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7th Annual Privacy Forum (APF)

作者： Raschke, Philip Zickau, Sebastian Kroeger, Jacob Leon Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Weizenbaum Inst Telekom Innovat Labs Berlin Germany

ISBN: (纸本)9783030217525;9783030217518

Targeted advertising is an inherent part of the modern Web as we know it. For this purpose, personal data is collected at large scale to optimize and personalize displayed advertisements to increase the probability that we click them. Anonymity and privacy are also important aspects of the World Wide Web since its beginning. Activists and developers relentlessly release tools that promise to protect us from Web tracking. Besides extensive blacklists to block Web trackers, researchers used machine learning techniques in the past years to automatically detect Web trackers. However, for this purpose often artificial data is used, which lacks in quality. Due to its sensitivity and the manual effort to collect it, real user data is avoided. Therefore, we present *** - The Transparency EXtension, which aims to record a browsing session in a secure and privacy-preserving manner. We define requirements and objectives, which are used for the design of the tool. An implementation is presented, which is evaluated for its performance. The evaluation shows that our implementation can be used for the collection of data to feed machine learning algorithms.

关键词： Web tracking Browsing behavior Data privacy Browser extension Data quality Machine-learning classification algorithm

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Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 179卷

作者： Hu, Shuwen Ingham, Aaron Schmoelzl, Sabine McNally, Jody Little, Bryce Smith, Daniel Bishop-Hurley, Greg Wang, You-Gan Li, Yutao Queensland Univ Technol Sch Math Sci Gardens Point Qld 4001 Australia CSIRO Agr & Food 306 Carmody Rd St Lucia Qld 4067 Australia CSIRO Agr & Food Armidale NSW 2350 Australia CSIRO DATA61 Private Bag 12 Hobart Tas 7001 Australia

Inertial motion sensors located on the animal have been used to study the behaviour of ruminant livestock. The time window size of segmented signal data can significantly affect the classification accuracy of animal behaviours. To date, there have been no studies evaluating the impact of a mixture of time window size features on the accuracy of animal behaviour classification. In this study, data was collected from accelerometers attached to the neck of 17 Merino sheep over a period of two days. We also recorded a ground truth dataset of behaviour recordings (grazing, ruminating, walking, and standing) over the same time period, We then investigated the ability of three machine learning (ML) approaches, Random Forest (RF), Support Vector Machine (SVM) and linear discriminant analysis (LDA), to accurately classify sheep behaviour. Our results clearly show that simultaneous inclusion of features derived from time windows of mixed sizes, ranging from 2 to 15 s, significantly improved the behaviour classification accuracy, in comparison to those determined from a single unique time window size. Of the three ML methods applied here, the RF approach yielded the best results. Together our results show that including features obtained from mixed window sizes improved the classification accuracy of sheep behaviours.

关键词： Mixture of time window sizes Features ranking classification algorithm Machine learning Sheep behaviour Accelerometer

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Impedance technique combined with supervised algorithms-based internal degradation state classification and its economic analysis for safety in retired battery pack

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JOURNAL OF ENERGY STORAGE 2023年 73卷

作者： Lee, Miyoung Han, Dongho Yoo, Kisoo Kim, Jonghoon Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea Yeungnam Univ Sch Mech Engn Gyoungsan 38541 South Korea

As the growth of lithium-ion batteries (LIBs) applications accelerates, future problems for waste LIB disposal are also appeared. When LIB reaches 80 % of the initial capacity, it is defined as the End-of-life (EOL). Since power and capacity of the LIB abruptly decreases at this point, it is determined that the LIB can't be used. However, in the case of the battery pack, even if it reaches 80 % of its initial capacity, it can be reused with secondary applications such as energy storage devices and uninterruptible power supplies (UPS). Therefore, in order to reduce environmental problems, such a battery pack reuse technique is required. However, as the internal degradation state of the LIB differs depending on the operating environment, research is needed to select the LIB in the same state through classification algorithm and configure the waste battery pack. Since the internal degradation of LIB cannot be evaluated qualitatively with existing technology, like capacity and internal resistance, so a new qualitative evaluation factor is required. This research classified an internal degradation state according to LIB environment using an impedance technique and supervised algorithms (Decision tree, Support vector machine, K-nearest neighbor) that could qualitatively represent LIB degradation mechanism. And it is validated that new parameters are good at classifying operating environment compared with existing parameters. Also, Economic analysis was performed for comparison with existing methods, and as a result, it was proved that it showed superior performance compared to existing techniques in both classification accuracy and economic analysis.

关键词： Electrochemical impedance spectroscopy (EIS) classification algorithm Support vector machine (SVM) Decision tree algorithm K-nearest neighbor algorithm (KNN) Battery operating environment classification

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A classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

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WATER 2022年第4期14卷

作者： Kim, Jongchan Jonoski, Andreja Solomatine, Dimitri P. IHE Delft Inst Water Educ Hydroinformat Chair Grp NL-2611 AX Delft Netherlands K Water Human Resources Dev Inst Daejeon 34350 South Korea Delft Univ Technol Water Resources Sect NL-2628 CD Delft Netherlands Water Problems Inst RAS Gubkina 3 Moscow 117971 Russia

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

关键词： cyanobacterial blooms machine learning classification algorithm feature selection imbalanced dataset oversampling

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Approximation with polynomial kernels and SVM classifiers

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ADVANCES IN COMPUTATIONAL MATHEMATICS 2006年第1-3期25卷 323-344页

作者： Zhou, Ding-Xuan Jetter, Kurt City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China Univ Hohenheim Inst Angew Math & Stat D-70593 Stuttgart Germany

This paper presents an error analysis for classification algorithms generated by regularization schemes with polynomial kernels. Explicit convergence rates are provided for support vector machine (SVM) soft margin classifiers. The misclassification error can be estimated by the sum of sample error and regularization error. The main difficulty for studying algorithms with polynomial kernels is the regularization error which involves deeply the degrees of the kernel polynomials. Here we overcome this difficulty by bounding the reproducing kernel Hilbert space norm of Durrmeyer operators, and estimating the rate of approximation by Durrmeyer operators in a weighted L-1 space (the weight is a probability distribution). Our study shows that the regularization parameter should decrease exponentially fast with the sample size, which is a special feature of polynomial kernels.

关键词： classification algorithm regularization scheme polynomial kernel approximation by Durrmeyer operators support vector machine misclassification error

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Research on TSV Void Defects Based on Machine Learning 19

Research on TSV Void Defects Based on Machine Learning

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3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Chen, Shouhong Kang, Huaiqiang Ma, Jun Guo, Ling Hou, Xingna Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Sch Elect Engn & Automat Guilin Peoples R China

ISBN: (纸本)9781450362948

With the rapid development of 3D TSV (through silicon via) technology, it is particularly important to improve the yield for TSV fault detection. Aiming at TSV void defects, the paper adopts supervised machine learning method to train S parameters in TSV model with void faults, and carries out classification processing, then predicts the size of void faults through stimulus signal and S parameters. The results show that for spherical void defects detection, the classification accuracy of ELM (Extreme Learning Machine) algorithm and KNN (K-Nearest Neighbor) algorithm is above 85%, while for TSV cylindrical void defects detection, the classification accuracy of ELM algorithm is 96%.

关键词： Machine learning TSV fault detection classification algorithm

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Analysis of Student Performance to Predict Career Specialization using Random Forest Data Mining Technique 24

Analysis of Student Performance to Predict Career Specializa...

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9th International Conference on Intelligent Information Technology (ICIIT)

作者： Hermogenes, Mary Grace G. Repaso, Jennifer Anne A. Perez, Joann G. Bulacan State Univ Malolos Philippines

ISBN: (纸本)9798400716713

Career specialization has emerged as a critical factor for students to attain success in today's highly competitive job market. Therefore, analyzing students' academic performance data to predict their career specialization can be a valuable tool for educational institutions. This study uses Random Forest to forecast graduates' job specialties. The study employed 360 records of IT graduates and the related grades from their technical/major subjects. The conducted tracer research was used to determine the graduates' present occupations. The model made use of 16 attributes in total. An accuracy rate of 87.19% was obtained from the prediction, which is quite good. The collected data also revealed that while students who perform fairly well in most of the major topics may have another relevant vocation, those who achieve very well will likely obtain a job as a developer.

关键词： Career Prediction classification algorithm Learning Analytics Prediction Random Forest

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Two-layered ensemble Kohonen nets for imbalanced streaming data

Two-layered ensemble Kohonen nets for imbalanced streaming d...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Roy, Asim Arizona State Univ Dept Informat Syst Tempe AZ 85287 USA

ISBN: (纸本)9781509006229

One of the dominant application areas of machine learning will be the industrial internet (Internet of Things), although most of the data will actually be confined to smaller internal networks and will not use the Internet. In this world of the industrial internet, streaming sensor data with rare events, such as machine or process failures and disruptions, will be the dominant form of data and it poses a particular problem for machine learning because its methods are generally not designed to handle imbalanced data. In this paper, I present a new classification algorithm for streaming imbalanced data that uses Kohonen nets at its core. To handle high-velocity streaming data, the proposed algorithm can be implemented on hardware to take advantage of massive parallelism. I provide an outline of the algorithm and some preliminary computational results.

关键词： Kohonen nets ensemble learning imbalanced data streaming data industrial internet classification algorithm

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Interest Mining algorithm based on Blog Information

Interest Mining Algorithm based on Blog Information

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Ou, Guohua Xu, Changjian Zhan, Haoxun Qin, Yong Huang, Han South China Univ Technol Sch Software Engn Guang Zhou Guang Dong Peoples R China Dongguan Univ Technol Sch Comp Dong Guan Guangdong Peoples R China

ISBN: (纸本)9783037858882

Recently, the amount of blogs on the Internet rises sharply. Hence, mining valuable information in blogs possesses realistic significance for improving user experience, network services, etc. This paper proposes a mining algorithm with blog authors' interests based on classification techniques, which introduces an evaluation standard of non-empty intersection. This algorithm can also improve the hit ratio of recommendation service based on blog authors' interests by means of the interest collection from expanding prediction;therefore, it can reach a higher degree of satisfaction. In addition, this paper performs experiments with the data set from Sina Blog and NetEase Blog, whose result illustrates the higher accuracy of our algorithm.

关键词： Blog mining Interest mining classification algorithm Non-empty intersection

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition 38

The Application of Negative Selection Algorithm in Multi-ang...

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38th International Conference on Telecommunications and Signal Processing (TSP)

作者： Yu, Xi Fu, Dongmei Yang, Tao Riha, Kamil Univ Sci & Technol Beijing Sch Infommt & Engeering Xueyuan Rd Beijing 100083 Peoples R China Brno Univ Technol Dept Telecommun Tech 12 Brno 61600 Czech Republic

ISBN: (纸本)9781479984985

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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