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Detecting Anxiety via machine learning algorithms: A Literature Review

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Tayarani-N., M. -H. Shahid, Shamim Ibne Univ Hertfordshire Sch Phys Engn & Comp Sci HATFIELD Hatfield AL10 9AB England

Recent machine learning (ML) advances have opened up new possibilities for addressing various challenges. Given their ability to tackle complex problems, the use of ML algorithms in diagnosing mental health disorders has seen substantial growth in both the number and scope of studies. Anxiety, a major health concern in today's world, affects a significant portion of the population. Individuals with anxiety often exhibit distinct characteristics compared to those without the disorder. These differences can be observed in their outward appearance-such as voice, facial expressions, gestures, and movements-and in less visible factors like heart rate, blood test results, and brain imaging data. In this context, numerous studies have utilized ML algorithms to extract a diverse range of features from individuals with anxiety, aiming to build predictive models capable of accurately identifying those affected by the disorder. This paper performs a comprehensive literature review on the state-of-the-art studies that employ machine learning algorithms to identify anxiety. This paper aims to cover a wide range of studies and categorize them based on their methodologies and data types used.

关键词： Anxiety disorders machine learning algorithms Mental health Prediction algorithms Feature extraction Depression Accuracy Systematic literature review Sleep Signal processing algorithms Anxiety disorder machine learning artificial intelligence mental disorder social signal processing affective computing

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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.)

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FOOD CHEMISTRY-X 2025年 27卷 102399页

作者： Wei, Jinpeng Dai, Zhangyan Zhang, Qi Yang, Le Zeng, Zhaoqi Zhou, Yuliang Liu, Jun Chen, Bingxian South China Agr Univ Coll Agr Guangzhou 510642 Peoples R China Guangdong Acad Agr Sci Agrobiol Gene Res Ctr Guangdong Prov Key Lab Crop Germplasm Resources Pr Guangzhou 510640 Peoples R China Zhongkai Univ Agr & Engn Coll Agr & Biol Guangzhou 510550 Peoples R China

Accurate identification of high-quality seeds is crucial for maintaining superior crop traits. Lettuce is widely consumed vegetable with diverse varieties, however, the traditionally identification methods are both timeconsuming and labor-intensive. This study explores feasibility of rapid, non-destructive identification of different lettuce varieties using multispectral imaging combined with machine learning. We firstly collected seed morphological and spectral data from 15 lettuce varieties using multispectral imaging. Then we applied Support Vector machine (SVM), Random Forest (RF), and Back-Propagation Neural Network (BP), Linear Discriminant Analysis (LDA) for variety identification. The results demonstrated that multispectral imaging combined with machine learning models, effectively distinguished different lettuce seed varieties. The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. In the batch validation, the LDA model achieved an accuracy of 93.2 %.This method reduces cost and improves efficiency, showing great potential for seed identification in other crops.

关键词： Lettuce Variety identification Seed multispectral imaging machine learning algorithms

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Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Ma, Xiao Chongqing Business Vocat Coll Sch Accounting Shapingba 401331 Chongqing Peoples R China

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

关键词： machine learning algorithms Virtual entertainment robots College students Psychological health services

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Groundwater Quality Prediction Using Proximal Hyperspectral Sensing, GIS, and machine learning algorithms

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WATER AIR AND SOIL POLLUTION 2025年第6期236卷 1-25页

作者： Raheja, Hemant Goel, Arun Pal, Mahesh Natl Inst Technol Kurukshetra Dept Civil Engn Kurukshetra 136119 Haryana India

The primary objective of the present study is to assess the suitability of groundwater for drinking purposes using the Water Quality Index (WQI), Geographic Information System (GIS), and proximal sensing techniques. For this purpose, 272 groundwater samples collected before and after the monsoon period were analyzed for various hydrochemical parameter concentrations. To explore the relationship between water spectral reflectance and WQI, a spectroradiometer was used to measure the reflectance of each sample under laboratory conditions. Four machine learning procedures i.e., Support Vector Regression (SVR), M5P, Random Forest (RF), and Gaussian Process Regression (GPR) were used to predict WQI using resampled spectral reflectance. The WQI analysis revealed that approximately 30% of the groundwater samples fell within the poor to extremely poor-quality category in both periods, while 55.88% (Pre-monsoon) and 55.14% (Post-monsoon) of samples were classified under moderate quality, indicating marginal suitability for drinking purposes. The spectral curves of WQI indicated a lower reflectance for water samples with lower WQI values, whereas higher reflectance values were associated with higher WQI values. Performance evaluation of machine learning models demonstrated that SVR outperformed M5P, RF, and GPR, achieving the highest Correlation Coefficient (CC = 0.9964) and the lowest Root Mean Square Error (RMSE = 7.0313 mg/L) and Mean Absolute Error (MAE = 3.5056 mg/L) in the training phase, while maintaining similar performance in the testing phase (CC = 0.9964, RMSE = 7.6748 mg/L, MAE = 4.0297 mg/L). These findings highlight the potential of integrating spectral reflectance with machine learning models for accurate groundwater quality assessment and spatial mapping.

关键词： Water Quality Index (WQI) Spatial Distribution Spectral Reflectance machine learning algorithms

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Interpreting the relationship between properties of wood and pulping & paper via machine learning algorithms combined with SHAP analysis

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NORDIC PULP & PAPER RESEARCH JOURNAL 2025年第1期40卷 149-160页

作者： Liu, Xing Hong, Jie Zhang, Mingming Zhou, Liang Anhui Agr Univ Sch Mat & Chem Key Lab Natl Forestry & Grassland Adm Wood Qual I Hefei 230036 Peoples R China

The pulping ability and quality of paper high relay on the wood properties. However, the relationship between them are profound. Based on the extracting digital information from the anatomical, chemical, and physical properties of poplar wood, predictive models were developed for paper properties (tensile index, burst index and tear index) and pulping properties (Kappa number and pulp yield) using six algorithms, namely PLSR, ENR, RF, XGBoost, LightGBM, and CatBoost. The prediction results revealed that among the six algorithms, PLSR, ENR, and RF exhibited results of most prediction greater than 0.79. Notably, XGBoost, LightGBM, and CatBoost algorithms demonstrated superior predictive performance, with results greater than 0.9, except for the tear index. Furthermore, SHAP analysis suggested that the cellulose content is the primary factors to modulate pulping ability and the morphological features of cell wall shows apparent effects on mechanical properties of paper. It hopes the result will benefit to provide information to evaluate the value of poplar wood from different resources and then deliver instructions to genetic breeding program and forest management of poplar plantation.

关键词： poplar paper properties pulping properties machine learning algorithms shapley additive exPlanations

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Prediction of building-scale solar energy potential in urban environment based on parametric modelling and machine learning algorithms

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Tian, Jia Ooka, Ryozo Univ Tokyo Grad Sch Engn Dept Architecture 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

Building integrated photovoltaics has received increasing attention due to its superior low-carbon characteristics. However, the surrounding environments influence the pattern of solar radiation distributed on building exteriors, posing a challenge to the installation of photovoltaic (PV) on buildings. Therefore, to optimize the use of solar resources for buildings in diverse block scenarios, a comprehensive assessment approach for building-scale solar energy potential is proposed in this research, using parametric modelling and machine learning algorithms. First, the building-scale solar energy potential is assessed under thousands of surrounding environments. Next, contributions of building factors to solar potential are analyzed. Finally, machine learning algorithms are employed to forecast the PV installation and generation for roofs and facades within different surrounding environments. Results show that for target buildings higher than 24 m, the PV installation ratio of roof, west and south fa & ccedil;ades are 98 %, 39 % and 46 %. Correspondingly, their average PV power potentials are 46kWh/m2.y, 75kWh/m2.y and 87kWh/m2.y. Based on the priority of contributions of building parameters to solar potential, the applicability of prediction is verified to provide optimal forecasting models and input combinations for predicting PV application on roofs and facades. The findings and workflows can provide references for predicting solar applications in buildings.

关键词： Building integrated photovoltaics Solar energy potential forecasting Photovoltaic power generation Solar radiation Parametric modelling machine learning algorithms

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Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms

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ENVIRONMENTAL POLLUTION 2025年 373卷 126159页

作者： Shi, Guangyao Du, Huihui Du, Lingtong Ni, Xilu Hu, Yang Pang, Danbo Yao, Liangjin Ningxia Univ Sch Ecol & Environm Breeding Base State Key Lab Land Degradat & Ecol R Yinchuan 750021 Peoples R China Ningxia Yinchuan Urban Ecosyst Res Stn Yinchuan 750021 Peoples R China Yinchuan Ecol Environm Monitoring Stn Yinchuan 750001 Peoples R China Zhejiang Acad Forestry Hangzhou 310023 Peoples R China

Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O3) formation. Understanding their distribution and pollution sources is essential to grasping the multifaceted impact of VOCs on O3 production. This study, conducted at the Yinchuan Urban Ecosystem Research Station, carried out simultaneous field observations to collect data on VOCs, meteorological factors, and O3. machine learning algorithms were employed to analyze the sources of VOCs pollution and evaluate their impact on O3 formation. Results show that the monthly average volume fraction of total VOCs was 29.28 x 10-9, with alkanes dominating, accounting for 51.1 % of total VOCs during summer at high altitudes. Ethane (3.55 x 10-9), n-hexane (3.36 x 10-9), and propane (2.85 x 10-9) were identified as key components. Artificial source contributed 78.6 % of VOCs emissions in summer, with hydrocarbon volatile emission source (31.6 %) and vehicle emission source (30.1 %) being the major sources, while natural emissions accounted for only 21.4 %. VOCs exhibited a notable negative impact on O3 levels, reflected by a total effect value of -0.29. Among the VOCs components, aromatics, halocarbons, and alkanes were identified as the primary contributors to O3 dynamics, with respective effect values of 0.84, 0.75, and 0.71, and their contribution rates were quantified as 21.8 %, 19.4 %, and 18.4 %, respectively. Among meteorological factors, temperature was a key determinant of O3 levels, with a significant positive effect (effect value of 0.58). Temperature, wind speed, and relative humidity primarily influenced O3 through direct effects, while photosynthetically active radiation indirectly influenced O3 by affecting VOCs. The findings of this study link pollution sources, meteorological factors, and air quality management. Through systematic multidimensional analysis, it offers deeper insights into the complex relationships between meteorological factors, VOC

关键词： O3 VOCs Atmospheric pollution machine learning algorithms Structural equation model

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Predicting the Classification of Heart Failure Patients Using Optimized machine learning algorithms

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IEEE ACCESS 2025年 13卷 30555-30569页

作者： Ahmed, Marzia Sulaiman, Mohd Herwan Hassan, Md Maruf Bhuiyan, Touhid Daffodil Int Univ DIU Dept Software Engn Daffodil Smart City Dhaka 1216 Bangladesh Univ Malaysia Pahang Al Sultan Abdullah UMPSA Fac Elect & Elect Engn Technol Pekan 26600 Malaysia Southeast Univ Dept Comp Sci & Engn Dhaka Bangladesh Washington Univ Sci & Technol WUST Sch IT Alexandria VA 22314 USA

Heart failure is a critical condition with a high mortality rate, making accurate survival prediction essential for timely interventions. This study proposes an optimized machine learning approach using Gradient Boosting machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) to predict heart failure survival. The dataset, sourced from Kaggle, includes clinical features such as age, ejection fraction, and serum creatinine levels for 299 heart failure patients. To address the imbalance in survival outcomes, Synthetic Minority Over-sampling Technique (SMOTE) was employed to balance the dataset, followed by SelectKBest and Chi-square feature selection methods to retain the most significant predictors. The optimized hyperparameters for the GBM model were identified using the AIW-PSO algorithm, which effectively balanced exploration and exploitation by adaptively adjusting inertia weights. Model selection was further refined using information criteria, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), ensuring that the best-performing model was chosen based on both predictive accuracy and model complexity. The optimized GBM model achieved a test accuracy of 94%, demonstrating superior performance compared to traditional machine learning models. The study underscores the importance of hyperparameter tuning through metaheuristic algorithms and highlights the potential of AIW-PSO in enhancing model performance for clinical prediction tasks. These findings have significant implications for clinical decision-making, offering a reliable and interpretable tool for predicting patient outcomes in heart failure management.

关键词： Cardiovascular diseases Predictive models Prediction algorithms machine learning algorithms Accuracy machine learning Computational modeling Mortality Adaptation models Particle swarm optimization Heart failure survival prediction machine learning algorithms hyperparameter optimization class imbalance handling AIW-PSO optimization

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Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms

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MATERIALS TESTING 2025年第1期67卷 160-182页

作者： Bozkurt, Ilyas Mus Alparslan Univ Mech Engn TR-49250 Mus Turkiye

Thanks to machine learning algorithms, the performance of composites with high energy absorption capacity can be predicted with high accuracy rates with a small number of data. The aim of this study is to experimentally and numerically determine the crushing performances of glass/epoxy composite pipe structures under compressive force and to predict their compression behavior with the help of different machine learning algorithms. In the study, the crushing performances of composite pipes (peak force (PF), peak force displacement (PFD), mean crushing force (MCF), specific energy absorption (SEA), and total inner energy (TIE)) were determined for different specimen thicknesses, specimen lengths, mesh sizes, numbers of integration points, diameters (D), and compression directions (axial and radial). Additionally, the maximum strength values of composite pipes under force were estimated with the help of Linear Regression (LR), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) machine learning algorithms. The data taken from the ANN algorithm were found to be more reliable in estimating the PF and TIE values, with an accuracy rate of 92%. When determining the MCF value, it was found that the data obtained from the LR algorithm was more reliable than other algorithms, with an accuracy rate of 80%.

关键词： composite pipes compression test machine learning algorithms progressive damage analysis finite element method

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Forecasting of electricity consumption in Pakistan based on integrating machine learning algorithms and Monte Carlo simulation

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ELECTRICAL ENGINEERING 2025年 1-21页

作者： Nazir, Muhammad Umair Li, Jinchao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

The quality of electricity consumption forecasting is crucial for the planning of Pakistan's power system. In order to improve the reliability of prediction results, this paper proposes a combined prediction model based on long short-term memory and Monte Carlo simulation. Firstly, the factors influencing Pakistan's electricity consumption were analyzed, such as economic growth rate, population size, and industrial development. Second, a deep-learning technique based on a long short-term memory (LSTM) neural network machine-learning model to forecast electricity consumption in Pakistan. Then, the probabilistic analysis of Monte Carlo with a 95% confidence interval forecasts the future consumption of electricity. At last, the historical data of Pakistan's GDP, Population, industry efficient and electricity consumption are used to testified the efficient, operative, consistent, and robust of the proposed model. The research of this paper is helpful to policymakers in Pakistan as it significantly contributes to more effective electricity management policies, energy supply industry behavioral changes, and reduced energy consumption.

关键词： Electricity consumption forecasting machine learning algorithms Monte Carlo simulation Pakistan

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