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4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Geng, Yixin Sui, Jie Zhu, Qian Univ Chinese Acad Sci Sch Engn Sci Beijing Peoples R China

ISBN: (纸本)9781728114101

Internet provides great convenience for our life and becomes an important channel to get information. However, a large amount of false information, called rumors, come with it. In terms of automatically detecting rumors, two main contributions of this paper are as follows: (1) To reduce the impact of unbalanced data on classification, we proposed an improvement SMOTE algorithm to resample data. (2) We proposed six new features based on Sina microblogs, including Words with Guidance (WG), Words with Menace (WM), Suspected Topic (ST), Recognition of Information (RI), Degree of Attention to Users (DAU) and Credit Rating (CR), which are related to user-based features, content-based features, propagation-based features and microblog-based features. By building subsets with new features and using machine learning algorithms including Xgboost etc. We tested the effect of rumor detection on a real data set. Experiments showed that our rumor detection method was significantly improved compared with the most advanced method of the same type, with precision, recall and F1 at 0.827, 0.837 and 0.825 respectively, and AUC at 0.895.

关键词： rumor detection SDSMOTE feature selection social network microblog machine learning classification algorithm

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Hyperspectral Identification and classification of Oilseed Rape Waterlogging Stress Levels Using Parallel Computing

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IEEE ACCESS 2018年 6卷 57663-57675页

作者： Xia, Ji'an Yang, Yuwang Cao, Hongxin Zhang, Weixin Xu, Lei Wan, Qian Ke, Yaqi Zhang, Wenyu Ge, Daokuo Huang, Bo Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Jiangsu Acad Agr Sci Inst Agr Informat Nanjing 210014 Jiangsu Peoples R China

This paper aims to evaluate the feasibility and performance of two related applications in agricultural big data: hyperspectral imaging and parallel computing. After selecting two oilseed rape varieties (NingYou 22 and NingZa 19) as the objects of study, we captured hyperspectral images of these siliques following their exposure to three different waterlogging stress levels (0, 3, and 6 days). The machine learning library for Spark was used to realize both artificial neural network (ANN) and support vector machine (SVM) classification algorithms, and to conduct hyperspectral classification analysis of the oilseed rape siliques under the various levels of waterlogging stress on the parallel computing platform. From the classification data sets, 70% of the data were randomly selected for training, and the remaining 30% were used for prediction. The experimental results indicate that, when the hyperspectral image of a region of interest (400-1000 nm) was extracted and combined with the spectrum image data, the oilseed rape waterlogging detection model based on the Spark parallel computing framework was feasible and efficient. For the multi-class classification problem, the accuracy of the ANN algorithm was superior to that of the SVM, but its convergence time exceeded that of the SVM algorithm. Using the ANN and SVM algorithms for binary classification of the samples from both varieties, the results indicate that the performance of the SVM algorithm was superior in terms of the binary classification problem. Meanwhile, of the two oilseed rape varieties, the NZ 19 waterlogging samples yielded better classification results. Five optimal wavebands (512, 621, 689, 953, and 961 nm) were selected as the inputs for the classification algorithm. The results show that the classification accuracy of full-waveband hyperspectral imaging was slightly higher than that of optimal waveband imaging, while the ANN algorithm was more accurate than the SVM algorithm. Finally, the three in

关键词： Rapeseed silique hyperspectral image parallel computing machine learning spark classification algorithm

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Improved Framework for Breast Cancer Prediction Using Frequent Itemsets Mining for Attributes Filtering

Improved Framework for Breast Cancer Prediction Using Freque...

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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sinha, Ankita Sahoo, Bhaswati Rautaray, Siddharth Swamp Pandey, Manjusha KIIT Deemed Univ Sch Comp Engn Bhubaneswar Odisha India

ISBN: (纸本)9781538681138

Data Mining is applicable for pulling up some new information by analyzing the database. It is also used for prediction based on real and actual current data. Breast cancer is a very harmful disease which effects badly to ones social, physical life and also effects mentally. This paper focuses on the attribute filtering techniques i.e frequent itemsets mining with the intention to find the essential and relevant attribute from the Wisconsin breast cancer dataset and classification algorithmic program like SVM, Naive Bayes, k-NN, Decision Tree comparison is done with attribute filtering. SVM produces beat result among all the classifier with attribute filtering.

关键词： Data mining Breast cancer Z-score normalization Frequent itemsets mining classification algorithm R analytical tool Prediction

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Handwriting Analysis to Support Alzheimer's Disease Diagnosis: A Preliminary Study 18th

Handwriting Analysis to Support Alzheimer's Disease Diagnosi...

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18th international Conference on Computer Analysis of Images and Patterns (CAIP)

作者： Cilia, Nicole Dalia De Stefano, Claudio Fontanella, Francesco Molinara, Mario Di Freca, Alessandra Scotto Univ Cassino & Southern Lazio Dept Elect & Informat Engn DIEI Via G Biasio 43 I-03043 Cassino FR Italy

ISBN: (纸本)9783030298913;9783030298906

Alzheimer's disease (AD) is the most common neurodegenerative dementia of old age and the leading chronic disease contributor to disability and dependence among older people worldwide. Handwriting is among the motor activities compromised by AD, which is the result of a complex network of cognitive, kinaesthetic and perceptive-motor skills. Indeed, researchers have shown that the patients affected by these diseases exhibit alterations in the spatial organization and poor control of movement. In this paper, we present the preliminary results of a study in which an experimental protocol (including the copy of words, letters and sentence task) has been used to assess the kinematic properties of the movements involved in the handwriting. The obtained results are very encouraging and seem to confirm the hypothesis that machine learning-based analysis of handwriting can be profitably used to support AD diagnosis.

关键词： Handwriting classification algorithm Alzheimer's disease

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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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Identifying Potential Experts on Stack Overflow 13th

Identifying Potential Experts on Stack Overflow

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13th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Ban, Zihan Yan, Jiafei Sun, Hailong Beihang Univ Sch Comp Sci & Engn SKLSDE Lab Beijing 100191 Peoples R China Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China

ISBN: (纸本)9789811330445;9789811330438

Question answering community is an online service of user-generated content, where users seek help by posting questions and help others by offering answers. In question answering community, most of high quality answers are posted by some users called experts. The early identification of experts is of great significance to the success of community, based on which we can take measures to avoid the loss of expert users and encourage them to make more contributions. Different from the related works, we put forward an efficient method of supervised learning to identify potential topical experts in question answering community. Above all, we define and quantify the concepts of expert. Then on a specific topic, we extract the user features from three dimensions, including text-feature, behavior-feature and time-feature. Finally, we use the classification algorithms in machine learning to identify whether a user is the potential expert on current topic. Based on the data of Stack Overflow, we carry out a lot of experiments and implement a potential experts identification system. The results demonstrate the excellent effectiveness of our method based on artificial neural network model. Besides, we find that expert users are inclined to interact with other expert users, providing new ideas for future research on this subject.

关键词： Question answering community Potential experts identifying Feature extraction classification algorithm

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Predicting Customer Behavior with Combination of Structured and Unstructured Data 3

Predicting Customer Behavior with Combination of Structured ...

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3rd International Conference on Science and Sustainable Development (ICSSD)

作者： Afolabi, Ibukun T. Worlu, Rowland E. Adebayo, O. P. Jonathan, Oluranti

Presently, there are numerous e-marketing and m-marketing mediums that exist such as YouTube, SMS, What Sapp, Google, twitter, yahoo, Facebook, LinkedIn, email and personal blogs. These mediums are beginning to be used for marketing purposes, particularly by the SMEs in Nigeria. The aim of this research is to address the problem of deciding which of the mediums mentioned above is mostly appropriate to target customer of a particular SME and also to discover the type of data that is most appropriate for analysis in making this decision. In order to achieve this, data was gathered by administering questionnaires and pre-processed based on structured and unstructured data sources. The J48 decision tree classification algorithm was used to mine the data, relevant predictions were made from the structured and unstructured data and the results were evaluated. The results revealed that predicting from unstructured data expresses more of popular opinion, so decision can start from unstructured results and be fined tuned or validated with predicting from structured data. Though structured prediction appears to be better than unstructured, unstructured prediction is still very valuable in situations where there are no structured data such as analysing text messages. Also, Models developed for predicting customer behaviour as regards the marketing channels studied, will form the foundation for marketing decision making, in small and medium businesses in Nigeria.

关键词： Data Mining classification algorithm Marketing e-marketing m-marketing Structured data Unstructured data

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Machine Learning-Assisted Detection for BPSK-modulated Ambient Backscatter Communication Systems

Machine Learning-Assisted Detection for BPSK-modulated Ambie...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Wang, Xiyu Duan, Ruifeng Yigitler, Huseyin Menta, Estifanos Jantti, Riku Aalto Univ Dept Commun & Networking Espoo 02150 Finland

ISBN: (纸本)9781728109626

Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed approach.

关键词： Ambient backscatter classification algorithm green communication machine learning signal detection

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classification of Strongly Conflict-Avoiding Codes

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IEEE COMMUNICATIONS LETTERS 2018年第12期22卷 2415-2418页

作者： Baicheva, Tsonka Stefanova Topalova, Svetlana Todorova Bulgarian Acad Sci Inst Math & Informat Veliko Tarnovo 5000 Bulgaria DA Tsenov Acad Econ Svishtov 5250 Bulgaria

Strongly conflict-avoiding codes (SCACs) for slot-asynchronous multiple-access collision channels without feedback are considered. Codes with maximum size (optimal codes) for given parameters are of interest. In this letter, we present classification results about optimal SCACs of weights 3, 4, and 5 and given small lengths. We also determine some previously unknown values of the size of optimal SCACs with these parameters.

关键词： Asynchronous communication strongly conflict-avoiding codes classification algorithm

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Comparison of classification and supervised learning algorithms in assessing the hydraulic conditions of sewer collection systems: A case study of local sewer networks in Jinju City, Korea

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DESALINATION AND WATER TREATMENT 2018年 124卷 202-210页

作者： Ki, Seo Jin Lee, Chun-Sik Jung, Won-Hee Park, Hyun-Geoun Gyeongnam Natl Univ Sci & Technol Dept Environm Engn 33 Dongjin Ro Jinju Si 52725 Gyeongsangnam D South Korea

Accurate screening of sewer conditions from monitoring data contributes to maintaining their operations (in terms of water quality and quantity) safe as well as reducing their associated costs (for operation and maintenance). This study was designed to assess the performance deterioration in sewer systems using a series of data classification tools, namely classical classification and novel supervised learning algorithms. The hydraulic data available for four sewer systems at Jinju City in Korea in a daily format during the monitoring period of 2013-2017 were provided as example data sets to those algorithms, which were evaluated independently with 70% training and 30% test data sets randomly divided. A self-organizing map (SOM) with a specialty in extracting hidden patterns in data was used to classify the data sets into three warning levels in the absence of any definite warning criteria for individual parameters. Our findings showed that three supervised learning algorithms achieved comparable performance in predicting warning levels defined from SOM to exiting classification algorithm in terms of accuracy and error rate. The network architecture optimized for supervised learning algorithms, in fact, varied significantly depending on the data sets, including that with additional variables on top of the original data set. In contrast, exiting classification algorithm unexpectedly produced high error rates in case that the hydraulic parameters had low coefficient of variation values reaching as high as 16%. Overall, these results demonstrated that novel supervised learning algorithms were more universally applicable for the assessment of hydraulic and/or water quality conditions in sewer systems than classical classification algorithm, regardless of the amount of variability in the data sets.

关键词： Supervised learning algorithms classification algorithm Self-organizing map Hydraulic parameters Water quality Sewer systems

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