EAI Endorsed Transactions on Scalable Information Systems最新文献_第6页

Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC 开发一种用于代码片段分类和堆栈溢出问题的超参数优化方法:HyperSCC

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-05-27 DOI: 10.4108/eai.27-5-2022.174084

M. Öztürk

{"title":"Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC","authors":"M. Öztürk","doi":"10.4108/eai.27-5-2022.174084","DOIUrl":"https://doi.org/10.4108/eai.27-5-2022.174084","url":null,"abstract":"Although there exist various machine learning and text mining techniques to identify the programming language of complete code files, multi-label code snippet prediction was not considered by the research community. This work aims at devising a tuner for multi-label programming language prediction of stack overflow posts. To that end, a Hyper Source Code Classifier (HyperSCC) is devised along with rule-based automatic labeling by considering the bottlenecks of multi-label classification. The proposed method is evaluated on seven multi-label predictors to conduct an extensive analysis. The method is further compared with the three competitive alternatives in terms of one-label programming language prediction. HyperSCC outperformed the other methods in terms of the F1 score. Preprocessing results in a high reduction (50%) of training time when ensemble multi-label predictors are employed. In one-label programming language prediction, Gradient Boosting Machine (gbm) yields the highest accuracy (0.99) in predicting R posts that have a lot of distinctive words determining labels. The findings support the hypothesis that multi-label predictors can be strengthened with sophisticated feature selection and labeling approaches.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"2012 1","pages":"e5"},"PeriodicalIF":1.3,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86421451","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: 0

GAN Data Augmentation for Improved Automated Atherosclerosis Screening from Coronary CT Angiography 从冠状动脉CT血管造影中改进自动动脉粥样硬化筛查的GAN数据增强

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-05-17 DOI: 10.4108/eai.17-5-2022.173981

Amel Laidi, Mohammed Ammar, Mostafa EL HABIB DAHO, S. Mahmoudi

{"title":"GAN Data Augmentation for Improved Automated Atherosclerosis Screening from Coronary CT Angiography","authors":"Amel Laidi, Mohammed Ammar, Mostafa EL HABIB DAHO, S. Mahmoudi","doi":"10.4108/eai.17-5-2022.173981","DOIUrl":"https://doi.org/10.4108/eai.17-5-2022.173981","url":null,"abstract":"INTRODUCTION: Atherosclerosis is a chronic medical condition that can result in coronary artery disease, strokes, or even heart attacks. early detection can result in timely interventions and save lives. OBJECTIVES: In this work, a fully automatic transfer learning-based model was proposed for Atherosclerosis detection in coronary CT angiography (CCTA). The model’s performance was improved by generating training data using a Generative Adversarial Network. METHODS: A first experiment was established on the original dataset with a Resnet network, reaching 95.2% accuracy, 60.8% sensitivity, 99.25% specificity and 90.48% PPV. A Generative Adversarial Network (GAN) was then used to generate a new set of images to balance the dataset, creating more positive images. Experiments were made adding from 100 to 1000 images to the dataset. RESULTS: adding 1000 images resulted in a small drop in accuracy to 93.2%, but an improvement in overall performance with 89.0% sensitivity, 97.37% specificity and 97.13% PPV. CONCLUSION: This paper was one of the early research projects investigating the e ffi ciency of data augmentation using GANs for atherosclerosis, with results comparable to the state of the art. long as the original work is properly cited.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"22 1","pages":"e4"},"PeriodicalIF":1.3,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78729354","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: 0

Automatic Data Clustering using Dynamic Crow Search Algorithm 基于动态Crow搜索算法的自动数据聚类

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-05-17 DOI: 10.4108/eai.17-5-2022.173982

Rajesh Ranjan, J. Chhabra

引用次数: 1

Forecasting Diabetes Correlated Non-alcoholic Fatty Liver Disease by Exploiting Naïve Bayes Tree 利用Naïve贝叶斯树预测糖尿病相关非酒精性脂肪肝

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-29 DOI: 10.4108/eai.29-4-2022.173975

S. Reddy, Nilambar Sethi, R. Rajender, G. Mahesh

{"title":"Forecasting Diabetes Correlated Non-alcoholic Fatty Liver Disease by Exploiting Naïve Bayes Tree","authors":"S. Reddy, Nilambar Sethi, R. Rajender, G. Mahesh","doi":"10.4108/eai.29-4-2022.173975","DOIUrl":"https://doi.org/10.4108/eai.29-4-2022.173975","url":null,"abstract":"INTRODUCTION: In recent years, non-alcoholic fatty liver disease (NAFLD) has been identified as the most vulnerable chronic disease. Fat is accumulated in the liver cells of persons with NAFLD. Diabetes is the most common ailment among people of all ages, so it is critical to recognize and prevent its adverse effects. OBJECTIVES: A relevant dataset with appropriate features was selected. Ensemble algorithms were applied for the prediction task, and finally, the method with the best performance was extracted. METHODS: In addition to Ensemble approaches namely bagging, Random forest and Ada-boost, individual classifiers Naive Bayes (NB) and C4.5 Decision tree were considered. These ML techniques were compared with the proposed NB tree algorithm, a combination of C4.5 and Naive Bayes. RESULTS: The following evaluation parameters were computed for each analyzed algorithm: accuracy, detection rate, negative predictive value (NPV), false negative rate (FNR), and false positive rate (FPR). The algorithms are then compared based on these metrics to determine the best algorithm. The NB tree was obtained to be the best method with 97.55% accuracy, 0.4853 detection rate, 0.9615 NPV, 0.0388 FNR, and 0.0099 FPR. CONCLUSION: The NB tree outperformed individual Naive bayes and C4.5 classifiers, and the other techniques studied. The developed algorithm could be applied in NAFLD-related research. accuracy, detection rate, NPV, FNR and FPR, diabetes mellitus (DM).","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"34 1","pages":"e2"},"PeriodicalIF":1.3,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86123202","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

Learning to Detect Phishing Web Pages Using Lexical and String Complexity Analysis 学习检测网络钓鱼网页使用词法和字符串复杂性分析

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-20 DOI: 10.4108/eai.20-4-2022.173950

D. Patil, T. Pattewar, Shailendra M. Pardeshi, Vipul D. Punjabi, Rajnikant Wagh

引用次数: 1

Impact of Features Reduction on Machine Learning Based Intrusion Detection Systems 特征约简对机器学习入侵检测系统的影响

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-13 DOI: 10.4108/eetsis.vi.447

Masooma Fatima, O. Rehman, Ibrahim M. H. Rahman

{"title":"Impact of Features Reduction on Machine Learning Based Intrusion Detection Systems","authors":"Masooma Fatima, O. Rehman, Ibrahim M. H. Rahman","doi":"10.4108/eetsis.vi.447","DOIUrl":"https://doi.org/10.4108/eetsis.vi.447","url":null,"abstract":"INTRODUCTION: As the use of the internet is increasing rapidly, cyber-attacks over user’s personal data and network resources are on the rise. Due to the easily accessible cyber-attack tools, attacks on cyber resources are becoming common including Distributed Denial-of-Service (DDoS) attacks. Intruders are using enhanced techniques for executing DDoS attacks. OBJECTIVES: Machine Learning (ML) based classification modules integrated with Intrusion Detection System (IDS) has the potential to detect cyber-attacks. This research aims to study the performance of several machine learning algorithms, namely Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine in classifying DDoS attacks from normal traffic. METHODS: The paper focuses on DDoS attacks identification for which multiclass dataset is being used including Smurf, SIDDoS, HTTP-Flood and UDP-Flood. balanced datasets are used for both training and testing purposes in order to obtain biased free results. four experimental scenarios are conducted in which each experiment contains a different set of reduced features. RESULTS: Result of each experiment is computed individually and the best algorithm among the four is highlighted by mean of its accuracy, detection rates and processing time required to build and test the classifiers. CONCLUSION: Based on all experimental results, it is found that Decision Tree algorithm has shown promising cumulative performances in terms of the metrics investigated.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"23 1","pages":"e9"},"PeriodicalIF":1.3,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86989845","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: 0

RETRACTED: CenterNet-SPP based on multi-feature fusion for basketball posture recognition [EAI Endorsed Scal Inf Syst (2022), Online First] 撤稿:基于多特征融合的CenterNet-SPP篮球姿势识别[EAI背书尺度信息系统(2022)，Online First]

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173788

Zhouxiang Jin

引用次数: 0

RETRACTED: Encoder-decoder structure based on conditional random field for building extraction in remote sensing images [EAI Endorsed Scal Inf Syst (2022), Online First] 撤稿:基于条件随机场的编码器-解码器结构在遥感图像中建筑提取[EAI背书尺度信息系统(2022)，Online First]

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173801

Yian Xu

引用次数: 0

RETRACTED: Parallax information fusion-based for dance moving image posture extraction [EAI Endorsed Scal Inf Syst (2022), Online First] 基于视差信息融合的舞蹈运动图像姿态提取[EAI背书尺度信息系统(2022)，在线第一]

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173799

Yin Lyu, Lin Teng

引用次数: 0

RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First] 撤下:基于深度学习和可变形部分模型的舞蹈动作特征提取[EAI背书尺度信息系统(2022)，在线第一]

IF 1.3

EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173790

Shuang Gao, Xiaowei Wang

引用次数: 0