Proceedings of the 3rd International Conference on Machine Learning and Soft Computing最新文献

Grammar Inference Based on Passive Learning and Genetic Algorithm 基于被动学习和遗传算法的语法推理

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311033

P. Grachev

引用次数: 0

21st Century Learning Skills Predictive Model Using PART Algorithm 基于PART算法的21世纪学习技能预测模型

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3310992

Betchie E. Aguinaldo

{"title":"21st Century Learning Skills Predictive Model Using PART Algorithm","authors":"Betchie E. Aguinaldo","doi":"10.1145/3310986.3310992","DOIUrl":"https://doi.org/10.1145/3310986.3310992","url":null,"abstract":"The skillset of a graduate is the key to success to perform successfully in their chosen career path, thus, academic institution continuously develops strategic ways to develop and prepare the 21st century Learning skills of the student before they completed the higher education studies. This paper presents the 21st Century Learning Predictive Model in Programming Logic Formulation using PART classifier algorithm. It also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for 21st Century Learning Skills using PART classifier algorithm. Six (6) standardized questionnaire of 21st Century Learning Skills of one hundred eight (180) students were coded and used as a dataset of the study. The result of the customized assessment exam in programming logic formulation was used as response attributes of the datasets. As a result of the study, five (5) rules were generated using PART classifier algorithm. Among the 21st Century Learning skills, Communication is the strongest predicting attributes of successful performance of students in programming logic formulation.","PeriodicalId":252781,"journal":{"name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129584627","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

An Improvement of Fuzzy Logic Based Clustering Combined for Mobile Sink Algorithm 一种基于模糊逻辑的聚类结合移动汇聚算法的改进

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311018

Phan Thi The, N. Thang, Tran Cong Hung

引用次数: 0

TRRS: Temporal Recurrent Recommender System based on Time-sync Comments TRRS:基于时间同步评论的时间循环推荐系统

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311022

Hao Ren, Dong Wang

引用次数: 1

Predicting Absenteeism at Work Using Tree-Based Learners 使用基于树的学习者预测工作缺勤

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3310994

Zaman Wahid, A. Z. Satter, Abdullah Al Imran, T. Bhuiyan

{"title":"Predicting Absenteeism at Work Using Tree-Based Learners","authors":"Zaman Wahid, A. Z. Satter, Abdullah Al Imran, T. Bhuiyan","doi":"10.1145/3310986.3310994","DOIUrl":"https://doi.org/10.1145/3310986.3310994","url":null,"abstract":"Absenteeism at workplace acts as a crucial role in demonstrating the productive and profitable capacity of a company. Thus the knowledge of absenteeism of employees' becomes the foundation for an organization in its multiple dimensions. Because the proper determination of employees' profile allows the identification of excesses of occurrences of certain morbidities. The early absenteeism research primarily focused on predicting the characteristics and the categories of diseases of employees that make them perform higher absenteeism at workplace. However, predicting the absenteeism time of employees using different machine learning classifiers is able to give the researches a new dimension in line with the intention of revealing the underlying causes and patterns of absenteeism. In this paper, we have applied 4 prominent machine learning algorithms namely Decision Tree, Gradient Boosted Tree, Random Forest, and Tree Ensemble on the absenteeism dataset of a courier company in Brazil in order to predict the absenteeism time of employees at work as well as the best classifier. Based on the 7 evaluation metrics such as True Positive, True Negative, False Positive, False Negative, Sensitivity, Specificity, and Accuracy we found that Gradient Boosted Tree produced the best result with an accuracy rate of 82% whereas Tree Ensemble performed the lowest with the accuracy rate of 79%.","PeriodicalId":252781,"journal":{"name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116880708","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: 15

The Role of Attention Mechanism and Multi-Feature in Image Captioning 注意机制和多特征在图像字幕中的作用

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311002

Tien Dang, A. Oh, In Seop Na, Soohyung Kim

引用次数: 4

Diagnosis of Common Diseases Using Type-2 Fuzzy System 二类模糊系统在常见病诊断中的应用

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311028

B. Erin, R. Abiyev

引用次数: 2

Implementation of Enhanced MD5 Algorithm using SSL to Ensure Data Integrity 利用SSL实现增强型MD5算法，保证数据完整性

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311027

Larry B. de Guzman, Ariel M. Sison, Ruji P. Medina

引用次数: 6

Towards a transfer learning approach to food recommendations through food images 通过食物图像进行食物推荐的迁移学习方法

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3310990

Hafiz Muhammad Zubair Hasan, Hammad Khan, Talha Asif, S. Hashmi, Muhammad Rafi

{"title":"Towards a transfer learning approach to food recommendations through food images","authors":"Hafiz Muhammad Zubair Hasan, Hammad Khan, Talha Asif, S. Hashmi, Muhammad Rafi","doi":"10.1145/3310986.3310990","DOIUrl":"https://doi.org/10.1145/3310986.3310990","url":null,"abstract":"User generated text/multimedia content are increasingly shared in online businesses systems and their effective use in user modelling and recommendation strategies is consequently growing too. In restaurant businesses the food menu along with images are a common practice and users also shared the food images they ordered and feel good about. Yelp data set challenge in their round 9 and onward introduced such rich images data for their competition. In this paper, we motivated from this rich images data of food for semantically incorporating image-specific features to the star-rating and recommendation process. We first applied a transfer learning approach with pre-trained CNNs (Convolution Neural Networks) which were used to label the Yelps food images of the restaurants using the Food101 data-set. We defined star-rating for restaurants by capturing a correlation between restaurant images and users shared images. Our proposed strategy works on discovering hidden aspects of food images and labels to be used in recommendation strategy. We performed an extensive set of experiments by creating a baseline using standard rating provided in the Yelp data-set. The proposed approach produced better Root Mean Square Error (RMSE), which is a clear indication of high-quality recommendation strategy","PeriodicalId":252781,"journal":{"name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123069402","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: 4

An improved prediction method for diabetes based on a feature-based least angle regression algorithm 一种改进的基于特征最小角回归的糖尿病预测方法

Proceedings of the 3rd International Conference on Machine Learning and Soft Computing Pub Date : 2019-01-25 DOI: 10.1145/3310986.3311024

Shaoming Qiu, Jiahao Li, Bo Chen, Ping Wang, Xiu-e Gao

{"title":"An improved prediction method for diabetes based on a feature-based least angle regression algorithm","authors":"Shaoming Qiu, Jiahao Li, Bo Chen, Ping Wang, Xiu-e Gao","doi":"10.1145/3310986.3311024","DOIUrl":"https://doi.org/10.1145/3310986.3311024","url":null,"abstract":"Existing diabetes prediction algorithms have a number of shortcomings, most notably low accuracy and poor generalizability. In this paper, we propose a method based on feature weights to improve diabetes prediction that combines the advantages of traditional least angle regression (LARS) algorithms and principal component analysis (PCA) algorithms.First of all, a principal component analysis algorithm is used to obtain the characteristic independent variables found in typical diabetes prediction regression models. Each of these variables is assigned its own characteristics. After this, the original variable correlation is multiplied by the weight of the variable obtained using principal component analysis to obtain a new degree of correlation. This new correlation is used to optimize the forward direction and variable selection of a least angle regression solution before calculating the regression coefficients for the new model. An experiment using the Pima Indians Diabetes dataset provided by the University of California was performed to validate the proposed algorithm. The experimental results show that the algorithm improved the approximation speed for the dependent variables and the accuracy of the regression coefficients. It was also able to select the key characteristic variables for diabetes prediction whilst simplifying the standard diabetes prediction model. Thus, it may help with the provision of more accurate diabetes prevention and treatment measures in the future.","PeriodicalId":252781,"journal":{"name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427540","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: 1