A. Krajka, Ireneusz Panasiuk, Adam Misiura, Grzegorz M. Wójcik
Abstract Objectives The most common technique of determining biological paternity or another relationship among people are the investigations of DNA polymorphism called Fingerprinting DNA. The key concept of these investigations is the statistical analysis, which leads to obtain the likelihood ratio (LR), sometimes called the paternity index. Methods Among the different assumptions stated in these computations is a mutation model (this model is used for all the computations). Results and conclusions Although its influence on LR is usually negligible, there are some situations (when the mother–child mutation arises) when it is crucial.
{"title":"On the mutation model used in the fingerprinting DNA","authors":"A. Krajka, Ireneusz Panasiuk, Adam Misiura, Grzegorz M. Wójcik","doi":"10.1515/bams-2020-0057","DOIUrl":"https://doi.org/10.1515/bams-2020-0057","url":null,"abstract":"Abstract Objectives The most common technique of determining biological paternity or another relationship among people are the investigations of DNA polymorphism called Fingerprinting DNA. The key concept of these investigations is the statistical analysis, which leads to obtain the likelihood ratio (LR), sometimes called the paternity index. Methods Among the different assumptions stated in these computations is a mutation model (this model is used for all the computations). Results and conclusions Although its influence on LR is usually negligible, there are some situations (when the mother–child mutation arises) when it is crucial.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45538655","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}
Parvathaneni Rajendra Kumar, S. Ravichandran, S. Narayana
Abstract Objectives This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification. Methods As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier. Results An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively. Results Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.
{"title":"Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system","authors":"Parvathaneni Rajendra Kumar, S. Ravichandran, S. Narayana","doi":"10.1515/bams-2020-0033","DOIUrl":"https://doi.org/10.1515/bams-2020-0033","url":null,"abstract":"Abstract Objectives This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification. Methods As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier. Results An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively. Results Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"17 1","pages":"119 - 136"},"PeriodicalIF":1.2,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43689362","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}
Abstract Objectives The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids. Methods This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection. Results Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively. Conclusions From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.
{"title":"A novel melanoma detection model: adapted K-means clustering-based segmentation process","authors":"S. Sukanya, S. Jerine","doi":"10.1515/bams-2020-0040","DOIUrl":"https://doi.org/10.1515/bams-2020-0040","url":null,"abstract":"Abstract Objectives The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids. Methods This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection. Results Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively. Conclusions From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"17 1","pages":"103 - 118"},"PeriodicalIF":1.2,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48204170","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}
Abstract COVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.
{"title":"Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19","authors":"S. Chander, Vijaya Padmanabha, Joseph Mani","doi":"10.1515/bams-2020-0030","DOIUrl":"https://doi.org/10.1515/bams-2020-0030","url":null,"abstract":"Abstract COVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44580519","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}
Natalia Browarska, Aleksandra Kawala-Sterniuk, Przemysław Chechelski, J. Zygarlicki
Abstract Objectives This presents a case for fear and stress stimuli and afterward EEG data analysis. Methods The stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation. Results Results obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants. Conclusions The study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.
{"title":"Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and brain-computer interface system: a case study","authors":"Natalia Browarska, Aleksandra Kawala-Sterniuk, Przemysław Chechelski, J. Zygarlicki","doi":"10.1515/bams-2020-0050","DOIUrl":"https://doi.org/10.1515/bams-2020-0050","url":null,"abstract":"Abstract Objectives This presents a case for fear and stress stimuli and afterward EEG data analysis. Methods The stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation. Results Results obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants. Conclusions The study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45252390","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}
Abstract Objectives The Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG. Methods This survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)-based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope. Results An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions This survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.
{"title":"An empirical survey of electroencephalography-based brain-computer interfaces","authors":"M. Wankhade, S. Chorage","doi":"10.1515/bams-2019-0053","DOIUrl":"https://doi.org/10.1515/bams-2019-0053","url":null,"abstract":"Abstract Objectives The Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG. Methods This survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)-based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope. Results An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions This survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2019-0053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49239341","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}
Natalia Browarska, Aleksandra Kawala-Sterniuk, J. Zygarlicki
Abstract Objectives In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance. Methods The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset. Results The obtained results were promising and complied with the initial assumptions, which stated that the “relax”-phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data. Conclusions Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.
{"title":"Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain–computer interfaces: choosing the appropriate filtering method","authors":"Natalia Browarska, Aleksandra Kawala-Sterniuk, J. Zygarlicki","doi":"10.1515/bams-2020-0051","DOIUrl":"https://doi.org/10.1515/bams-2020-0051","url":null,"abstract":"Abstract Objectives In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance. Methods The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset. Results The obtained results were promising and complied with the initial assumptions, which stated that the “relax”-phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data. Conclusions Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"17 1","pages":"79 - 93"},"PeriodicalIF":1.2,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48996229","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}
Abstract In breast cancer patients, metastasis remains a major cause of death. The metastasis formation process is given by an interaction between the cancer cells and the microenvironment that surrounds them. In this article, we develop a mathematical model that analyzes the role of interleukin IL-17 and its action in promoting cancer and in facilitating tissue metastasis in breast cancer, using a dynamic analysis based on a stochastic process that accounts for the local and global action of this molecule. The model uses the Ornstein–Uhlembeck and Markov process in continuous time. It focuses on the oncological expansion and the interaction between the interleukin IL-17 and cell populations This analysis tends to clarify the processes underlying the metastasis expansion mechanism both for a better understanding of the pathological event and for a possible better control of therapeutic strategies. IL-17 is a proinflammatory interleukin that acts when there is tissue damage or when there is a pathological situation caused by an external pathogen or by a pathological condition such as cancer. This research is focused on the role of interleukin IL-17 which, especially in the case of breast cancer, turns out to be a dominant “communication pin” since it interconnects with the activity of different cell populations affected by the oncological phenomenon. Stochastic modeling strategies, specially the Ornstein-Uhlenbeck process, with the aid of numerical algorithms are elaborated in this review. The role of IL-17 is discussed in this manuscript at all the stages of cancer. It is discussed that IL-17 also acts as “metastasis promoter” as a result of its proinflammatory nature. The stochastic nature of IL-17 is discussed based on the evidence provided by recent literature. The resulting dynamical analysis can help to select the most appropriate therapeutic strategy. Cancer cells, in the case of breast cancer, have high level of IL-17 receptors (IL-17R); therefore the interleukin itself has direct effects on these cells. Immunotherapy research, focused on this cytokine and interlinked with the stochastic modeling, seems to be a promising avenue.
{"title":"Deep learning of the role of interleukin IL-17 and its action in promoting cancer","authors":"Alessandro Nutini, A. Sohail","doi":"10.1515/bams-2020-0052","DOIUrl":"https://doi.org/10.1515/bams-2020-0052","url":null,"abstract":"Abstract In breast cancer patients, metastasis remains a major cause of death. The metastasis formation process is given by an interaction between the cancer cells and the microenvironment that surrounds them. In this article, we develop a mathematical model that analyzes the role of interleukin IL-17 and its action in promoting cancer and in facilitating tissue metastasis in breast cancer, using a dynamic analysis based on a stochastic process that accounts for the local and global action of this molecule. The model uses the Ornstein–Uhlembeck and Markov process in continuous time. It focuses on the oncological expansion and the interaction between the interleukin IL-17 and cell populations This analysis tends to clarify the processes underlying the metastasis expansion mechanism both for a better understanding of the pathological event and for a possible better control of therapeutic strategies. IL-17 is a proinflammatory interleukin that acts when there is tissue damage or when there is a pathological situation caused by an external pathogen or by a pathological condition such as cancer. This research is focused on the role of interleukin IL-17 which, especially in the case of breast cancer, turns out to be a dominant “communication pin” since it interconnects with the activity of different cell populations affected by the oncological phenomenon. Stochastic modeling strategies, specially the Ornstein-Uhlenbeck process, with the aid of numerical algorithms are elaborated in this review. The role of IL-17 is discussed in this manuscript at all the stages of cancer. It is discussed that IL-17 also acts as “metastasis promoter” as a result of its proinflammatory nature. The stochastic nature of IL-17 is discussed based on the evidence provided by recent literature. The resulting dynamical analysis can help to select the most appropriate therapeutic strategy. Cancer cells, in the case of breast cancer, have high level of IL-17 receptors (IL-17R); therefore the interleukin itself has direct effects on these cells. Immunotherapy research, focused on this cytokine and interlinked with the stochastic modeling, seems to be a promising avenue.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"16 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2020-0052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67483182","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}
Abstract It is well-known that chemotherapy is the most significant method on curing the most death-causing disease like cancer. These days, the use of controller-based approach for finding the optimal rate of drug injection throughout the treatment has increased a lot. Under these circumstances, this paper establishes a novel robust controller that influences the drug dosage along with parameter estimation. A new nonlinear error function-based extended Kalman filter (EKF) with improved scaling factor (NEF-EKF-ISF) is introduced in this research work. In fact, in the traditional schemes, the error is computed using the conventional difference function and it is deployed for the updating process of EKF. In our previous work, it has been converted to the nonlinear error function. Here, the updating process is based on the prior error function, though scaled to a nonlinear environment. In addition, a scaling factor is introduced here, which considers the historical error improvement, for the updating process. Finally, the performance of the proposed controller is evaluated over other traditional approaches, which implies the appropriate impact of drug dosage injection on normal, immune and tumor cells. Moreover, it is observed that the proposed NEF-EKF-ISF has the ability to evaluate the tumor cells with a better accuracy rate.
{"title":"Robust controller for cancer chemotherapy dosage using nonlinear kernel-based error function","authors":"U. L. Mohite, H. Patel","doi":"10.1515/BAMS-2019-0056","DOIUrl":"https://doi.org/10.1515/BAMS-2019-0056","url":null,"abstract":"Abstract It is well-known that chemotherapy is the most significant method on curing the most death-causing disease like cancer. These days, the use of controller-based approach for finding the optimal rate of drug injection throughout the treatment has increased a lot. Under these circumstances, this paper establishes a novel robust controller that influences the drug dosage along with parameter estimation. A new nonlinear error function-based extended Kalman filter (EKF) with improved scaling factor (NEF-EKF-ISF) is introduced in this research work. In fact, in the traditional schemes, the error is computed using the conventional difference function and it is deployed for the updating process of EKF. In our previous work, it has been converted to the nonlinear error function. Here, the updating process is based on the prior error function, though scaled to a nonlinear environment. In addition, a scaling factor is introduced here, which considers the historical error improvement, for the updating process. Finally, the performance of the proposed controller is evaluated over other traditional approaches, which implies the appropriate impact of drug dosage injection on normal, immune and tumor cells. Moreover, it is observed that the proposed NEF-EKF-ISF has the ability to evaluate the tumor cells with a better accuracy rate.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"16 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/BAMS-2019-0056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67483114","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}
Anna Gajos-Balinska, Grzegorz M. Wójcik, Przemysław Stpiczyński
Abstract Objectives The electroencephalographic signal is largely exposed to external disturbances. Therefore, an important element of its processing is its thorough cleaning. Methods One of the common methods of signal improvement is the independent component analysis (ICA). However, it is a computationally expensive algorithm, hence methods are needed to decrease its execution time. One of the ICA algorithms (fastICA) and parallel computing on the CPU and GPU was used to reduce the algorithm execution time. Results This paper presents the results of study on the implementation of fastICA, which uses some multi-core architecture and the GPU computation capabilities. Conclusions The use of such a hybrid approach shortens the execution time of the algorithm.
{"title":"Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data","authors":"Anna Gajos-Balinska, Grzegorz M. Wójcik, Przemysław Stpiczyński","doi":"10.1515/BAMS-2020-0044","DOIUrl":"https://doi.org/10.1515/BAMS-2020-0044","url":null,"abstract":"Abstract Objectives The electroencephalographic signal is largely exposed to external disturbances. Therefore, an important element of its processing is its thorough cleaning. Methods One of the common methods of signal improvement is the independent component analysis (ICA). However, it is a computationally expensive algorithm, hence methods are needed to decrease its execution time. One of the ICA algorithms (fastICA) and parallel computing on the CPU and GPU was used to reduce the algorithm execution time. Results This paper presents the results of study on the implementation of fastICA, which uses some multi-core architecture and the GPU computation capabilities. Conclusions The use of such a hybrid approach shortens the execution time of the algorithm.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/BAMS-2020-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42605350","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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