, Diwan, Nagamani, Yanda, Merugula, Suneetha, and Premnath, S P

Subjects
*DEEP learning, *SKIN cancer, *EARLY detection of cancer, *BLENDED learning, *FEATURE extraction, *OPTIMIZATION algorithms
Abstract

Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Clinical Diagnosis Application of Metagenomic Next-Generation Sequencing of Plasma in Suspected Sepsis

Author

Wang L, Li S, Qin J, Tang T, Hong J, Tung TH, Xu C, Yu S, and Qian J

Subjects
clinical concordance, true positive rate, metagenomic next-generation sequencing, blood infection, sepsis, Infectious and parasitic diseases, RC109-216
Abstract

Lisha Wang,1 Shixiao Li,1 Jiajia Qin,1 Tianbin Tang,1 Jiawen Hong,1 Tao-Hsin Tung,2 Chunyan Xu,1 Sufei Yu,1 Jiao Qian1 1Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, People’s Republic of China; 2Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Linhai, People’s Republic of ChinaCorrespondence: Sufei Yu; Jiao Qian, Email yusf@enzemed.com; qianj@enzemed.comPurpose: We analyzed the clinical concordance of mNGS test results from blood samples and improved the clinical efficiency of mNGS in the diagnosis of suspected sepsis pathogens.Patients and Methods: In this study, 99 samples of suspected blood flow infection were included for plasma mNGS, and the correlation between mNGS results and blood culture results, serum inflammatory indices, clinical symptoms and antibiotic treatment was analyzed, as well as the comparison with the detection rate of BALF pathogens, as well as the classification of different pathogens in the mNGS results were analyzed.Results: The mNGS pathogen detection rate was higher than that of traditional blood culture (83.02% vs 35.82%). The rate of the mNGS results being consistent with the clinical diagnosis was also higher than that of traditional blood culture (58.49% vs 20.75%). This study shows that bacteria and fungi are the main pathogens in sepsis, and viral sepsis is very rare. In this study, 32% of sepsis patients were secondary to pneumonia. Compared with the pathogen detection rate using alveolar lavage fluid, the detection rate from plasma mNGS was 62.5%. Samples were also easy to sample, noninvasive, and more convenient for clinical application.Conclusion: This study shows that compared with blood culture, the detection rate of mNGS pathogen that meets the diagnosis of sepsis is higher. We need a combination of multiple indicators to monitor the early diagnosis and treatment of sepsis.Keywords: clinical concordance, true positive rate, metagenomic next-generation sequencing, blood infection, sepsis

Published
2023

6. Automatic Modulation Classification Using Cumulants and Ensemble Classifiers

Author

Venkata Subbarao, M., Samundiswary, P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kalya, Shubhakar, editor, Kulkarni, Muralidhar, editor, and Shivaprakasha, K. S., editor

Published
2021
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7. Effective Detection of Cyber Attack in a Cyber-Physical Power Grid System

Author

Otokwala, Uneneibotejit, Petrovski, Andrei, Kalutarage, Harsha, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2021
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8. Perbandingan AlexNet dan VGG untuk Pengenalan Ekspresi Wajah pada Dataset Kelas Komputasi Lanjut.

Author

Nurdiati, Sri, Najib, Mohamad Khoirun, Bukhari, Fahren, Ardhana, Muhammad Reza, Rahmah, Salsabilla, and Blante, Trianty Putri

Subjects
FACIAL expression, DATA augmentation, EMOTION recognition, ELECTRONIC data processing
Abstract

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2022
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9. Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data.

Author

Kim, Doyun and Heo, Tae-Young

Subjects
*DEEP learning, *MACHINE learning, *ANOMALY detection (Computer security), *FISHER discriminant analysis, *FEATURE extraction, *INTERNET of things
Abstract

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Considerations on the region of interest in the ROC space.

Author

Lavazza, Luigi and Morasca, Sandro

Subjects
*RECEIVER operating characteristic curves, *PYTHON programming language, *BORDERLANDS, *STATISTICAL correlation, *SOFTWARE engineers, *SOFTWARE engineering
Abstract

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that ϕ (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method. [ABSTRACT FROM AUTHOR]

Published
2022
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11. Combining biomarkers by maximizing the true positive rate for a fixed false positive rate.

Author

Meisner, Allison, Carone, Marco, Pepe, Margaret S., and Kerr, Kathleen F.

Abstract

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution‐free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption. [ABSTRACT FROM AUTHOR]

Published
2021
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12. Ad Fraud Measure and Benchmark

Author

Zhu, Xingquan, Tao, Haicheng, Wu, Zhiang, Cao, Jie, Kalish, Kristopher, Kayne, Jeremy, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Zhu, Xingquan, Tao, Haicheng, Wu, Zhiang, Cao, Jie, Kalish, Kristopher, and Kayne, Jeremy

Published
2017
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14. Effectiveness of Basic and Advanced Sampling Strategies on the Classification of Imbalanced Data. A Comparative Study Using Classical and Novel Metrics

Author

Kraiem, Mohamed S., Moreno, María N., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Martínez de Pisón, Francisco Javier, editor, Urraca, Rubén, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2017
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15. Thistle Detection

Author

Olsen, Søren I., Nielsen, Jon, Rasmussen, Jesper, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sharma, Puneet, editor, and Bianchi, Filippo Maria, editor

Published
2017
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20. Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Author

Doyun Kim and Tae-Young Heo

Subjects
anomaly detection, hydraulic components, IoT sensor, Boruta algorithm, true negative rate, true positive rate, Chemical technology, TP1-1185
Abstract

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study’s findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers’ judgment.

Published
2022
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21. Autoscaling Bloom filter: controlling trade-off between true and false positives.

Author

Kleyko, Denis, Rahimi, Abbas, Gayler, Ross W., and Osipov, Evgeny

Subjects
*FILTERS & filtration, *ARTIFICIAL neural networks, *MATHEMATICAL analysis, *PROBABILISTIC databases
Abstract

A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate. [ABSTRACT FROM AUTHOR]

Published
2020
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22. Universal Matched-Filter Template Versus Individualized Template for Single Trial Detection of Movement Intentions of Different Tasks

Author

Akmal, Muhammad, Jochumsen, Mads, Navid, Muhammad Samran, Shafique, Muhammad, Zaidi, Syed Muhammad Tahir, Taylor, Denise, Niazi, Imran Khan, Howlett, Robert James, Series editor, Jain, Lakhmi C., Series editor, Bassis, Simone, editor, Esposito, Anna, editor, Morabito, Francesco Carlo, editor, and Pasero, Eros, editor

Published
2016
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24. Measuring the Performance of a Classifier

Author

Bramer, Max, Mackie, Ian, Series editor, Abramsky, Samson, Advisory board, Breitman, Karin, Advisory board, Hankin, Chris, Advisory board, Kozen, Dexter C., Advisory board, Pitts, Andrew, Advisory board, Riis Nielson, Hanne, Advisory board, Skiena, Steven S, Advisory board, Stewart, Iain, Advisory board, and Bramer, Max

Published
2016
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26. Alignment of Tractograms as Linear Assignment Problem

Author

Sharmin, Nusrat, Olivetti, Emanuele, Avesani, Paolo, Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Fuster, Andrea, editor, Ghosh, Aurobrata, editor, Kaden, Enrico, editor, Rathi, Yogesh, editor, and Reisert, Marco, editor

Published
2016
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27. Gesture-Based Continuous Authentication for Wearable Devices: The Smart Glasses Use Case

Author

Chauhan, Jagmohan, Asghar, Hassan Jameel, Mahanti, Anirban, Kaafar, Mohamed Ali, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Josef, Kittler, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Manulis, Mark, editor, Sadeghi, Ahmad-Reza, editor, and Schneider, Steve, editor

Published
2016
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28. Component-Based Ethnicity Identification from Facial Images

Author

Boyseens, A., Viriri, S., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chmielewski, Leszek J., editor, Datta, Amitava, editor, Kozera, Ryszard, editor, and Wojciechowski, Konrad, editor

Published
2016
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29. Principal components-based hidden Markov model for automatic detection of whale vocalisations.

Author

Usman, A.M. and Versfeld, D.J.J.

Subjects
*HUMPBACK whale, *HIDDEN Markov models, *FEATURE extraction, *WHALES, *PRINCIPAL components analysis
Abstract

Over the years, researchers have continued to put forward solutions to lessen the threats faced by whales within their ecosystem. The correct detection of the different species of whale is important in the search for solutions that will lessen the threats. In order to accurately detect and classify whale species, a number of techniques have been proposed over the years, with varying degrees of success. This research seeks to improve the performance of the hidden Markov models (HMMs), which is one of the most consistent methods for the detection and classification of whale vocalisations. The performance of HMMs is affected by the quality of the feature vectors fed into them. Thus, this research proposes feature extraction (FE) techniques based on principal component analysis. The principal components (PC) computed from PCA and kernel PCA were uniquely transformed into feature vector structures suitable for the HMMs. The emerging models, PCA-HMMs and kPCA-HMMs, were experimented with on passive acoustic monitoring (PAM) datasets containing southern right whale and humpback whale vocalisations. The results from the experiments showed that the kPCA-HMMs outperformed PCA-HMMs. This is due to kPCA's ability to find non-linear subspaces that may exist in whale vocalisations. Furthermore, the results of the developed PC-HMMs were compared with other existing FE techniques used with HMMs in the literature for the detection of whale vocalisations. The proposed PC-HMMs did not only outperform the existing FE-HMMs but also offered lower computational complexity than the existing HMMs for the detection of whale vocalisations. • Developed models for the automatic detection of whale species. • Whale features are extracted using PCA and kernel PCA. • Features are used with HMM for the detection of humpback and southern right whale. • Methodology can be modified for automatic detection of other vocalising animals. • Models contribute to whale conservation efforts. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Monitoring Rail Condition Based on Sound and Vibration Sensors Installed on an Operational Train

Author

Jensen, T., Chauhan, S., Haddad, K., Song, W., Junge, S., Boersma, Bendiks Jan, Series editor, Fujii, Kozo, Series editor, Haase, Werner, Series editor, Leschziner, Michael A., Series editor, Periaux, Jacques, Series editor, Pirozzoli, Sergio, Series editor, Rizzi, Arthur, Series editor, Roux, Bernard, Series editor, Shokin, Yurii I., Series editor, Nielsen, Jens C.O., editor, Anderson, David, editor, Gautier, Pierre-Etienne, editor, Iida, Masanobu, editor, Nelson, James T., editor, Thompson, David, editor, Tielkes, Thorsten, editor, Towers, David A., editor, and de Vos, Paul, editor

Published
2015
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33. Predicting Micro-Level Behavior in Online Communities for Risk Management

Author

Hiscock, Philippa A., Avramidis, Athanassios N., Fliege, Jörg, Bock, Hans-Hermann, Editor-in-chief, Gaul, Wolfgang, Editor-in-chief, Baier, Daniel, Series editor, Vichi, Maurizio, Editor-in-chief, Critchley, Frank, Series editor, Decker, Reinhold, Series editor, Diday, Edwin, Series editor, Greenacre, Michael, Series editor, Lauro, Carlo Natale, Series editor, Meulman, Jacqueline, Series editor, Monari, Paola, Series editor, Nishisato, Shizuhiko, Series editor, Ohsumi, Noboru, Series editor, Opitz, Otto, Series editor, Ritter, Gunter, Series editor, Schader, Martin, Series editor, Weihs, Claus, Editor-in-chief, Lausen, Berthold, editor, Krolak-Schwerdt, Sabine, editor, and Böhmer, Matthias, editor

Published
2015
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35. Outlier Detection in Cox Proportional Hazards Models Based on the Concordance c-Index

Author

Pinto, João Diogo, Carvalho, Alexandra M., Vinga, Susana, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pardalos, Panos, editor, Pavone, Mario, editor, Farinella, Giovanni Maria, editor, and Cutello, Vincenzo, editor

Published
2015
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36. Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data

Author

Neugebauer, Judith, Kramer, Oliver, Sonnenschein, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Woon, Wei Lee, editor, Aung, Zeyar, editor, and Madnick, Stuart, editor

Published
2015
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37. Plane Extraction for Indoor Place Recognition

Author

Potena, Ciro, Pretto, Alberto, Bloisi, Domenico D., Nardi, Daniele, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Battiato, Sebastiano, editor, Blanc-Talon, Jacques, editor, Gallo, Giovanni, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2015
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38. Digital Waste Sorting: A Goal-Based, Self-Learning Approach to Label Spam Email Campaigns

Author

Sheikhalishahi, Mina, Saracino, Andrea, Mejri, Mohamed, Tawbi, Nadia, Martinelli, Fabio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Foresti, Sara, editor

Published
2015
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39. Uncertainty-Driven Forest Predictors for Vertebra Localization and Segmentation

Author

Richmond, David, Kainmueller, Dagmar, Glocker, Ben, Rother, Carsten, Myers, Gene, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Navab, Nassir, editor, Hornegger, Joachim, editor, Wells, William M., editor, and Frangi, Alejandro, editor

Published
2015
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42. Improving Aggregated Search Coherence

Author

Arguello, Jaime, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hanbury, Allan, editor, Kazai, Gabriella, editor, Rauber, Andreas, editor, and Fuhr, Norbert, editor

Published
2015
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49. Rate-Constrained Ranking and the Rate-Weighted AUC

Author

Millard, Louise A. C., Flach, Peter A., Higgins, Julian P. T., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Calders, Toon, editor, Esposito, Floriana, editor, Hüllermeier, Eyke, editor, and Meo, Rosa, editor

Published
2014
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50. Boosted Fractal Integral Paths for Object Detection

Author

Ehlers, Arne, Baumann, Florian, Rosenhahn, Bodo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, McMahan, Ryan, editor, Jerald, Jason, editor, Zhang, Hui, editor, Drucker, Steven M., editor, Kambhamettu, Chandra, editor, El Choubassi, Maha, editor, Deng, Zhigang, editor, and Carlson, Mark, editor

Published
2014
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