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machine learning algorithm

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تکرار جستجوی کلیدواژه machine learning algorithm در مقالات مجلات علمی
  • Jun Hu *, Jingyan Yang, Na Hu, Zongting Shi, Tiemin Hu, Baohong Mi, Hong Wang, Weiheng Chen
    Background
    Glioblastoma (GBM) is the most aggressive form of brain cancer, with poor prognosis despite treatments like temozolomide (TMZ). Resistance to TMZ is a significant clinical challenge, and understanding the genes involved is crucial for developing new therapies and prognostic markers. This study aims to identify key genes associated with TMZ resistance in GBM, which could serve as valuable biomarkers for predicting patient outcomes and potential targets for treatment.
    Objectives
    This study aimed to identify genes involved in TMZ resistance in GBM and to assess the value of these genes in GBM treatment and prognosis evaluation.
    Materials and Methods
    Bioinformatics analysis of Gene Expression Omnibus (GEO) datasets (GSE113510 and GSE199689) and The Chinese Glioblastoma Genome Atlas (CGGA) database was performed to identify differentially expressed genes (DEGs) between GBM cell lines with and without TMZ resistance. Subsequently, the key modules associated with GBM patient prognosis were identified by weighted gene coexpression network analysis (WGCNA). Furthermore, hub genes related to TMZ resistance were accurately screened and confirmed using three machine learning algorithms. In addition, immune cell infiltration analysis, TF-miRNA coregulatory network analysis, drug sensitivity prediction, and gene set enrichment analysis (GSEA) were also performed for temozolomide resistance-specific genes. Finally, the expression levels of key genes were validated in our constructed TMZ-resistant cell lines by real-time quantitative polymerase chain reaction (RT–qPCR) and Western blotting (WB).
    Results
    Integrated analysis of the GEO and CGGA datasets revealed 769 differentially expressed genes (DEGs), comprising 350 downregulated and 419 upregulated genes, between GBM patients and normal controls. Among these DEGs, three key genes, namely, PITX1, TNFRSF11B, and IGFBP2, exhibited significant differences in expression between groups and were prioritized via machine learning algorithms. The expression levels of these genes were found to be closely related to adverse clinical features and immune cell infiltration levels in GBM patients. These genes were also found to participate in several biological pathways and processes. RT‒qPCR and WB confirmed the differential expression of these genes in vitro, indicating that they play vital roles in GBM patients with TMZ resistance.
    Conclusions
    PITX1, TNFRSF11B, and IGFBP2 are key genes associated with the prognosis of GBM patients with TMZ resistance. The differential expression of these genes correlates with adverse outcomes in GBM patients, suggesting that they are valuable biomarkers for predicting patient prognosis and that they could serve as diagnostic biomarkers or treatment targets.
    Keywords: Biomarkers, GEO Database, Glioblastoma, Machine Learning Algorithm, Temozolomide Resistance
  • Alireza Rahimzadeh, Mehran Matinfard *, Zohreh Hajiha, Ehsan Rahmaninia
    The main aim of the represented research is to provide a comprehensive model for predicting the type of audit opinion based on a number of machine learning algorithms in some companies in the Tehran Stock Exchange. In order to achieve this goal, 1,606 company-years (146 companies for 11 years) observations collected from the annual financial reports of companies admitted to the Tehran Stock Exchange from 2010 to 2020 have been tested. In this study, six machine learning algorithms (decision tree and regression, random forest, neural network, nearest neighbor, logit regression, support vector machine) and also two methods of selecting the final variables of the research (two samples mean comparing test, forward step-by-step selection method) has been used for the model creation. The results show that the overall accuracy of decision tree and regression, random forest, neural network, nearest neighbor, logit regression, and support vector machine procedures respectively are 78.7%, 77.7%, 76.9%, 74.6%, 78.3%, and 76.7%. Regarding the obtained outcomes, the decision tree and regression algorithm outperform in forecasting the type of audit opinion compared to other studied methods. Meanwhile, in general, the result of variable selection techniques illustrates that the step-by-step method is far more effective. Hence, in the studied companies in the Tehran Stock Exchange, the step-by-step method and the decision tree and regression algorithm provide the most efficient model for the prediction of the audit opinion type.
    Keywords: : Type Of Audit Opinion, Prediction, Machine Learning Algorithm
  • Hojjat Tajik, Ghodratollah Talebnia *, HamidReza Vakilifard, Faegh Ahmadi

    Artificial intelligence plays an important role in the field of personal computers. Right now personal computers are part of our lives, so AI should be used in all everyday tasks. Humans are great thinkers, but machines can be more effective at counting than humans. The machine cannot fully explain different conditions, but it can create a different type of connection between different salient points and features. Either way, there can be many benefits to establishing machine learning computing in our daily lives. Machine learning or machine learning is one of the subsets of artificial intelligence that enables systems to learn and improve automatically without explicit programming, and controlling the credit risk of real bank customers is one of these benefits that can help the monetary and banking system to improve conditions and reduce risk. Therefore, the use of machine learning to create an algorithm to manage credit risks is a topic addressed in this research.

    Keywords: Intelligent model, Credit Risk, real customers, Banks, Machine learning algorithm
  • ملیحه عباس زاده*، اردشیر هزارخانی، سعید سلطانی محمدی

    مطالعه سیالات درگیر اغلب به صورت آزمایشگاهی و با هدف ارتقا صحت و دقت تجزیه های صورت گرفته انجام می شود. از آنجا که استفاده کاربردی از داده های حاصل از این مطالعات آزمایشگاهی می تواند در فرآیند اکتشاف کانسارها و یا دستیابی به اطلاعات اکتشافی تکمیلی از کانسارهای کشف شده سودمند باشد، در این مطالعه تخمین و مدل سازی پارامترهای ترمودینامیکی سیال درگیر (دمای همگنی، دمای یوتکتیک و شوری) در کانسار مس پورفیری سونگون انجام و در گام نخست، با استفاده از تخمین گر رگرسیون بردار پشتیبان، مدل سه بعدی این پارامترها تهیه شده است. دقت مدل سازی صورت گرفته جهت تخمین داده های سیالات درگیر شامل دمای همگنی، دمای یوتکتیک و شوری سیال درگیر به ترتیب برابر 76، 71 و 93 درصد می باشد. سپس براساس شرایط ترمودینامکی مساعد برای نهشت کالکوپیریت (بازه دمایی 300 تا 400 درجه سانتی گراد و شوری متوسط تا بالا)، از این مدل سه بعدی برای تهیه مدل پیش گویانه کانی زایی استفاده شده است. مقایسه مدل پیش گویانه با مدل بلوکی زمین شناسی عیار مس در محدوده کانسار نشان داد که تطابق مطلوبی بین این دو مدل وجود دارد. در نتیجه می توان 1) از مدل تهیه شده در ادامه فرآیند اکتشاف و با هدف اکتشافات تکمیلی بهره مند شد و 2) از این روش، برای شناسایی مناطق پرپتانسیل کانسارهایی که هنوز در مراحل اکتشافات مقدماتی هستند استفاده کرد.

    کلید واژگان: الگوریتم یادگیری ماشین، رگرسیون بردار پشتیبان، سیالات درگیر، کانسار مس پورفیری سونگون، مدل پیش-گویانه
    Maliheh Abbaszadeh *
    Introduction

    The background of 3D modeling of fluid inclusion data goes back to use of inverse distance weighting (IDW) method in the Caixiashan Pb and Zn deposit (Sun et al., 2011). This method in spite of having some advantages such as simplicity in basis is associated with disadvantages such as uncertainty in selection of weighting function and ignoring data distribution. Today, new methods have been proposed for estimation including the support vector machine method (Dutta et al., 2010). One of this method’s capabilities is in dealing with small data sets (Dutta, 2006; Zhang et al., 1998). In this study, fluid inclusion thermodynamic parameters have been estimated using support vector regression method. Predictive model of mineralization has been provided acording to 3D models resulted for fluid inclusion data and also assumption of proper thermodynamic conditions for chalcopyrite deposition in the Sungun porphyry copper deposit.

    Material and Methods

    In this study, a total of 173 data sets of fluid inclusions were obtained from 59 locations. This dataset using genetic algorithm method divided into training and testing sets (80% and 20%, respectively). Modeling of fluid inclusion thermodynamic parameters has been done by support vector regression method. The SVR is based on the statistical learning theory and the structural risk minimization.

    Results and discussion

    After preparing and determination of training and test datasets, radial basis kernel function (RBF) was selected in order to estimate and model the fluid inclusion thermodynamic parameters using the support vector regression method. Better functionality was the main reason of using this kernel. In the next step, parameters were needed to be carefully determined to obtain a model with high generalization ability. In this regard, the grid search method with cross validation was used to determine optimal values for the model parameters. Model was then trained using the training dataset and finally evaluated on the test dataset. Then fluid inclusion thermodynamic parameters for each block of deposit were estimated using support vector regression method. According to mineralogical and fluid inclusion studies in the Sungun porphyry copper deposit, it has been determined that chalcopyrite deposition is related to fluids with moderate to high salinity and temperatures of 300-400 °C. The predictive model was prepared based on these conditions and estimated thermodynamic Parameters in block model. In this model, each arbitrary block has been labeled on a scale of 1 to 4 (based on the favorable conditions for chalcopyrite deposition). These labels are possibility index for copper deposition. According to possibility index, proper zones have been determined in 3D model. In order to performance evaluation of support vector regression method, the predictive model was compared with 3D model of copper grade. The results of this comparison showed that prepared predictive 3D model has high consistent with copper grade block model.

    Conclusion

    In this study, 3D modeling of fluid inclusion data was performed to estimate the thermodynamic parameters affecting mineralization (homogenization and eutectic temperatures and salinity) using support vector regression method to determine potential mineralization points in the area. Using the 3D models, we found the homogenization and eutectic temperatures and fluids salinity (in different ranges of these factors) in the Sungun porphyry copper deposit. To evaluate the 3D modeling efficiency in advancing the exploration process of the porphyry deposits, the conformity between mineralization and thermodynamic variations of the fluid inclusions was investigated and, based on it; a tool called “Predictive Model” was presented for the evaluation of the occurrence of mineralization in different parts of the region. A comparison of the SVR-based predictive model and the copper grade block model shows acceptable conformity in low, medium, and high-grade regions.

    Keywords: Machine learning algorithm, Support Vector Regression, Fluid inclusion, Sungun Porphyry Copper Deposit, Predictive Model
  • S. Shanthini, A .S. Aarthi Mai

    ABZU is an explainable non- linear model, that has reimagined artificial intelligence to completely change the way problems are solved. It has a new standard of interpretability that has simple visual depictions and mathematical expression for models developed which yields high accuracy. From this, a model developed can be highly accurate and algorithm is recognized to all. This makes more complicated predictions in an easier level and explore new features of the model. This can be achieved through leveraging the results in the form of graphs and representing it. This algorithm applied in the fields of Artificial Intelligence / Machine Learning will yield an accurate result. This improves efficiency and increases the model’s reusability.

    Keywords: Machine learning algorithm, Data science
نکته
  • نتایج بر اساس تاریخ انتشار مرتب شده‌اند.
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