Pub Date : 2021-03-27DOI: 10.1142/S2424922X21500017
Gladys D. C. Barriga, A. K. Suzuki, V. Cancho, F. Louzada
We propose a new class of survival models for time-to-event data with a cure fraction. This new model is an extension of the promotion time cure rate model. Furthermore, we extend the model to the regression model to evaluate the effect of covariates in the cure fraction. An expectation-maximization algorithm is adopted for estimating the model parameters. A simulation study is conducted in order to assess the proposed model and the computation algorithm. The methodology is illustrated using a real Brazilian bank personal loan portfolio data set.
{"title":"A New Class of Cure Rate Survival Models: Properties, Inference and Applications","authors":"Gladys D. C. Barriga, A. K. Suzuki, V. Cancho, F. Louzada","doi":"10.1142/S2424922X21500017","DOIUrl":"https://doi.org/10.1142/S2424922X21500017","url":null,"abstract":"We propose a new class of survival models for time-to-event data with a cure fraction. This new model is an extension of the promotion time cure rate model. Furthermore, we extend the model to the regression model to evaluate the effect of covariates in the cure fraction. An expectation-maximization algorithm is adopted for estimating the model parameters. A simulation study is conducted in order to assess the proposed model and the computation algorithm. The methodology is illustrated using a real Brazilian bank personal loan portfolio data set.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"35 1","pages":"2150001:1-2150001:15"},"PeriodicalIF":0.6,"publicationDate":"2021-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75969945","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}
Pub Date : 2021-02-17DOI: 10.1142/S2424922X21500030
Y. Tsai, Hwei-Jen Lin, Pei Chi, Kelvin W. Lee
In a previous study, we solved the two-fold dynamic ambulance allocation problem, including forecasting the distribution of Emergency Medical Service (EMS) requesters and dynamically allocating ambulances according to the predicted distribution of requesters. In the definition of the coverage region, the Euclidean distance was used, which is not suitable for measuring the length of a route between two places. This study improved on the previous one by redefining the coverage region for practical application and providing a simulation model to verify the effectiveness of the proposed ambulance allocation method. The simulation results show the proposed allocation method providing higher demand coverage rates and shorter response distances than the official allocation.
{"title":"Empirical Spatial Density-Based Emergency Medical Service Demand Forecast for Ambulance Allocation","authors":"Y. Tsai, Hwei-Jen Lin, Pei Chi, Kelvin W. Lee","doi":"10.1142/S2424922X21500030","DOIUrl":"https://doi.org/10.1142/S2424922X21500030","url":null,"abstract":"In a previous study, we solved the two-fold dynamic ambulance allocation problem, including forecasting the distribution of Emergency Medical Service (EMS) requesters and dynamically allocating ambulances according to the predicted distribution of requesters. In the definition of the coverage region, the Euclidean distance was used, which is not suitable for measuring the length of a route between two places. This study improved on the previous one by redefining the coverage region for practical application and providing a simulation model to verify the effectiveness of the proposed ambulance allocation method. The simulation results show the proposed allocation method providing higher demand coverage rates and shorter response distances than the official allocation.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"31 1","pages":"2150003:1-2150003:23"},"PeriodicalIF":0.6,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80079864","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}
Pub Date : 2020-12-08DOI: 10.1142/s2424922x20020015
Asadullah Shaikh, Khairan D. Rajab
{"title":"Editorial: Special Issue on New Frontiers in Data Sciences and Data Analytics Tools and Applications","authors":"Asadullah Shaikh, Khairan D. Rajab","doi":"10.1142/s2424922x20020015","DOIUrl":"https://doi.org/10.1142/s2424922x20020015","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"133 1","pages":"2002001:1-2002001:3"},"PeriodicalIF":0.6,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72825105","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}
Pub Date : 2020-12-02DOI: 10.1142/s2424922x20500114
R. A. Metzger, J. Doherty, D. Jenkins, D. L. Hall
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.
{"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}
Pub Date : 2020-11-30DOI: 10.1142/s2424922x20500102
Perpetual Andam Boiquaye
This paper focuses primarily on pricing an American put option with a fixed term where the price process is geometric mean-reverting. The change of measure is assumed to be incorporated. Monte Carlo simulation was used to calculate the price of the option and the results obtained were analyzed. The option price was found to be $94.42 and the optimal stopping time was approximately one year after the option was sold which means that exercising early is the best for an American put option on a fixed term. Also, the seller of the put option should have sold $0.01 assets and bought $95.51 bonds to get the same payoff as the buyer at the end of one year for it to be a zero-sum game. In the simulation study, the parameters were varied to see the influence it had on the option price and the stopping time and it showed that it either increases or decreases the value of the option price and the optimal stopping time or it remained unchanged.
{"title":"On Pricing American Put Option on a Fixed Term: A Monte Carlo Approach","authors":"Perpetual Andam Boiquaye","doi":"10.1142/s2424922x20500102","DOIUrl":"https://doi.org/10.1142/s2424922x20500102","url":null,"abstract":"This paper focuses primarily on pricing an American put option with a fixed term where the price process is geometric mean-reverting. The change of measure is assumed to be incorporated. Monte Carlo simulation was used to calculate the price of the option and the results obtained were analyzed. The option price was found to be $94.42 and the optimal stopping time was approximately one year after the option was sold which means that exercising early is the best for an American put option on a fixed term. Also, the seller of the put option should have sold $0.01 assets and bought $95.51 bonds to get the same payoff as the buyer at the end of one year for it to be a zero-sum game. In the simulation study, the parameters were varied to see the influence it had on the option price and the stopping time and it showed that it either increases or decreases the value of the option price and the optimal stopping time or it remained unchanged.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"54 1","pages":"2050010:1-2050010:11"},"PeriodicalIF":0.6,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77146997","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}
Pub Date : 2020-11-30DOI: 10.1142/s2424922x20500096
Maged Nasser, N. Salim, Hamza Hentabli, Faisal Saeed, I. Rabiu
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.
{"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}
Pub Date : 2020-11-26DOI: 10.1142/s2424922x20410053
F. Abbasi, Mohamed-Hedi Karray, R. Houé, M. Memon, B. Archimède
This paper focuses on the proposed generic framework of Cyber-Physical Social Systems (CPSS) for Multidimensional Urban Mobility (MUM). The introduction of air mobility will increase the complexity of urban mobility management due to the increase of data (real-time information exchange), services, and infrastructure. This paper first summarizes the shift of the smart mobility and associated concerns and emphasizes the smart technologies to be introduced for efficient deployment of the MUM. Afterwards, the state-of-the-art on CPSS present the details of the research work in the context of CPSS data management process and challenges related with data. In conclusion, a framework to address the various challenges associated with the data paradigm is proposed.
{"title":"Towards a Knowledge-Driven Framework of Cyber-Physical Social System for Multidimensional Urban Mobility","authors":"F. Abbasi, Mohamed-Hedi Karray, R. Houé, M. Memon, B. Archimède","doi":"10.1142/s2424922x20410053","DOIUrl":"https://doi.org/10.1142/s2424922x20410053","url":null,"abstract":"This paper focuses on the proposed generic framework of Cyber-Physical Social Systems (CPSS) for Multidimensional Urban Mobility (MUM). The introduction of air mobility will increase the complexity of urban mobility management due to the increase of data (real-time information exchange), services, and infrastructure. This paper first summarizes the shift of the smart mobility and associated concerns and emphasizes the smart technologies to be introduced for efficient deployment of the MUM. Afterwards, the state-of-the-art on CPSS present the details of the research work in the context of CPSS data management process and challenges related with data. In conclusion, a framework to address the various challenges associated with the data paradigm is proposed.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"63 1","pages":"2041005:1-2041005:11"},"PeriodicalIF":0.6,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80179912","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}
Pub Date : 2020-11-26DOI: 10.1142/s2424922x20500072
Armstrong Aboah, Y. Adu-Gyamfi
The commonly used index for measuring pavement roughness is the International Roughness index (IRI). Traditional method for collecting road surface information is expensive and as such researchers over the years have resorted to other cheaper ways of collecting data. This study focuses on developing a deep learning model to quickly and accurately determine the IRI values of road sections at a cheaper cost. The study proposed a model that uses accelerometer data and previous year’s IRI values to predict current year IRI values. The study concludes that addition of accelerometer readings to previous year’s IRIs increased the accuracy of prediction.
{"title":"Smartphone-Based Pavement Roughness Estimation Using Deep Learning with Entity Embedding","authors":"Armstrong Aboah, Y. Adu-Gyamfi","doi":"10.1142/s2424922x20500072","DOIUrl":"https://doi.org/10.1142/s2424922x20500072","url":null,"abstract":"The commonly used index for measuring pavement roughness is the International Roughness index (IRI). Traditional method for collecting road surface information is expensive and as such researchers over the years have resorted to other cheaper ways of collecting data. This study focuses on developing a deep learning model to quickly and accurately determine the IRI values of road sections at a cheaper cost. The study proposed a model that uses accelerometer data and previous year’s IRI values to predict current year IRI values. The study concludes that addition of accelerometer readings to previous year’s IRIs increased the accuracy of prediction.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"37 1","pages":"2050007:1-2050007:20"},"PeriodicalIF":0.6,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90218408","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}
Pub Date : 2020-11-18DOI: 10.1142/s2424922x20500084
Irfan Ali Kandhro, Sahar Zafar Jumani, K. Kumar, Abdul Hafeez, F. Ali
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.
{"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}
Pub Date : 2020-11-17DOI: 10.1142/S2424922X20500060
J. Gilles
The recently proposed empirical wavelet transform was based on a particular type of filter. In this paper, we aim to propose a general framework for the construction of empirical wavelet systems in the continuous case. We define a well-suited formalism and then investigate some general properties of empirical wavelet systems. In particular, we provide some sufficient conditions to the existence of a reconstruction formula. In the second part of the paper, we propose the construction of empirical wavelet systems based on some classic mother wavelets.
{"title":"Continuous Empirical Wavelets Systems","authors":"J. Gilles","doi":"10.1142/S2424922X20500060","DOIUrl":"https://doi.org/10.1142/S2424922X20500060","url":null,"abstract":"The recently proposed empirical wavelet transform was based on a particular type of filter. In this paper, we aim to propose a general framework for the construction of empirical wavelet systems in the continuous case. We define a well-suited formalism and then investigate some general properties of empirical wavelet systems. In particular, we provide some sufficient conditions to the existence of a reconstruction formula. In the second part of the paper, we propose the construction of empirical wavelet systems based on some classic mother wavelets.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"48 1","pages":"2050006:1-2050006:21"},"PeriodicalIF":0.6,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87936350","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}
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