Advances in Data Science and Adaptive Analysis最新文献_第4页

A New Class of Cure Rate Survival Models: Properties, Inference and Applications 一类新的治愈率生存模型:性质、推论及应用

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Advances in Data Science and Adaptive Analysis Pub Date : 2021-03-27 DOI: 10.1142/S2424922X21500017

Gladys D. C. Barriga, A. K. Suzuki, V. Cancho, F. Louzada

引用次数: 0

Empirical Spatial Density-Based Emergency Medical Service Demand Forecast for Ambulance Allocation 基于空间密度的救护车配置急诊医疗服务需求预测

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Advances in Data Science and Adaptive Analysis Pub Date : 2021-02-17 DOI: 10.1142/S2424922X21500030

Y. Tsai, Hwei-Jen Lin, Pei Chi, Kelvin W. Lee

引用次数: 0

Editorial: Special Issue on New Frontiers in Data Sciences and Data Analytics Tools and Applications 社论:关于数据科学和数据分析工具与应用新领域的特刊

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-12-08 DOI: 10.1142/s2424922x20020015

Asadullah Shaikh, Khairan D. Rajab

引用次数: 0

Approximate Entropy and Empirical Mode Decomposition for Improved Speaker Recognition 改进说话人识别的近似熵和经验模态分解

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-12-02 DOI: 10.1142/s2424922x20500114

R. A. Metzger, J. Doherty, D. Jenkins, D. L. Hall

{"title":"Approximate Entropy and Empirical Mode Decomposition for Improved Speaker Recognition","authors":"R. A. Metzger, J. Doherty, D. Jenkins, D. L. Hall","doi":"10.1142/s2424922x20500114","DOIUrl":"https://doi.org/10.1142/s2424922x20500114","url":null,"abstract":"When processing real-world recordings of speech, it is highly probable noise will be present at some instance in the signal. Compounding this problem is the situation when the noise occurs in short, impulsive bursts at random intervals. Traditional signal processing methods used to detect speech rely on the spectral energy of the incoming signal to make a determination whether or not a segment of the signal contains speech. However when noise is present, this simple energy detection is prone to falsely flagging noise as speech. This paper will demonstrate an alternative way of processing a noisy speech signal utilizing a combination of information theoretic and signal processing principles to differentiate speech segments from noise. The utilization of this preprocessing technique will allow a speaker recognition system to train statistical speaker model using noise-corrupted speech files, and construct models statistically similar to those constructed from noise-free data. This preprocessing method will be shown to outperform traditional spectrum-based methods for both low-entropy and high-entropy noise in low signal-to-noise ratio environments, with a reduction in the feature space distortion when measured using the Cauchy–Schwarz (CS) distance metric.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"20 1","pages":"2050011:1-2050011:24"},"PeriodicalIF":0.6,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77969513","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

On Pricing American Put Option on a Fixed Term: A Monte Carlo Approach 固定期限美式看跌期权定价:蒙特卡洛方法

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-30 DOI: 10.1142/s2424922x20500102

Perpetual Andam Boiquaye

引用次数: 0

Features Reweighting and Selection in ligand-based Virtual Screening for Molecular Similarity Searching Based on Deep Belief Networks 基于深度信念网络的分子相似性搜索配体虚拟筛选中的特征重加权与选择

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-30 DOI: 10.1142/s2424922x20500096

Maged Nasser, N. Salim, Hamza Hentabli, Faisal Saeed, I. Rabiu

{"title":"Features Reweighting and Selection in ligand-based Virtual Screening for Molecular Similarity Searching Based on Deep Belief Networks","authors":"Maged Nasser, N. Salim, Hamza Hentabli, Faisal Saeed, I. Rabiu","doi":"10.1142/s2424922x20500096","DOIUrl":"https://doi.org/10.1142/s2424922x20500096","url":null,"abstract":"Virtual screening (VS) is defined as the use of a compilation of computational procedures to grade, score and/or sort several chemical formations. The purpose of VS is to identify the molecules holding the greatest prior probabilities of activity. Many of the conventional similarity methods assume that molecular features that do not relate to the biological activity carry the same weight as the important ones. For this reason, the researchers on this paper investigated that some features are being more important than others through the chemist structure diagrams and the weight for each fragment should be taken into consideration by giving more weight to those fragments that are more important. In this paper, a deep learning method specifically known as Deep Belief Networks (DBN) has been used to reweight the molecule features and based on this new weigh, the reconstruction feature error has been calculated for all the features. Based on the reconstruction feature error values, Principal Component Analysis (PCA) has been used for the dimension’s reduction and only few hundreds of features have been selected based on the less error rate. The main aim of this research is to show an improvement of the similarity searching performance based on the selected features those have less error rate. The results derived through the DBN were compared with those derived through other similarity methods, such as the Tanimoto coefficient and the quantum-based methods. This comparison revealed the performance of the DBN with the structurally heterogeneous data sets (DS1 and DS3) to be superior to the performances of all the other techniques.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"26 1","pages":"2050009:1-2050009:28"},"PeriodicalIF":0.6,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78130310","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

Towards a Knowledge-Driven Framework of Cyber-Physical Social System for Multidimensional Urban Mobility 面向多维城市交通的信息物理社会系统知识驱动框架

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-26 DOI: 10.1142/s2424922x20410053

F. Abbasi, Mohamed-Hedi Karray, R. Houé, M. Memon, B. Archimède

引用次数: 0

Smartphone-Based Pavement Roughness Estimation Using Deep Learning with Entity Embedding 基于实体嵌入深度学习的智能手机路面粗糙度估计

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-26 DOI: 10.1142/s2424922x20500072

Armstrong Aboah, Y. Adu-Gyamfi

引用次数: 19

Roman Urdu Headline News Text Classification Using RNN, LSTM and CNN 罗马乌尔都语标题新闻文本分类使用RNN, LSTM和CNN

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-18 DOI: 10.1142/s2424922x20500084

Irfan Ali Kandhro, Sahar Zafar Jumani, K. Kumar, Abdul Hafeez, F. Ali

{"title":"Roman Urdu Headline News Text Classification Using RNN, LSTM and CNN","authors":"Irfan Ali Kandhro, Sahar Zafar Jumani, K. Kumar, Abdul Hafeez, F. Ali","doi":"10.1142/s2424922x20500084","DOIUrl":"https://doi.org/10.1142/s2424922x20500084","url":null,"abstract":"This paper presents the automated tool for the classification of text with respect to predefined categories. It has always been considered as a vital method to manage and process a huge number of documents in digital forms which are widespread and continuously increasing. Most of the research work in text classification has been done in Urdu, English and other languages. But limited research work has been carried out on roman data. Technically, the process of the text classification follows two steps: the first step consists of choosing the main features from all the available features of the text documents with the usage of feature extraction techniques. The second step applies classification algorithms on those chosen features. The data set is collected through scraping tools from the most popular news websites Awaji Awaze and Daily Jhoongar. Furthermore, the data set splits in training and testing 70%, 30%, respectively. In this paper, the deep learning models, such as RNN, LSTM, and CNN, are used for classification of roman Urdu headline news. The testing accuracy of RNN (81%), LSTM (82%), and CNN (79%), and the experimental results demonstrate that the performance of the LSTM method is state-of-art method compared to CNN and RNN.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"87 1","pages":"2050008:1-2050008:13"},"PeriodicalIF":0.6,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80844137","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

Continuous Empirical Wavelets Systems 连续经验小波系统

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Advances in Data Science and Adaptive Analysis Pub Date : 2020-11-17 DOI: 10.1142/S2424922X20500060

J. Gilles

引用次数: 2