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Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

SENSORS 2022年第6期22卷 2167-2167页

作者： Xue, Bing Shi, Wen Chotirmall, Sanjay H. Koh, Vivian Ci Ai Ang, Yi Yang Tan, Rex Xiao Ser, Wee Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Harvard Univ Harvard Med Sch Cambridge MA 02115 USA Nanyang Technol Univ Lee Kong Chian Sch Med Singapore 637551 Singapore Aevice Hlth Pte Ltd Singapore 637551 Singapore

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.

关键词： acoustic signal processing distance classification feature selection algorithm health monitoring wearable devices

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Automated gender-Parkinson's disease detection at the same time via a hybrid deep model using human voice

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第26期34卷

作者： Kaya, Duygu Firat Univ Dept Elect & Elect Engn Elazig Turkey

Gender and Parkinson disease (PD) identifications are critical parts to be noted from a given in human voice. Numerous artificial intelligence based methods have been proposed to detect gender and PD easily in literature. It is purposed to build an effective and a dependable simultaneously gender and PD recognition system based on feature extraction and feature selection methods in this study. First, CNN structure is used for obtaining deeper features from TQWT applied data and acoustic deep parameters are obtained by it. Later, these deep features are subjected to mRMR feature selection algorithm that increase the performance efficiency of the classifiers. As a result, the crucial features obtained by this hybrid structure and significant success rate 98.9% is obtained with the k-NN classifier. Thus, gender and PD are detected at the same time. Also, this work is multiclass problem so, the other success parameters are calculated separately.

关键词： deep features feature selection algorithm k-NN mRMR TQWT

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Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions

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GEODERMA 2021年 403卷 115379-115379页

作者： Mokarram, Marzieh Zarei, Abdol Rassoul Shiraz Univ Coll Agr & Nat Resources Darab Dept Range & Watershed Management Shiraz Iran Fasa Univ Coll Agr Dept Range & Watershed Management Nat Engn Fasa Iran

Gully erosion is one of the types of water erosion which causes the loss of fertile soil, depletion of soil moisture, and so on. Due to the importance of the identification of the areas that are prone to this type of erosion in the management and appropriate control of this phenomenon, the present study has tried to determine the prone areas to gully erosion in the southern regions of Fars province using Multiple-criteria decision-making (MCDM). For this purpose, First, fuzzy maps are prepared for each parameter with membership functions, and then the analytic hierarchy process (AHP) method was implemented to determine the weight coefficients of each parameter by pairwise comparison based on their importance in determining areas prone to gully erosion. The map of the gully-prone areas was prepared by multiplying the weight coefficients in the fuzzy layers. Since there are too many influential parameters in gully erosion (19 parameters), the feature selection algorithm is carried out to select the most prominent parameters. It is also decided to apply best subsets regression to determine the strength of the relationship between various parameters as well as their effects on gully erosion. Finally, land use changes (i.e., urbanization) and their effect on gully erosion are investigated. The results showed that the areas located in the central regions of the study area (about 15%) are more susceptible to erosion. The results of the feature J48 algorithm (i.e., Best-First and Greedy-Stepwise method) showed that the most important effective parameters in gully erosion are normalized difference vegetation index (NDVI), slope, topographic wetness index (TWI), altitude, terrain ruggedness index (TRI), lithology and land use. Then, the gully erosion map obtained based on these data. The comparison of the map prepared by selection data and the map prepared by all data show that the gully erosion map with selection data has high accuracy (AUC = 0.80%). The best subsets regress

关键词： Gully erosion Fars province Fuzzy method AHP feature selection algorithm Best subsets regression Land use changes

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Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2021年 91卷 107456-107456页

作者： Qin, Xinyi Liu, Min Zhang, Lu Liu, Guangzhong Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiary structure. The realization of protein structure classification is indispensable for this problem and fold recognition is a commonly used method of protein structure classification. Protein sequences of 40% identity in the ASTRAL protein classification database are used for fold recognition research in current work to predict 27 folding types which mostly belong to four protein structural classes: alpha, beta, alpha/beta and alpha + beta. We extract features from primary structure of protein using methods covering DSSP, PSSM and HMM which are based on secondary structure and evolutionary information to convert protein sequences into feature vectors that can be recognized by machine learning algorithm and utilize the combination of LightGBM feature selection algorithm and incremental feature selection method (IFS) to find the optimal classifiers respectively constructed by machine learning algorithms on the basis of tree structure including Random Forest, XGBoost and LightGBM. Bayesian optimization method is used for hyper-parameter adjustment of machine learning algorithms to make the accuracy of fold recognition reach as high as 93.45% at last. The result obtained by the model we propose is outstanding in the study of protein fold recognition.

关键词： Protein fold recognition ASTRAL Secondary structure Evolutionary information feature selection algorithm IFS

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A multiscale and multiparametric approach for modeling the progression of oral cancer

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2012年第1期12卷 136-136页

作者： Exarchos, Konstantinos P. Goletsis, Yorgos Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Fdn Res & Technol Hellas Inst Mol Biol & Biotechnol Dept Biomed Res GR-45110 Ioannina Greece Univ Ioannina Dept Econ GR-45110 Ioannina Greece

Background: In this work, we propose a multilevel and multiparametric approach in order to model the growth and progression of oral squamous cell carcinoma (OSCC) after remission. OSCC constitutes the major neoplasm of the head and neck region, exhibiting a quite aggressive nature, often leading to unfavorable prognosis. Methods: We formulate a Decision Support System assembling a multitude of heterogeneous data sources (clinical, imaging tissue and blood genomic), aiming to capture all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse. Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease. For this purpose a Dynamic Bayesian Network (DBN) is employed which is able to capture in a transparent manner the underlying mechanism dictating the disease evolvement, and employ it for monitoring the status and prognosis of the patients after remission. Results: By feeding as input to the DBN data from the baseline visit we achieve accuracy of 86%, which is further improved to complete discrimination when data from the first follow-up visit are also employed. Conclusions: Knowing in advance the progression of the disease, i.e. identifying groups of patients with higher/lower risk of reoccurrence, we are able to determine the subsequent treatment protocol in a more personalized manner.

关键词： feature selection Random Forest Oral Cancer Oral Squamous Cell Carcinoma feature selection algorithm

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Neural network-based strategies for automatically diagnosing of COVID-19 from X-ray images utilizing different feature extraction algorithms

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023年第1期12卷 28-28页

作者： Prity, Farida Siddiqi Nath, Nishu Nath, Antara Uddin, K. M. Aslam Noakhali Sci & Technol Univ Dept Informat & Commun Engn Noakhali Bangladesh Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA Mainamoti Med Coll Cumilla Bangladesh

The COVID-19 pandemic has had an obliterating impact on the health and well-being of the worldwide populace. It has recently become one of the most severe and acute diseases and has spread globally. Therefore, an automated detection system should be implemented as the fastest diagnostic option to control the spread of COVID-19. This study aims to introduce neural network-based strategies for classifying and detecting COVID-19 early through image processing utilizing X-ray images. Despite the extensive use of directly fed X-ray images into the classifier, there is a lack of comparative analysis between the feature-based system and the direct imaging system in the classification of COVID-19 to evaluate the efficiency of feature extraction methods. Therefore, the proposed system represents introductory experiments of feature-based system using image feature Extraction algorithms [Texture, Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Dependence Matrix (GLDM), Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT)], Dimensionality Reduction algorithms (Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Sparse Autoencoder, and Stacked Autoencoder), feature selection algorithms (Anova F-measure, Chi-square Test, and Random Forest), and Neural Networks [Feed-Forward Neural Network (FFNN) and Convolutional Neural Network (CNN)] on generated datasets consisting of Normal, COVID-19, and Pneumonia-infected chest X-ray images. Tenfold Cross-Validations are implemented during the classification process. A baseline model is created where no feature Extraction algorithm is implemented. Accuracy, sensitivity, specificity, precision, and F-measure metrics are utilized to assess classification performance. Finally, a comprehensive comparison of the success of the feature-based system (feature Extraction algorithms, Dimensionality Reduction algorithms, and feature selection algorithms) in COVID-19 classification from X-ray images is made w

关键词： COVID-19 X-ray image Neural network feature extraction algorithm Dimensionality reduction algorithm feature selection algorithm

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Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic features Using Machine Learning Techniques

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SENSORS 2020年第11期20卷 3127.-3127.页

作者： Chowdhury, Moajjem Hossain Shuzan, Md Nazmul Islam Chowdhury, Muhammad E. H. Mahbub, Zaid B. Uddin, M. Monir Khandakar, Amith Reaz, Mamun Bin Ibne North South Univ Dept Elect & Comp Engn Dhaka 1229 Bangladesh Qatar Univ Dept Elect Engn Doha 2713 Qatar North South Univ Dept Math & Phys Dhaka 1229 Bangladesh Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important;however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

关键词： blood pressure photoplethysmograph feature selection algorithm machine learning

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Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma

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CANCER CELL INTERNATIONAL 2021年第1期21卷 694-694页

作者： Wang, Hanwen Xie, Xiaoxia Zhu, Junyou Qi, Shaohai Xie, Julin Sun Yat Sen Univ Dept Burn Surg Affiliated Hosp 1 Guangzhou 510080 Guangdong Peoples R China

Background Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology. Methods Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC). Results Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM. Conclusions In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

关键词： Bioinformatics feature selection algorithm Skin cutaneous melanoma Prognostic signature IFI16

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Sublineage structure analysis of Mycobacterium tuberculosis complex strains using multiple-biomarker tensors

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BMC GENOMICS 2011年第Sup2期12卷 S1-S1页

作者： Ozcaglar, Cagri Shabbeer, Amina Vandenberg, Scott Yener, Buelent Bennett, Kristin P. Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA Siena Coll Dept Comp Sci Loudonville NY USA

Background: Strains of Mycobacterium tuberculosis complex (MTBC) can be classified into major lineages based on their genotype. Further subdivision of major lineages into sublineages requires multiple biomarkers along with methods to combine and analyze multiple sources of information in one unsupervised learning model. Typically, spacer oligonucleotide type (spoligotype) and mycobacterial interspersed repetitive units (MIRU) are used for TB genotyping and surveillance. Here, we examine the sublineage structure of MTBC strains with multiple biomarkers simultaneously, by employing a tensor clustering framework (TCF) on multiple-biomarker tensors. Results: Simultaneous analysis of the spoligotype and MIRU type of strains using TCF on multiple-biomarker tensors leads to coherent sublineages of major lineages with clear and distinctive spoligotype and MIRU signatures. Comparison of tensor sublineages with SpolDB4 families either supports tensor sublineages, or suggests subdivision or merging of SpolDB4 families. High prediction accuracy of major lineage classification with supervised tensor learning on multiple-biomarker tensors validates our unsupervised analysis of sublineages on multiple-biomarker tensors. Conclusions: TCF on multiple-biomarker tensors achieves simultaneous analysis of multiple biomarkers and suggest a new putative sublineage structure for each major lineage. Analysis of multiple-biomarker tensors gives insight into the sublineage structure of MTBC at the genomic level.

关键词： Major Lineage feature selection algorithm Restriction Fragment Length Polymorphism PARAFAC Model Mycobacterium Tuberculosis Complex

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Analysis of the innovation path of university education management informatization in the era of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhu, Zefeng Zhejiang Normal Univ Acad Affair Off Jinhua Zheiang Peoples R China

In order to have a qualitative improvement in the education management of universities in the background of the era of big data technology, it is necessary to integrate information technology and technological means in the process of education management. Based on this background, this paper constructs an informatization education management platform. In the process of platform architecture, the cloud storage module obtains the exact nearest neighbors by the time factor, optimizes the construction process by using the weighting function and hybrid coordination filtering algorithm, and simplifies the teaching management process. In the teaching management system, the fixed rules of the feature selection algorithm are used to achieve teaching resource selection prediction in the optimal feature subset and establish the best classification surface. Finally, the performance of the platform proposed in this paper is verified from two levels: resource recommendation and education management. The experimental results show that the MAE (Mean Absolute Error) value of this platform is always under 0.7, with a mean value of 0.523, and the experimental teaching resource recommendation effect is better than that of the IE virtual experiment teaching platform and data platform. And the maximum number of visits that the platform can bear is 884, and the maximum amount of government processing can reach 839, which is much larger than the number of 800 users of experimental teaching in the university and fully meets the needs of university education management.

关键词： Big data technology Education management platform Time factor MAE value feature selection algorithm

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