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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall machine learning algorithms

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Analysis of decarbonization path in New York state and forecasting carbon emissions using different machine learning algorithms

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CARBON NEUTRALITY 2024年第1期3卷 8页

作者： Ekaterina, Glukhova Li, Jia Shanghai Jiao Tong Univ China UK Low Carbon Coll 3 Yinlian Rd Shanghai 201306 Peoples R China

The state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change.

关键词： machine learning algorithms Forecasting Extreme learning machine Support Vector machine Hybrid models New York CO2 emission

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Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

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MOLECULAR THERAPY NUCLEIC ACIDS 2024年第2期35卷 102155页

作者： Liu, Xiaodie Wang, Wenhui Zhang, Xiaolei Liang, Jing Feng, Dingqing Li, Yuebo Xue, Ming Ling, Bin Chinese Acad Med Sci & Peking Union Med Coll China Japan Friendship Hosp Inst Clin Med Sci Dept Obstet & Gynecol Beijing 100029 Peoples R China China Japan Friendship Hosp Dept Obstet & Gynecol Beijing 100029 Peoples R China Shandong Univ Qilu Hosp Dept Obstet & Gynecol 107 Wenhua West Rd Jinan 250012 Shandong Peoples R China

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets;the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics;CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism -pathway -based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13 -hub gene -based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism -based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism -targeted therapy in EC.

关键词： MT: Bioinformatics endometrial cancer multi-omics metabolism heterogeneity molecular subgroups machine learning algorithms precision therapy nomogram

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Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

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RESEARCH IN VETERINARY SCIENCE 2024年 171卷 105201页

作者： Campler, Magnus R. Cheng, Ting -Yu Lee, Chang -Won Hofacre, Charles L. Lossie, Geoffrey Silva, Gustavo S. El-Gazzar, Mohamed M. Arruda, Andreia G. Ohio State Univ Dept Vet Prevent Med Columbus OH 43210 USA ARS USDA Exot & Emerging Avian Dis Southeast Poultry Res LabNatl Poultry Res Ctr Athens GA 30605 USA Southern Poultry Res Grp Inc Watkinsville GA 30607 USA Purdue Univ Coll Vet Med Dept Comparat Pathobiol & Anim Dis Diagnost Lab W Lafayette IN 47907 USA Iowa State Univ Coll Vet Med Dept Vet Diagnost & Prod Anim Med Ames IA 50011 USA Ohio State Univ Coll Vet Med Dept Vet Prevent Med Columbus OH 43210 USA

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learningbased random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninetytwo geospatial-related and poultry -density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

关键词： Poultry machine learning algorithms Infectious bronchitis virus Risk factor analysis QGIS

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Integrating chemokines and machine learning algorithms for diagnosis and bleeding assessment in primary immune thrombocytopenia: A prospective cohort study

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BRITISH JOURNAL OF HAEMATOLOGY 2024年第5期205卷 1938-1950页

作者： Wen, Qing Sun, Ting Chen, Jia Li, Yang Liu, Xiaofan Li, Huiyuan Fu, Rongfeng Liu, Wei Xue, Feng Ju, Mankai Dong, Huan Dai, Xinyue Wang, Wentian Chi, Ying Yang, Renchi Chen, Yunfei Zhang, Lei Chinese Acad Med Sci & Peking Union Med Coll Inst Hematol & Blood Dis Hosp CAMS Key Lab Gene Therapy Blood Dis Haihe Lab Cell EcosystState Key Lab Expt Hematol Tianjin 300020 Peoples R China Tianjin Inst Hlth Sci Tianjin Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Populat Med & Publ Hlth Beijing Peoples R China

Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder, and chemokines have been shown to be dysregulated in autoimmune disorders. We conducted a prospective analysis to identify potential chemokines that could enhance the diagnostic accuracy and bleeding evaluation in ITP patients. In the discovery cohort, a Luminex-based assay was employed to quantify concentrations of plasma multiple chemokines. These levels were subjected to comparative analysis using a cohort of 60 ITP patients and 17 patients with thrombocytopenia other than ITP (non-ITP). Additionally, comparative evaluation was conducted between a subgroup of 12 ITP patients characterised by bleeding episodes (ITP-B, as defined by an ITP-2016 bleeding grade >= 2) and 33 ITP patients without bleeding episodes (ITP-NB, as defined by an ITP-2016 bleeding grade <= 1). machine learning algorithms further identified CCL20, interleukin-2, CCL26, CCL25, and CXCL1 as promising indicators for accurate diagnosis of ITP and CCL21, CXCL8, CXCL10, CCL8, CCL3, and CCL15 as biomarkers for assessing bleeding risk in ITP patients. The results were confirmed using enzyme-linked immunosorbent assays in a validation cohort (43 ITP patients and 19 non-ITP patients). Overall, the findings suggest that specific chemokines show promise as potential biomarkers for diagnosis and bleeding evaluation in ITP patients.

关键词： bleeding risk chemokine diagnosis ITP machine learning algorithms

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Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Wu, Robert M. X. Shafiabady, Niusha Zhang, Huan Lu, Haiyan Gide, Ergun Liu, Jinrong Charbonnier, Clement Franck Benoit Univ Technol Fac Engn & Informat Technol Sydney Australia Australian Catholic Univ Peter Faber Business Sch Dept Informat Technol North Sydney Australia Shanxi Normal Univ Sch Geog Taiyuan Peoples R China Cent Queensland Univ Sch Engn & Technol Sydney Australia Charles Darwin Univ Fac Sci & Technol Sydney Campus Sydney Australia

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research.

关键词： machine learning algorithms Short-term forecasting Gas warning systems Case study Assessment visualization tool

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Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 94卷

作者： Berigel, Muhammet Boztas, Gizem Dilan Rocca, Antonella Neagu, Gabriela Karadeniz Tech Univ TR-61080 Trabzon Turkiye Univ Naples Parthenope I-80142 Naples Italy Res Inst Qual Life Bucharest 050711 Romania

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability.

关键词： Sustainability NEET SDG machine learning algorithms

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Power System Load Forecasting Using machine learning algorithms: Optimal Approach

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2024年第3期14卷 458-467页

作者： Babu, M. Ravindra Chintalapudi, V. Suresh Kalyan, Ch. Nagasai Bhaskar, K. Krishna Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Narasaraopet Dept Elect & Elect Engn Narasaraopet 522601 Andhra Pradesh India Vasireddy Venkatadri Inst Technol Dept Elect & Elect Engn Guntur 522508 Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Kakinada Dept Mech Engn Kakinada 533003 Andhra Pradesh India

Accurate forecasting of long-term electricity demand has a prominent role in demand side management and electricity system planning and operation. Demand over-estimation gives rise to over-investment in system assets, driving up the electric power prices, while demand under-estimation may give on to under-investment results in unreliable and insecure electricity. The electrical load on a station varies more dynamically and hence forecasting of load values is more significant. In long-term load forecasting (LF), economic factors, weather conditions, time factors and random effects plays a prominent role. machine learning (ML) algorithms often used to estimate the future values constructed on the inferences drawn from the existing values. For these algorithms, the input data is supplied for training and as well testing the algorithm efficacy. Now a days, large amount of data is available everywhere. Therefore, it is essential to analyze this data in order to extract some profitable information and to implement an algorithm based on this analysis. These algorithms works based on historical relationship between the various factors considered in the problem. In this paper, some popular ML algorithms such as Linear develop the Forecasting model for standard 14 and 30 bus systems. The efficiency of the forecasting procedure is examined based on RMSE, Accuracy and time taken to generate the model. Further, the optimization results encourages for implementing these algorithms for real time operations in order to minimize number of loss violation instances in a given system

关键词： Load Forecasting machine learning algorithms Support Vector machine KNN Regression

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Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population

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EPILEPSY RESEARCH 2024年 205卷 107404-107404页

作者： Kaushik, Mahima Mahajan, Siddhartha Machahary, Nitin Thakran, Sarita Chopra, Saransh Tomar, Raj Vardhan Kushwaha, Suman S. Agarwal, Rachna Sharma, Sangeeta Kukreti, Ritushree Biswal, Bibhu Univ Delhi Cluster Innovat Ctr Delhi India Council Sci & Ind Res CSIR Inst Genom & Integrat Biol IGIB Genom & Mol Med Unit Delhi 110007 India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Inst Human Behav & Allied Sci Dept Neurol Delhi India

Purpose: This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). Methods: Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Na & iuml;ve Bayes (NB) and Logistic Regression (LR) to construct classification models. Results: Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chisquare (chi 2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furtherm

关键词： Epilepsy Antiseizure medication machine learning algorithms Predictive analytics Precision medicine

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Prediction and feature selection of low birth weight using machine learning algorithms

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JOURNAL OF HEALTH POPULATION AND NUTRITION 2024年第1期43卷 1-13页

作者： Reza, Tasneem Binte Salma, Nahid Jahangirnagar Univ Dept Stat & Data Sci Dhaka 1342 Bangladesh

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual's health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017-2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML *** pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector machine), NB (Na & iuml;ve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model's performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC *** shows, Boruta algorithm identifies eleven significant features including respondent's age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm's significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model's respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy =

关键词： Low birth weight Boruta algorithm Wrapper method Key features machine learning algorithms

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