International Conference on Machine Learning and Computing最新文献_第4页

A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning 基于特征的机器学习漏洞预测特征选择研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318345

Zhanjun Li, Yan Shao

引用次数: 9

Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems 求解无人机路径规划问题的混沌杜鹃搜索算法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318310

Jeng-Shyang Pan, Jenn-Long Liu, Shou-Cheng Hsiung

引用次数: 17

Quantifying Customer Review by Integrating Multiple Source of Knowledge 通过整合多个知识来源来量化客户评论

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318309

Yuanchao Liu, Xinping Li, Mingjiang Wang

引用次数: 0

Feature Based Data Anonymization with Slicing Method for Data Publishing 基于特征的数据匿名化与切片数据发布方法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318389

Esther Gachanga, Michael W. Kimwele, L. Nderu

引用次数: 3

Recommendation Algorithm for Mobile E-commerce Based on Reputation 基于声誉的移动电子商务推荐算法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318390

Yuan Chai, Dong Li, Yuchen Wu

{"title":"Recommendation Algorithm for Mobile E-commerce Based on Reputation","authors":"Yuan Chai, Dong Li, Yuchen Wu","doi":"10.1145/3318299.3318390","DOIUrl":"https://doi.org/10.1145/3318299.3318390","url":null,"abstract":"In the new technical background, more and more goods appear in front of the user. Unfortunately, users are increasingly easy to get lost in the massive commodity information. In order to improve the users experience of mobile e-commerce, combined with the advanced technology of large data age, there have been many recommendation algorithms for mobile e-commerce. All of this recommendation algorithms based on customer interests, sales and other different considerations, in order to achieve a purposeful and efficient screening recommendation information. In many ways of thinking, the user reputation as a new era of the spirit of the contract reflects the important value. In this paper, we focus on the current technical situation, designing a set of scoring algorithm through the analysis of the previous behavioral data modeling. After this algorithm, from different customer categories of different needs to start using the mainstream for the score of the business recommendation algorithm algorithm to achieve. And the results are compared with the expected analysis in order to obtain the current algorithm in the credibility of the algorithm under the improvement and adjustment. Finally, we will design a set of recommendation algorithm based on the reputation standards.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132581088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Novel Hyperspectral Based Dorsal Hand Recognition System 一种新的基于高光谱的手背识别系统

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318316

Wei Nie, Bob Zhang, Shuping Zhao

{"title":"A Novel Hyperspectral Based Dorsal Hand Recognition System","authors":"Wei Nie, Bob Zhang, Shuping Zhao","doi":"10.1145/3318299.3318316","DOIUrl":"https://doi.org/10.1145/3318299.3318316","url":null,"abstract":"In biometrics, dorsal hand recognition systems are secure, user-friendly, and non-intrusive. Currently, the state-of-the-art in this area uses a single near-infrared spectral band such as 850nm or far-infrared thermography with a range of 8-14μm. This would limit the extraction of only hand veins from a dorsal hand image. However, the dorsal hand contains much more features such as the texture of the skin, pigmented moles, and various chromophores. Therefore, using one spectral band cannot extract all possible dorsal hand features. To resolve this issue, in this paper we propose a novel hyperspectral based dorsal hand recognition system. First, a novel hyperspectral acquisition device is designed to establish a hyperspectral dorsal hand database consisting of 53 spectra. Next, a region of interest was extracted from all spectral dorsal hand images. Then, the partitioned local binary pattern was applied for feature representation extracting both dorsal hand texture and vein features. Finally, the nearest neighborhood classifier was utilized to perform recognition. To validate the proposed system, extensive experiments were conducted on all spectral bands (individually) and the combination of wavelengths of visible light and NIR for both identification and verification using 120 individuals. The highest accuracy for identification (0.998) was achieved using 600nm and 860nm, while for verification the same spectral combo produced the lowest EER of 0.049.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134440156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Blood Glucose Level Prediction with Minimal Inputs Using Feedforward Neural Network for Diabetic Type 1 Patients 用前馈神经网络最小输入预测1型糖尿病患者血糖水平

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318354

Muhammad Asad, Usman Qamar, Babar Zeb, Aimal Khan, Younas Khan

{"title":"Blood Glucose Level Prediction with Minimal Inputs Using Feedforward Neural Network for Diabetic Type 1 Patients","authors":"Muhammad Asad, Usman Qamar, Babar Zeb, Aimal Khan, Younas Khan","doi":"10.1145/3318299.3318354","DOIUrl":"https://doi.org/10.1145/3318299.3318354","url":null,"abstract":"Introduction: Diabetes mellitus is one of the rapidly increasing diseases throughout the world. Studies reveal that proper management of blood glucose levels can reduce the complications associated with diabetes type 1. Objective: We use only continuous blood glucose data and predicted future blood glucose level using the previous data. Method: In this research, we input Continuous Glucose Monitoring (CGM) data to train a feedforward neural network using window model, to get optimal neural network for each subject in predicting prior blood glucose values. We have investigated virtual CGM data of 10 subjects in order to depict the efficiency of the proposed method and to validate the ANN. These ten case studies have been compiled from AIDA i.e. the freeware mathematical diabetes simulator. Results: For BGL predictions, improved results have been shown for minimal inputs in the prediction horizon (PH) of 15 minutes. Results produced by experimentation reveal that our ANN is accurate, adaptive, and can be implemented in clinics. Moreover, this study targets to make life easier for T1D patients by minimizing human input to the system. Conclusion and Future work: We conclude that feedforward gives better results for minimal inputs while other methods have better results for multiple inputs. In the future, we intend to investigate a larger collection of AIDA scenarios, and for real patients.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

File Matching Based on Secure Authentication and Proxy Homomorphic Re-encryption 基于安全认证和代理同态重加密的文件匹配

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318392

Bin Yu, Chen Zhang, Weihong Li

引用次数: 4

A Study on the Performance Evaluation of Machine Learning Models for Phoneme Classification 音素分类机器学习模型的性能评价研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318385

Ali Shariq Imran, Abdolreza Sabzi Shahrebabaki, Negar Olfati, T. Svendsen

{"title":"A Study on the Performance Evaluation of Machine Learning Models for Phoneme Classification","authors":"Ali Shariq Imran, Abdolreza Sabzi Shahrebabaki, Negar Olfati, T. Svendsen","doi":"10.1145/3318299.3318385","DOIUrl":"https://doi.org/10.1145/3318299.3318385","url":null,"abstract":"This paper provides a comparative performance analysis of both shallow and deep machine learning classifiers for speech recognition task using frame-level phoneme classification. Phoneme recognition is still a fundamental and equally crucial initial step toward automatic speech recognition (ASR) systems. Often conventional classifiers perform exceptionally well on domain-specific ASR systems having a limited set of vocabulary and training data in contrast to deep learning approaches. It is thus imperative to evaluate performance of a system using deep artificial networks in terms of correctly recognizing atomic speech units, i.e., phonemes in this case with conventional state-of-the-art machine learning classifiers. Two deep learning models - DNN and LSTM with multiple configuration architectures by varying the number of layers and the number of neurons in each layer on the OLLO speech corpora along with six shallow machine learning classifiers for Filterbank acoustic features are thoroughly studied.\u0000 Additionally, features with three and ten frames temporal context are computed and compared with no-context features for different models. The classifier's performance is evaluated in terms of precision, recall, and F1 score for 14 consonants and 10 vowels classes for 10 speakers with 4 different dialects. High classification accuracy of 93% and 95% F1 score is obtained with DNN and LSTM networks respectively on context-dependent features for 3-hidden layers containing 1024 nodes each. SVM surprisingly obtained even a higher classification score of 96.13% and a misclassification error of less than 5% for consonants and 4% for vowels.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130182089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Research of Integrated Reorganization of Mineral Rock and Fossil Specimen Data Based on ETL Technology 基于ETL技术的矿物岩石与化石标本数据整合重组研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318393

Zhiyuan Wu, Yang Mei, Yinqian Cheng

引用次数: 0