Application Of Machine Learning Technology Research Articles

Optimization of Early Detection of Tuberculosis Through Machine Learning Using Clinical Data: Development and Evaluation of Multilayer Perceptron and Extreme Learning Machine for Tuberculosis Classification

This research takes an innovative step in the fight against Tuberculosis (TB), one of Indonesia's prominent public health challenges, by developing and evaluating Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) models in machine learning for early detection of TB using clinical data. The main result of this study was the discovery that the MLP model, when applied without the Synthetic Minority Over-sampling Technique (SMOTE), achieved an impressive accuracy of 95.00%, signaling significant progress in TB early detection efforts. This discovery not only highlights the great potential of applying machine learning technology in improving the accuracy of TB diagnosis but also paves the way for the possible application of advanced technology in the health sector to deal with infectious diseases. This research illustrates how machine learning technology can be integrated into clinical practice to effectively detect TB cases at an early stage, thus enabling faster and more precise treatment, which can ultimately reduce the spread of the disease. This is particularly important given TB's significant impact on public health, especially in developing countries. The results also open up opportunities for further research into the application of machine learning techniques to other infectious diseases, promising a paradigm shift in the way we detect and manage various health conditions.

RNA secondary structure prediction using transformer-based deep learning models

The Human Genome Project has led to an exponential increase in data related to the sequence, structure, and function of biomolecules. Bioinformatics is an interdisciplinary research field that primarily uses computational methods to analyze large amounts of biological macromolecule data. Its goal is to discover hidden biological patterns and related information. Furthermore, analysing additional relevant information can enhance the study of biological operating mechanisms. This paper discusses the fundamental concepts of RNA, RNA secondary structure, and its prediction.Subsequently, the application of machine learning technologies in predicting the structure of biological macromolecules is explored. This chapter describes the relevant knowledge of algorithms and computational complexity and presents a RNA tertiary structure prediction algorithm based on ResNet. To address the issue of the current scoring function's unsuitability for long RNA, a scoring model based on ResNet is proposed, and a structure prediction algorithm is designed. The chapter concludes by presenting some open and interesting challenges in the field of RNA tertiary structure prediction.

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Hydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility and high sensitivity. With the development of single sensor to multi‐channel and multi‐mode sensor network, the sensor data also presents the characteristics of multi‐dimension, complex and massive. Traditional data analysis methods can no longer meet the data analysis requirements of hydrogel flexible sensor networks. The introduction of machine learning (ML) technology optimizes the process of data analysis. With the continuous development of multi‐layer neural network technology and the improvement of computer performance, deep learning (DL) algorithm is increasingly used to achieve higher efficiency and accuracy, provides a powerful tool for data analysis of hydrogel flexible sensor, and accelerates the intelligent process of hydrogel flexible sensor equipment. This paper introduces the classification of hydrogel flexible sensors and the working mechanism and common algorithms of ML, and summarizes the application of ML technology to assist hydrogel flexible sensors in data analysis in the fields of health care and information recognition. This review will provide inspiration and reference for integrating ML technology into the field of hydrogel flexible sensors.

ESM Cloud Toolkit: A Copilot for Energy Storage Material Research

Searching and designing new materials play crucial roles in the development of energy storage devices. In today’s world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on MatElab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.

Enhancing Magnetic Material Data Analysis with Genetic Algorithm-Optimized Variational Mode Decomposition

As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications.

Machine Learning in Financial Transaction Fraud Detection and Prevention

In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

Cloud Computing for Large-Scale Resource Computation and Storage in Machine Learning

With the rapid development of Internet technology, cloud computing technology has gradually entered people's lives. Cloud computing provides users with various IT resources (computing, storage, etc.) in data centers all over the world through the Internet. Currently, there are hundreds of thousands of servers in large-scale data centers, and effective management of resources in such large-scale data centers is a major problem in academia and industry. This article explores the importance and pervasive use of cloud computing and machine learning in today's technology landscape. As the global cloud computing market size and the application of machine learning technologies continue to grow, the demand for computing and storage resources is also increasing. This paper aims to solve the challenges of large-scale resource computing and storage requirements in machine learning, and puts forward a solution how to give full play to the advantages of cloud computing platform and combine machine learning algorithms and technologies. Through practical data and case studies, we highlight the application scenarios, advantages and challenges of cloud computing in machine learning, and look forward to the future development trend.

Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Urban inundation disasters caused by extreme rainfall events are becoming increasingly severe. However, the numerical inundation model based on physical process has relatively slow computational speed and struggle to meet the demands of current forecasting and early warning systems. Addressing how to integrate machine learning technology into rapid urban inundation prediction, especially when dealing with limited training samples, and selecting the most appropriate machine learning algorithms for different rain patterns, is a crucial and pressing issue. To tackle this problem, this work combines a coupled hydrological-hydrodynamic model known for its high-computational accuracy with efficient machine learning algorithms. Taking different rain patterns into account, a rapid urban inundation prediction method is proposed. This method utilizes data-driven results from the coupled hydrological-hydrodynamic model and constructs multiple machine learning algorithms based on the conclusions drawn from the analysis regarding the most suitable machine learning algorithms for different rain patterns. The results show that for single-peak, double-peak, and uniform rain patterns, the Ridge algorithm, KNN algorithm, and RF algorithm should be respectively employed. The method achieves a mean absolute percentage error of 5.32 %, 7.73 %, and 2.49 % for single-peak, double-peak, and uniform rainfall scenarios and can predict inundation within a 3.68 km2 area in 14.07 s. This method offers high prediction accuracy and fast computational speed, not only meeting the requirements of daily forecasting and early warning but also providing a new direction for the application of machine learning technology in urban inundation prevention.

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

Skin patch based makeup finish assessment technique by deep neural network.

Skin color and texture play a significant role in influencing impressions. To understand the influence of skin appearance and to develop better makeup products, objective evaluation methods for makeup finish have been explored. This study aims to apply machine learning technology, specifically deep neural network (DNN), to accurately analyze and evaluate delicate and complex cosmetic skin textures. "Skin patch datasets" were extracted from facial images and used to train a DNN model. The advantages of using skin patches include retaining fine texture, eliminating false correlations from non-skin features, and enabling visualization of the inferred results for the entire face. The DNN was trained in two ways: a classification task to classify skin attributes and a regression task to predict the visual assessment of experts. The trained DNNs were applied for the evaluation of actual makeup conditions. In the classification task training, skin patch-based classifiers for age range, presence or absence of base makeup, formulation type (powder/liquid) of the applied base makeup, and immediate/while after makeup application were developed. The trained DNNs on regression task showed high prediction accuracy for the experts' visual assessment. Application of DNN to the evaluation of actual makeup conditions clearly showed appropriate evaluation results in line with the appearance of the makeup finish. The proposed method of using DNNs trained on skin patches effectively evaluates makeup finish. This approach has potential applications in visual science research and cosmetics development. Further studies can explore the analysis of different skin conditions and the development of personalized cosmetics.

DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis

In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.

Fusion of multifactor modeling and supervised learning algorithms in quantitative finance: a comparative analysis of predictive and explanatory power

Abstract Finance serves as the lifeblood of the real economy’s development, fundamentally aimed at bolstering the real economy and mitigating financial risks. This paper investigates the application of machine learning technologies in finance, organizing a detailed classification and analysis of associated risks within the sector. Specifically, the study employs the Conditional Value at Risk (CoVaR) to quantify systemic financial risks. It introduces both the Fama-French three-factor and five-factor models for constructing multifactor financial models. It assesses the significance and regression coefficients of risk factors within these models to provide a comparative analysis. Moreover, this research develops an early warning model for financial risk by incorporating financial risk measurement indices into the XGBoost machine learning framework. It utilizes the SHAP (Shapley Additive exPlanations) explanatory framework to elucidate the key features influencing financial risk. This comprehensive approach not only enhances the understanding of financial risk dynamics but also advances the predictive capabilities of financial risk management. It was found that the modified R² increased from 36.52% to 56.25% after adding the IMU factor to the three-factor model, and the coefficient of SMB decreased from 0.135 to −0.215 the larger the size of the enterprise. The accuracy of the financial risk early warning model was 96.35%, and the probability of being predicted by the model to be a high-risk sample was lower when the value of the characteristic INDI was taken as [0,0.125]. Financial institutions can enhance their risk prevention ability by combining the financial risk factors obtained from the multifactor model with the supervised learning algorithm.

Machine learning based techniques for ECG noise removal and feature extraction

Machine learning (ML) is being applied to all aspects of life with the development of artificial intelligence (AI). This paper explores the application of machine learning technology in electrocardiogram (ECG) analysis to diagnose and classify a patient's current cardiac disease, predict possible future diseases, and provide a personalised treatment plan. However, several challenges have been highlighted. First, individual ECG signals display variability, causing concern about effective diagnosis based on changing ECG data. Second, different diseases can produce similar ECG results, requiring powerful classification algorithms to accurately classify diseases. Finally, using patient information to predict the probability of future heart attacks is critical to developing appropriate prevention and treatment strategies. Overcoming these challenges could revolutionise the field of cardiology. It could enable precise and proactive medical intervention. The study highlights the potential of machine learning to improve cardiovascular care and personalised medicine and emphasises the importance of addressing key challenges to maximise its impact in clinical practice.

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students.

Application of artificial intelligence in geography

With the gradual maturation of artificial intelligence technology and the widespread application of machine learning technology in recent years, the research direction of geographic science has shifted dramatically. It has become an important problem in geography to efficiently and accurately mine the inherent laws for these massive, diverse and multidimensional data. Simultaneously, through an in-depth study of geographic information systems, a more reasonable layout and planning in urban and rural construction, traffic management, industrial transfer, and other aspects of human geography can be made. This paper focuses on the application of various machine learning methods in various geographical environments, such as the atmosphere, geology, hydrology, and natural disasters in natural geography. Humanities like urban and rural planning, agricultural production, and tourism geography like GIS, RS, GIS, Beidou are key study object as well.

Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.

Machine Learning Technology for EEG-Forecast of the Blood-Brain Barrier Leakage and the Activation of the Brain's Drainage System during Isoflurane Anesthesia.

Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrity of the blood-brain barrier (BBB), leading to neuroinflammation and neurotoxicity. However, there are no widely used methods for real-time BBB monitoring during surgery. The development of technologies for an express diagnosis of the opening of the BBB (OBBB) is a challenge for reducing post-surgical/anesthesia consequences. In this study on male rats, we demonstrate a successful application of machine learning technology, such as artificial neural networks (ANNs), to recognize the OBBB induced by isoflurane, which is widely used in surgery. The ANNs were trained on our previously presented data obtained on the sound-induced OBBB with an 85% testing accuracy. Using an optical and nonlinear analysis of the OBBB, we found that 1% isoflurane does not induce any changes in the BBB, while 4% isoflurane caused significant BBB leakage in all tested rats. Both 1% and 4% isoflurane stimulate the brain's drainage system (BDS) in a dose-related manner. We show that ANNs can recognize the OBBB induced by 4% isoflurane in 57% of rats and BDS activation induced by 1% isoflurane in 81% of rats. These results open new perspectives for the development of clinically significant bedside technologies for EEG-monitoring of OBBB and BDS.

Application of Machine learning techniques in environmental governance: A review

In recent years, based on the continuous improvement of computer hardware performance, machine learning (ML) technology has made rapid development. Due to the strong ability of ML methods to find complex functions between associated data and the low cost of human and material resources, they are increasingly favored by environmental governance practitioners. Current research has confirmed that the application of ML technology to the field of environmental governance is of great significance in overcoming the difficulties of analysis and practice in traditional environmental work, and can greatly promote the long-term development of such directions as air quality prediction, solid waste classification, pollutant distribution mapping, and intelligent management of water environment. Therefore, by organizing the representative literature in the past five years, this paper describes the current status of the application of ML technology in four different elements of environmental governance, namely, atmosphere, water, soil, and solid waste, and aims to summarize the possible directions of the future development of ML technology and the new development brought to the field of environmental governance by exploring the basic concepts of ML, the advantages and disadvantages of the model and the constraints and other issues.

Design of Virtual Anchor Based on 3Dmax

With the rapid development of virtual reality and live streaming technologies, virtual anchors have become increasingly popular in recent years. In this paper, we propose a design method of virtual anchors based on 3DMAX. Through the use of modeling, rigging, and animation techniques, virtual anchors with realistic appearances and human-like movements can be created. We also explore the application of machine learning technologies in improving the interaction between virtual anchors and users. In addition, we provide a case study on the design and implementation of a virtual anchor for a popular live streaming platform. Our results show that the use of 3DMAX in virtual anchor design can greatly enhance user engagement and improve the overall user experience.Virtual anchor design technology based on 3DMAX is a highly complex design work, which requires designers to have a variety of skills and creative capabilities, and needs to fully consider the needs of the audience and the development trend of the industry. Designers should also have certain cultural accumulation and creative ability, and be able to design an attractive and valuable virtual anchor image from the perspective of the audience.This paper analyzes the production and design of the current virtual anchors, in order to provide some reference significance for the production, operation and commercial realization of the virtual anchors in the future.

Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review

Multi-criterion decision making (MCDM) methods can derive alternative rankings as solutions to decision-making problems based on survey or historical data about the performance or preference information of alternatives regarding multiple criteria. Today's information age makes it easy to accumulate data, but also brings challenges to MCDM, such as massive data and weak data correlation. The real data in the information age should be fully utilized to put MCDM from theoretical formulation into practical application. In this regard, machine learning technologies that can adaptively discover rules as well as patterns from data of different types and characteristics show great application potential. This study dedicates to exploring the status of implementing machine learning technologies in solving MCDM problems. The related work and research advances of MCDM and machine learning technologies are briefly described. A bibliometric analysis is conducted to provide an overview of the research status, hotspots, and trends. Then, we summarize the challenges of implementing MCDM in the information age in four aspects, around which we review the research status of applying machine learning technologies to criteria extraction, criteria interaction, parameter determination, and integrated solutions of MCDM. Also, the fields of practical applications about the subject matter including business management, industrial engineering, sustainable development, emergency management, along with other fields are reviewed. This study outlines how machine learning technologies contribute to MCDM. The lessons learned from the review and future research directions are discussed. It is hoped that this review can serve as a reference and provide convenience for scholars and practitioners in the fields of decision analysis and machine learning.