Proceedings of the 3rd International Conference on Software Engineering and Information Management最新文献_第5页

Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs 深度学习模型在胸部x线片黑肺检测中的性能比较

Proceedings of the 3rd International Conference on Software Engineering and Information Management Pub Date : 2020-01-12 DOI: 10.1145/3378936.3378968

Liton Devnath, S. Luo, P. Summons, Dadong Wang

{"title":"Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs","authors":"Liton Devnath, S. Luo, P. Summons, Dadong Wang","doi":"10.1145/3378936.3378968","DOIUrl":"https://doi.org/10.1145/3378936.3378968","url":null,"abstract":"Black Lung (BL) is an incurable respiratory disease caused by long term inhalation of respirable coal dust. Confidentiality restrictions and disease incidence limit the availability of BL datasets, which presents significant challenges in the training of deep learning (DL) models. This paper presents the implementations and detailed performance comparison of seven DL models for BL detection with small datasets. The models include VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet121 and CheXNet. A small BL dataset of real and synthetic images was used to train the seven deep learning models. Segmented lung X-ray images, with and without BL, were used as training images to establish a benchmark. To increase the number of images required for training a deep learning system the training data set was augmented, using a Cycle-Consistent Adversarial Networks (CycleGAN) and the Keras Image Data Generator, to generate additional augmented and synthetic radiographs. The effects of different dropout nodes as a blocking factor was also investigated on all seven models. The best sensitivity (Normal Prediction Rate), specificity (BL prediction Rate), error rate (ERR or incorrect prediction rate), accuracy (1-ERR), as well as total execution time for binary classification for each model, with and without augmentation, was compared for optimal BL detection. On average, the CheXNet model gave the best performance of all seven DL models.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115688992","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: 9

A Data Mining Approach for Student Referral Service of the Guidance Center: An Input in Designing Mediation Scheme for Higher Education Institutions of the Philippines 指导中心学生转介服务的数据挖掘方法:为菲律宾高等教育机构设计调解方案的输入

Proceedings of the 3rd International Conference on Software Engineering and Information Management Pub Date : 2020-01-12 DOI: 10.1145/3378936.3378958

J. A. Cabrera, Markdy Y. Orong, Nelpa N. Capio, Arnel Filarca, Eden A. Neri, Ariel R. Clarin

{"title":"A Data Mining Approach for Student Referral Service of the Guidance Center: An Input in Designing Mediation Scheme for Higher Education Institutions of the Philippines","authors":"J. A. Cabrera, Markdy Y. Orong, Nelpa N. Capio, Arnel Filarca, Eden A. Neri, Ariel R. Clarin","doi":"10.1145/3378936.3378958","DOIUrl":"https://doi.org/10.1145/3378936.3378958","url":null,"abstract":"The academic guidance office of an educational institution holds pertinent data of all the students in the institution such as psychological examination results, students' referral records and the like. Further, the office offered orientation services, testing services, counseling and follow-up services, individual inventory services, career guidance services, research & evaluation services and placement services. In this paper, a data mining approach was used to produce a trend analysis through time series and forecasted data using the Autoregressive Integrated Moving Average (ARIMA) of the student referral details from one of the Higher Education Institutions in the Philippines. Student referral historical data from the second semester of school year 2016- 2017, first semester of school year 2017-2018, second semester of school year 2017-2018 and the first semester of school year 2018- 2019 was used in the study. Results showed that absenteeism, poor attendance and poor academic performance were the highest number of recorded students' referrals over the others in which poor attendance yields a decreasing pattern among the three. On the other hand, based on the forecasted data, only poor academic performance and poor attendance showed a slight increasing patterns among others. These further signify that a proper program should be in place by the school counselors in mitigating the occurrence of referrals especially on the reasons showing an increase of prediction data.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132045599","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

Application of Cloud Computing for Big Data in the X-Ray Crystallography Community 大数据云计算在x射线晶体学界的应用

Proceedings of the 3rd International Conference on Software Engineering and Information Management Pub Date : 2020-01-12 DOI: 10.1145/3378936.3378950

A. Tosson, M. Shokr, U. Pietsch

引用次数: 0

Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models 基于图论、机器学习和深度学习模型的股票市场预测

Proceedings of the 3rd International Conference on Software Engineering and Information Management Pub Date : 2020-01-12 DOI: 10.1145/3378936.3378972

Pratik Patil, C. Wu, Katerina Potika, Marjan Orang

{"title":"Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models","authors":"Pratik Patil, C. Wu, Katerina Potika, Marjan Orang","doi":"10.1145/3378936.3378972","DOIUrl":"https://doi.org/10.1145/3378936.3378972","url":null,"abstract":"Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this paper, a novel approach is proposed using graph theory. This approach leverages Spatio-temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123189882","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: 24

Tamper Resistance Evaluation of TWINE Implemented on 8-bit Microcontroller 在8位微控制器上实现TWINE的抗篡改性能评估

Proceedings of the 3rd International Conference on Software Engineering and Information Management Pub Date : 2020-01-12 DOI: 10.1145/3378936.3378980

Y. Nozaki, M. Yoshikawa

引用次数: 1