检索结果-内蒙古大学图书馆

machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

HELIYON 2024年第4期10卷 e26652页

作者： Altman, Morteza Babaee Wan, Wenbin Hosseini, Amineh Sadat Nowdeh, Saber Arabi Alizadeh, Masoumeh Amirkabir Univ Technol Tehran Polytech Dept Energy Engn Tehran *** Iran Univ New Mexico Dept Mech Engn MSC01 1150 Albuquerque NM 87131 USA Islamic Azad Univ Dept Elect & Biomed Engn Gorgan Golestan Iran Golestan Tech & Vocat Training Ctr Gorgan Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran *** Iran

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast machine learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications.

关键词： Biomedical applications Embedded System FPGA Hardware acceleration machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

引用

AGRIBUSINESS 2023年第1期39卷 214-241页

作者： Atalan, Abdulkadir Gaziantep Islam Sci & Technol Univ Dept Ind Engn Gaziantep Turkey

The study aimed to describe and test machine learning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross-validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean absolute error (MAE) (0.029) in the training and MSE (0.009), RMSE (0.096), and MA (0.055) in the testing dataset. This study presents an interesting perspective with practical potential to adopt ML methods in the dairy industry. The developed ML algorithms can provide dairy investors and policymakers with important decision-support information. [EconLit Citations: C13, C53, L66, C88].

关键词： factors machine learning algorithms milk price prediction

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithms and Neural Networks for Recognition of Parasitic Parameters by the Output Signal in High-Power Pulsed Electrophysics Devices

引用

PHYSICS OF ATOMIC NUCLEI 2023年第12期86卷 2696-2702页

作者： Averyanov, G. P. Dmitrieva, V. V. Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Moscow 115409 Russia

The problem of recognition and classification of loads at the output of generating and transmitting distributed parameter lines (DPL) in devices of high-power pulse technology (HPPT) by the amplitude and shape of the output signal using mathematical models based on machine learning methods and neural networks has been considered. A web application that recognizes parasitic parameters occurring in the devices based on DPL has been developed.

关键词： electrophysics high-power pulsed devices forming and transmitting distributed parameter lines machine learning algorithms convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

machine learning algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile

引用

WATER 2023年第11期15卷 1994页

作者： Rodriguez-Lopez, Lien Bustos Usta, David Bravo Alvarez, Lisandra Duran-Llacer, Iongel Lami, Andrea Martinez-Retureta, Rebeca Urrutia, Roberto Univ San Sebastian Fac Ingn Arquitectura & Diseno Lientur 1457 Concepcion 4030000 Chile Univ Concepcion Fac Oceanog Concepcion 4030000 Chile Univ Concepcion Dept Elect Engn Edmundo Larenas 219 Concepcion 4030000 Chile Univ Mayor Fac Ciencias Ingn & Tecnol Hemera Ctr Observac Tierra Camino La Piramide 5750 Santiago 8580745 Chile Inst Water Res IRSA Sez Verbania I-1000015 Verbania CP Italy Univ Concepcion Fac Ciencias Ambientales Concepcion 4030000 Chile

The world's water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources.

关键词： machine learning algorithms chlorophyll-a lake

来源：评论

学校读者我要写书评

暂无评论

Prediction of risk factors for first trimester pregnancy loss in frozen-thawed good-quality embryo transfer cycles using machine learning algorithms

引用

JOURNAL OF ASSISTED REPRODUCTION AND GENETICS 2023年第2期40卷 279-288页

作者： Ozer, Gonul Akca, Aysu Yuksel, Beril Duzguner, Ipek Pehlivanli, Ayca Cakmak Kahraman, Semra Mem Sisli Hosp IVF & Reprod Genet Ctr Piyalepasa Bulvari TR-35385 Istanbul Turkey Mimar Sinan Fine Arts Univ Fac Sci Bomonti Campus TR-34380 Istanbul Turkey Mimar Sinan Fine Arts Univ Letters Stat Dept Bomonti Campus TR-34380 Istanbul Turkey

Purpose Can the risk factors that cause first trimester pregnancy loss in good-quality frozen-thawed embryo transfer (FET) cycles be predicted using machine learning algorithms? Methods This is a retrospective cohort study conducted at Sisli Memorial Hospital, ART and Reproductive Genetics Center, between January 2011 and May 2021. A total of 3805 good-quality FET cycles were included in the study. First trimester pregnancy loss rates were evaluated according to female age, paternal age, body mass index (BMI), diagnosis of infertility, endometrial preparation protocols (natural/artificial), embryo quality (top/good), presence of polycystic ovarian syndrome (PCOS), history of recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), severe male infertility, adenomyosis and endometriosis. Results The first trimester pregnancy loss rate was 18.2% (693/ 3805). The presence of RPL increased first trimester pregnancy loss (OR = 7.729, 95%CI = 5.908-10.142, P = 0.000). BMI, which is > 30, increased first trimester pregnancy loss compared to < 25 (OR = 1.418, 95%CI = 1.025-1.950, P = 0.033). Endometrial preparation with artificial cycle increased first trimester pregnancy loss compared to natural cycle (OR = 2.101, 95%CI = 1.630-2.723, P = 0.000). Female age, which is 35-37, increased first trimester pregnancy loss compared to < 30 (OR = 1.617, 95%CI = 1.120-2.316, P = 0.018), and female age, which is > 37, increased first trimester pregnancy loss compared to < 30 (OR = 2.286, 95%CI = 1.146-4,38, P = 0.016). The presence of PCOS increased first trimester pregnancy loss (OR = 1.693, 95%CI = 1.198-2.390, P = 0.002). The number of previous IVF cycles, which is > 3, increased first trimester pregnancy loss compared to < 3 (OR = 2.182, 95%CI = 1.708-2.790, P = 0.000). Conclusions History of RPL, RIF, advanced female age, presence of PCOS, and high BMI (> 30 kg/m(2)) were the factors that increased first trimester pregnancy loss.

关键词： First trimester pregnancy loss Frozen-thawed embryo transfer (FET) cycles In vitro fertilization (IVF) machine learning algorithms Infertility

来源：评论

学校读者我要写书评

暂无评论

Spectral characterization and classification of two different crown root rot and vascular wilt diseases (fusarium oxysporum ***. radicis lycopersici and fusarium solani) in tomato plants using different machine learning algorithms

引用

EUROPEAN JOURNAL OF PLANT PATHOLOGY 2023年第2期165卷 271-286页

作者： Bilgili, Aysin Bilgili, Ali Volkan Tenekeci, Mehmet Emin Karadag, Kerim GAP Agr Res Inst GAPTAEM Dept Plant Hlth TR-63100 Sanliurfa Turkey Harran Univ Agr Fac Dept Soil Sci & Plant Nutr Osmanbey Campus TR-63300 Sanliurfa Turkey Harran Univ Fac Engn Dept Comp Engn Osmanbey Campus TR-63300 Sanliurfa Turkey Harran Univ Fac Engn Dept Elect & Elect Engn Osmanbey Campus TR-63300 Sanliurfa Turkey

Fusarium oxysporum ***. radicis lycopersici (FORL) and Fusarium solani (F.S.) are common fungi responsible for crown root rot and vascular wilt diseases that highly impact the development of plants, causing significant yield losses. This study investigated changes in the hyperspectral reflectance of normal and Fusarium (FORL and F.S.)-infected tomato plants in a growth chamber at different disease stages (3, 10, 16, 23, 31 and 37 days after inoculation (DAI)) using a spectroradiometer as an alternative to traditional approaches for the early identification and classification of such diseases. Raw spectra, significant wavebands obtained with the RELIEF algorithm and various statistical features extracted from raw spectra were used to classify healthy and infected plants using three different classification algorithms (CAs): decision tree, cubic support vector machine and k-nearest neighbor models. At different stages of the disease, the spectral bands such as 508, 711, 540, 717, 536, 644 nm and 705, 1883, 525, 518, 444, 522 nm were the most effective in distinguishing FORL and F.S.-inoculated plants from healthy plants, respectively. While FORL caused general stress in the plants, F.S. also had a negative physiological effect. All CAs proved highly successful in distinguishing healthy and diseased plants, with maximum classification accuracy achieved as early as 3 DAI. CAs using statistical parameters as input had higher accuracies than other CAs. Healthy and diseased plant classification was significantly different between the different CAs (p < 0.05), while DAI, pathogen type and inputs of the classification did not exhibit significant differences in classification (p > 0.05) according to ANOVA.

关键词： Tomatoes Fusarium spp machine learning algorithms Visible near-infrared spectroradiometry Classification ANOVA

来源：评论

学校读者我要写书评

暂无评论

Identification and validation of shared genes and key pathways in endometriosis and endometriosis-associated ovarian cancer by weighted gene co-expression network analysis and machine learning algorithms

引用

JOURNAL OF OBSTETRICS AND GYNAECOLOGY RESEARCH 2023年第8期49卷 2135-2150页

作者： Du, Yongming Hu, Yichao Zhang, Fubin Guan, Yutao Sheng, Yuehua Ningbo Univ Affiliated Hosp 1 Dept Gynecol Ningbo Zhejiang Peoples R China Ningbo Univ Affiliated Hosp 1 Dept Urol Ningbo Zhejiang Peoples R China Ningbo Univ Affiliated Hosp 1 Dept Gynecol & Obstet Ningbo Zhejiang Peoples R China

Background: Epidemiological studies reported that patients with endometriosis had an increased risk of developing endometriosis-associated ovarian cancer (EAOC). The present study aimed to identify shared genes and key pathways that commonly interacted between EAOC and ***: The expression matrix of ovarian cancer and endometriosis were collected from the Gene Expression Omnibus database. The weighted gene co-expression network analysis (WGCNA) was used to construct co-expression gene network. machine learning algorithms were applied to identify characteristic genes. CIBERSORT deconvolution algorithm was used to explore the difference in tumor immune microenvironment. Furthermore, diagnostic nomogram was constructed and evaluated for supporting clinical ***: We identified 262 shared genes between EAOC and endometriosis via WGCNA analysis. They were mainly enriched in cytokine-cytokine receptor interaction. After protein-protein interaction network and machine learning algorithms, we recognized two characteristic genes (EDNRA, OCLN) and established a nomogram that presented an outstanding predictive performance. The hub genes demonstrated remarkable associations with immunological functions. Survival analysis indicated that dysregulated expressions of EDNRA and OCLN were closely correlated with prognosis of ovarian cancer patients. gene set enrichment analyses revealed that the two characteristic genes were mainly enriched in the cancer- and immune-related ***: Our findings pave the way for further investigation of potential candidate genes and will aid in improving the diagnosis and treatment of EAOC in endometriosis patients. More research is required to determine the exact mechanisms by which these two hub genes affecting the development and progression of EAOC from endometriosis.

关键词： endometriosis endometriosis-associated ovarian cancer machine learning algorithms WGCNA

来源：评论

学校读者我要写书评

暂无评论

Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants

引用

INTERNATIONAL JOURNAL OF CLINICAL PHARMACY 2023年第3期45卷 659-668页

作者： Sridharan, Kannan Shah, Shamik Arabian Gulf Univ Coll Med & Med Sci Dept Pharmacol & Therapeut Manama Bahrain Salmaniya Med Complex Dept Nephrol Manama Bahrain Arabian Gulf Univ Coll Med & Med Sci Dept Internal Med Manama Bahrain

BackgroundSingle nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal *** set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant *** sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (beta) with a 95% confidence interval (CI) were used as the model *** a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (beta -1.8;95% CI -3, -0.5;p = 0.006), and age (beta -0.04;95% CI -0.1, -0.006;p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (beta -80.8;95% CI -130.3, -31.2;p = 0.001), and age (beta -3.4;95% CI -5.9, -0.9;p = 0.007).ConclusionWe observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens;yet, the findings must be externally validated.

关键词： Artificial intelligence Cyclosporine Immunosuppressants machine learning algorithms Tacrolimus

来源：评论

学校读者我要写书评

暂无评论

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin

引用

INTERNATIONAL JOURNAL OF CLINICAL PHARMACY 2023年第1期45卷 79-87页

作者： Sridharan, Kannan Ramanathan, Murali Al Banna, Rashed Arabian Gulf Univ Coll Med & Med Sci Dept Pharmacol & Therapeut Manama Bahrain Univ Buffalo State Univ New York Sch Pharm & Pharmaceut Sci Dept Pharmaceut Sci Buffalo NY USA Salmaniya Med Complex Dept Cardiol Manama Bahrain

Background machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine. Aim We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD). Method Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models. Results In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4. Conclusion Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted.

关键词： Anticoagulants Artificial intelligence Decision trees machine learning algorithms Pharmacogenetics

来源：评论

学校读者我要写书评

暂无评论

Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study

引用

JOURNAL OF DIABETES INVESTIGATION 2023年第2期14卷 309-320页

作者： Mao, Yaqian Zhu, Zheng Pan, Shuyao Lin, Wei Liang, Jixing Huang, Huibin Li, Liantao Wen, Junping Chen, Gang Fujian Med Univ Fujian Prov Hosp Dept Internal Med South BranchShengli Clin Med Coll Fuzhou Peoples R China Fujian Med Univ Fujian Prov Hosp Dept Endocrinol Shengli Clin Med Coll Fuzhou Peoples R China Fujian Acad Med Fujian Prov Key Lab Med Anal Fuzhou Peoples R China

Aims/Introduction To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10-fold cross-validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823-0.886) in the training set and 0.835 (95% CI 0.779-0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814-0.866) in the training set and 0.834 (95% CI 0.785-0.884) in the test set. Conclusions In the real-world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.

关键词： Diabetes machine learning algorithms Predictive model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：