Pub Date : 2023-01-12DOI: 10.1142/s1793962324410010
N. Thenmoezhi, B. Perumal, A. Lakshmi
{"title":"Clustering based image segmentation for optimal image fusion using CT and MRI images","authors":"N. Thenmoezhi, B. Perumal, A. Lakshmi","doi":"10.1142/s1793962324410010","DOIUrl":"https://doi.org/10.1142/s1793962324410010","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84382031","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 : 2023-01-12DOI: 10.1142/s1793962324410022
V. Shanmugarajeshwari, M. Ilayaraja
{"title":"IoT Based Multiclass Decision Support System of Chronic Kidney Disease Using Optimal DNN","authors":"V. Shanmugarajeshwari, M. Ilayaraja","doi":"10.1142/s1793962324410022","DOIUrl":"https://doi.org/10.1142/s1793962324410022","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77519848","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 : 2022-12-23DOI: 10.1142/s1793962323410313
Yunrui Wang, Yaodong Wang, Yao Wang, Juan Li
{"title":"Research on Abnormal Event Diagnosis Method of Complex Product Production Based on Digital Twin","authors":"Yunrui Wang, Yaodong Wang, Yao Wang, Juan Li","doi":"10.1142/s1793962323410313","DOIUrl":"https://doi.org/10.1142/s1793962323410313","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89238670","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 : 2022-12-23DOI: 10.1142/s1793962323500381
Pramitha P Ambily, John T. Abraham
{"title":"Hybrid Classifier for Sentiment Analysis in Malayalam with Modified TF-IDF features","authors":"Pramitha P Ambily, John T. Abraham","doi":"10.1142/s1793962323500381","DOIUrl":"https://doi.org/10.1142/s1793962323500381","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83820509","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 : 2022-12-19DOI: 10.1142/s179396232450003x
S. Gupta, Devendra Kumar, Jagdev Singh
In this paper, we present a reliable numerical algorithm to determine approximate solutions of the two-point boundary value problems having Robin boundary conditions that naturally occur in the investigation of distinct tumor growth issues, the dispersal of heat sources in the person head and steady state oxygen diffusion in spherical cell possessing Michaelis–Menten uptake kinetics. This approach is based on a modified concept of Adomian polynomials (AP), and the two-step Adomian decomposition method (TSADM) merged with Padé approximants. Furthermore a Maple package ADMP is applied to solve various problems, which is very easy to use and efficient and needed to input the system of equations with initial or boundary conditions and diverse essential parameters to deliver the analytic approximate solutions within a few seconds. The suggested scheme does not require linearization, perturbations, guessing the initial terms, a set of basis function or other limiting presumptions, which yields the solutions in closed form. Many examples are examined to make clear the scope and validity of the package ADMP.
{"title":"A reliable algorithm for a class of singular nonlinear two-point boundary value problems arising in physiology","authors":"S. Gupta, Devendra Kumar, Jagdev Singh","doi":"10.1142/s179396232450003x","DOIUrl":"https://doi.org/10.1142/s179396232450003x","url":null,"abstract":"In this paper, we present a reliable numerical algorithm to determine approximate solutions of the two-point boundary value problems having Robin boundary conditions that naturally occur in the investigation of distinct tumor growth issues, the dispersal of heat sources in the person head and steady state oxygen diffusion in spherical cell possessing Michaelis–Menten uptake kinetics. This approach is based on a modified concept of Adomian polynomials (AP), and the two-step Adomian decomposition method (TSADM) merged with Padé approximants. Furthermore a Maple package ADMP is applied to solve various problems, which is very easy to use and efficient and needed to input the system of equations with initial or boundary conditions and diverse essential parameters to deliver the analytic approximate solutions within a few seconds. The suggested scheme does not require linearization, perturbations, guessing the initial terms, a set of basis function or other limiting presumptions, which yields the solutions in closed form. Many examples are examined to make clear the scope and validity of the package ADMP.","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90293130","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 : 2022-12-07DOI: 10.1142/s1793962323410301
Li Dongdong, W. Lei, Cai Jingcao, Wang Anheng, Tan Tielong, Gui Jingsong
{"title":"Research on path planning of mobile robot based on improved genetic algorithm","authors":"Li Dongdong, W. Lei, Cai Jingcao, Wang Anheng, Tan Tielong, Gui Jingsong","doi":"10.1142/s1793962323410301","DOIUrl":"https://doi.org/10.1142/s1793962323410301","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90858839","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 : 2022-12-02DOI: 10.1142/s1793962322500659
A. Ibrahim, A. Zaghrout, K. Raslan, K. Ali
{"title":"On study of the coupled system of nonlocal fractional q-integro-differential equations","authors":"A. Ibrahim, A. Zaghrout, K. Raslan, K. Ali","doi":"10.1142/s1793962322500659","DOIUrl":"https://doi.org/10.1142/s1793962322500659","url":null,"abstract":"","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80951323","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 : 2022-11-30DOI: 10.1142/s1793962323410295
Kalyanapu Jagadeeshwar, T. SreenivasaRao, Padmaja Pulicherla, K. Satyanarayana, K. Mohana Lakshmi, Pala Mahesh Kumar
Automatic speech emotion recognition (ASER) from source speech signals is quite a challenging task since the recognition accuracy is highly dependent on extracted features of speech that are utilized for the classification of speech emotion. In addition, pre-processing and classification phases also play a key role in improving the accuracy of ASER system. Therefore, this paper proposes a deep learning convolutional neural network (DLCNN)-based ASER model, hereafter denoted with ASERNet. In addition, the speech denoising is employed with spectral subtraction (SS) and the extraction of deep features is done using integration of linear predictive coding (LPC) with Mel-frequency Cepstrum coefficients (MFCCs). Finally, DLCNN is employed to classify the emotion of speech from extracted deep features using LPC-MFCC. The simulation results demonstrate the superior performance of the proposed ASERNet model in terms of quality metrics such as accuracy, precision, recall, and F1-score, respectively, compared to state-of-the-art ASER approaches.
{"title":"ASERNet: Automatic speech emotion recognition system using MFCC-based LPC approach with deep learning CNN","authors":"Kalyanapu Jagadeeshwar, T. SreenivasaRao, Padmaja Pulicherla, K. Satyanarayana, K. Mohana Lakshmi, Pala Mahesh Kumar","doi":"10.1142/s1793962323410295","DOIUrl":"https://doi.org/10.1142/s1793962323410295","url":null,"abstract":"Automatic speech emotion recognition (ASER) from source speech signals is quite a challenging task since the recognition accuracy is highly dependent on extracted features of speech that are utilized for the classification of speech emotion. In addition, pre-processing and classification phases also play a key role in improving the accuracy of ASER system. Therefore, this paper proposes a deep learning convolutional neural network (DLCNN)-based ASER model, hereafter denoted with ASERNet. In addition, the speech denoising is employed with spectral subtraction (SS) and the extraction of deep features is done using integration of linear predictive coding (LPC) with Mel-frequency Cepstrum coefficients (MFCCs). Finally, DLCNN is employed to classify the emotion of speech from extracted deep features using LPC-MFCC. The simulation results demonstrate the superior performance of the proposed ASERNet model in terms of quality metrics such as accuracy, precision, recall, and F1-score, respectively, compared to state-of-the-art ASER approaches.","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72950904","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 : 2022-11-30DOI: 10.1142/s1793962324500053
W. Bu, Min Qu
This study proposes a time-stepping [Formula: see text] scheme to approximate the linear fractional dynamical systems based on nonuniform mesh. The developed numerical scheme is unconditionally stable, and exhibits second-order accuracy when the suitable graded mesh is used. A posteriori error estimation is derived for the obtained numerical scheme and the corresponding adaptive algorithm is devised. Finally, two numerical examples are provided to demonstrate the effectiveness of our approach and verify the theoretical results.
{"title":"An effective adaptive algorithm for linear fractional dynamical systems","authors":"W. Bu, Min Qu","doi":"10.1142/s1793962324500053","DOIUrl":"https://doi.org/10.1142/s1793962324500053","url":null,"abstract":"This study proposes a time-stepping [Formula: see text] scheme to approximate the linear fractional dynamical systems based on nonuniform mesh. The developed numerical scheme is unconditionally stable, and exhibits second-order accuracy when the suitable graded mesh is used. A posteriori error estimation is derived for the obtained numerical scheme and the corresponding adaptive algorithm is devised. Finally, two numerical examples are provided to demonstrate the effectiveness of our approach and verify the theoretical results.","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76867305","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 : 2022-11-26DOI: 10.1142/s1793962324500028
Luis Guillermo, Jose-Maria Rojas, W. Ugarte
Visual speech is hard to recreate by human hands because animation itself is a time-consuming task: both precision and detail must be considered and match the expectations of the developers, but above all, those of the audience. To solve this problem, some approaches has been designed to help accelerate the animation of characters faces, as procedural animation or speech-lip synchronization, where the most common areas for researching these methods are Computer Vision and Machine Learning. However, in general, these tools can have any of these main problems: difficulty on adapting to another language, subject or animation software, high hardware specifications, or the results can be receipted as robotic. Our work presents a Deep Learning model for automatic expressive facial animation using audio. We extract generic audio features from expressive audio speeches rich in phonemes for nonidiom focus speech processing and emotion recognition. From videos used for training, we extracted the landmarks for frame-speech targeting and have the model learn animation for phonemes pronunciation. We evaluated four variants of our model (two function losses and with emotion conditioning) by using a user perspective survey where the one using a Reconstruction Loss Function with emotion training conditioning got more natural results and score in synchronization with the approval of the majority of interviewees. For perception of naturalness, it obtained a 38.89% of the total votes of approval and for language synchronization obtained the highest average score with 65.55% (98.33 of a 150 total points) for English, German and Korean languages.
{"title":"Emotional 3D speech visualization from 2D audio visual data","authors":"Luis Guillermo, Jose-Maria Rojas, W. Ugarte","doi":"10.1142/s1793962324500028","DOIUrl":"https://doi.org/10.1142/s1793962324500028","url":null,"abstract":"Visual speech is hard to recreate by human hands because animation itself is a time-consuming task: both precision and detail must be considered and match the expectations of the developers, but above all, those of the audience. To solve this problem, some approaches has been designed to help accelerate the animation of characters faces, as procedural animation or speech-lip synchronization, where the most common areas for researching these methods are Computer Vision and Machine Learning. However, in general, these tools can have any of these main problems: difficulty on adapting to another language, subject or animation software, high hardware specifications, or the results can be receipted as robotic. Our work presents a Deep Learning model for automatic expressive facial animation using audio. We extract generic audio features from expressive audio speeches rich in phonemes for nonidiom focus speech processing and emotion recognition. From videos used for training, we extracted the landmarks for frame-speech targeting and have the model learn animation for phonemes pronunciation. We evaluated four variants of our model (two function losses and with emotion conditioning) by using a user perspective survey where the one using a Reconstruction Loss Function with emotion training conditioning got more natural results and score in synchronization with the approval of the majority of interviewees. For perception of naturalness, it obtained a 38.89% of the total votes of approval and for language synchronization obtained the highest average score with 65.55% (98.33 of a 150 total points) for English, German and Korean languages.","PeriodicalId":45889,"journal":{"name":"International Journal of Modeling Simulation and Scientific Computing","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85295645","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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