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Correlation Analysis Between Student Psychological State and Grades Based on data mining algorithms

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 49-58页

作者： Zeng Daoyan Chen Disi Sichuan Huaxin Modern Vocat Coll Chengdu 610000 Sichuan Peoples R China

As society has evolved and educational reform has become more profound, the psychological state and academic performance of vocational college students have become the focus of attention for educators. This study aims to construct a correlation model between the positive psychological state and academic performance of vocational college students based on data mining algorithms to offer a conceptual foundation and practical guidance for the optimization of vocational education. The relationship between positive psychological state and academic performance was analyzed through a literature review, as well as the application of data mining algorithms in the field of education. A certain amount of data on vocational college students was collected using questionnaire surveys and empirical research methods, including their basic information, positive psychological status indicators, and academic performance data. Subsequently, data mining algorithms were used to preprocess and analyze the collected data, and a correlation model between the positive psychological state and academic performance of vocational college students was constructed. Finally, through validation and evaluation of the model, it was found that there is a significant positive correlation between positive psychological state and academic performance, and the model has high predictive accuracy. The study's results suggest that the positive psychological state of vocational college students has a significant impact on their academic performance. Educators should consider students' mental health and take effective measures to enhance their positive psychological state, thereby improving their academic performance. This study provides a new research perspective and method for the field of vocational education, which helps to promote the development and reform of vocational education.

关键词： data mining algorithms vocational students positive psychological state academic performance correlation model

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Use of data mining algorithms to predict body weight of Dorper sheep Breed in two selected villages of Lepelle-Nkumpi local municipality in Limpopo province, South Africa

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SMART AGRICULTURAL TECHNOLOGY 2024年 8卷

作者： Phaladi, Abigail Mmapaseka Tyasi, Thobela Louis Tada, Obert Mogashoa, Stanley Univ Limpopo Dept Agr Econ & Anim Prod Private Bag X1106 ZA-0727 Sovenga South Africa Gauteng Dept Agr Rural Dev & Environm 56 Eloff St ZA-2000 Johannesburg South Africa

Sheep farmers in village areas find it difficult to estimate body weight without the weighing tools. The best fitted model for estimation of sheep body weight is not conclusive. Classification and regression tree (CART), Chisquare automatic interaction detection (CHAID), Exhaustive chi-square automatic interaction detection (ExCHAID) and Multivariate adaptive regression spline (MARS) were used to predict body weight (BW) of 306 Dorper sheep (female = 244 and male = 62) aged 1 to 2 years from linear body measurement traits such as rump height (RH), rump length (RL), heart girth (HG), withers height (WH), and body length (BL). Goodness of fit criteria 's such as Pearson 's correlation coefficients (r), coefficient of determination (R 2 ), Akaike 's information criterion AIC and adjusted R 2 (Adj.R 2 ) were computed for data mining algorithms performance assessment. The results indicated that MARS was the best data mining algorithm with r of 0.979, R 2 of 0.958, Adj.R 2 of 0.957, RMSE of 2.503 and AIC of 422.25. The MARS model provided the most appropriate predictive capability in the prediction of body weight for Dorper sheep with HG, WH, RH, BL, and sex (male) as the important traits. As a result, MARS data mining algorithm might be used to identify factors that might be vital to improve body weight of Dorper sheep for breeding programs.

关键词： data mining algorithms Body weight Linear body measurement traits Heart girth Goodness of fit

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Enhancing data-driven modeling of fluoride concentration using new data mining algorithms

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ENVIRONMENTAL EARTH SCIENCES 2022年第3期81卷 1-13页

作者： Gupta, Praveen Kumar Maiti, Saumen IIT ISM Dept Appl Geophys Dhanbad 826004 Bihar India

Groundwater is an essential constituent of drinking water in hard rock areas and hence it requires the analysis of contaminant resources. Fluoride contamination with large spatial variation in the part of Sindhudurg district is reported. The present study focuses on the development of data-driven modeling of fluoride concentration using on-site measurement of physicochemical parameters. In this configuration, six machine learning(ML) architectures, namely data mining algorithms were explored including novel algorithms Gaussian process (GP) and long short term memory (LSTM). The results were compared with support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and multi-layer perceptron (MLP) as a benchmark to test the robustness of the modeling process. In total 225 water samples from different dug-wells/bore- wells were obtained from the area (latitude:15.37-16.40 degree, longitude:73.19-74.18 degree) in the period of 2009-2016. Two subsets of data were divided with 80% data in training and 20% in testing. Different 9 physicochemical parameters pH, EC, TDS, Ca2+, Mg2+, Na+, Cl-, HCO3-, SO42- were used in the modeling of fluoride (F-). In this context logarithmic transformation of raw data was employed to improve the correlation between input and target and therefore to enhance the modeling accuracy. Different quantitative and qualitative (visual) measures were taken to establish the prediction power of models. Results revealed that GP outperform all other models in fluoride prediction followed by LSTM, SVM, MLP, RF, and ELM, respectively. Results also revealed that the model's performance depends on model structure and data accuracy.

关键词： Fluoride data transformation data mining algorithms Gaussian process

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A Financial Decision Support Optimization System Based on Artificial Intelligence and data mining algorithms 3

A Financial Decision Support Optimization System Based on Ar...

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3rd IEEE International Conference on Electrical Engineering, Big data and algorithms, EEBDA 2024

作者： Cao, Natalie National University of Singapore 117597 Singapore

ISBN: (纸本)9798350380989

Effective financial decision-making is crucial for organizational growth, yet it is often impeded by limitations in information availability and subjective biases. While traditional computer-based financial decision support systems (FDSS) have marginally enhanced decision quality, they still need further improvement with data inaccuracies, limited analytical capabilities, and slow response times. This paper examines the potential of advanced Artificial Intelligence (AI) and data mining algorithms to revolutionize FDSS. We critically analyze the shortcomings of current FDSS and introduce an innovative approach incorporating AI-driven data mining into electronic financial decision support frameworks. This integration aims to create more sophisticated, agile, and intelligent tools, addressing the evolving demands of financial decision-makers. Our study highlights the transformative impact of AI and data mining on FDSS and sets the stage for future advancements in intelligent financial decision-making. © 2024 IEEE.

关键词： artificial intelligence data mining algorithms financial optimization systems

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Impact of Cloud Computing on Agricultural Advancement using data mining algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud Computing data mining algorithms Ensemble Learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement Learning Resource Optimization Sustainable Practices

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data mining algorithms in Healthcare: An Extensive Review 5

Data Mining Algorithms in Healthcare: An Extensive Review

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Jain, Richa Devendran, V Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9781665426428

The rapid growth of data science in medicine has been fueled by the digitalization of the medical services, which has resulted in a flood of clinical huge data. The information gathered from this flood of data should be organized in such a way that it can provide better healthcare insights. The efficiency and effectiveness of the medical care systems can be improved by data mining algorithms by allowing them to use data more effectively. data mining can be beneficial to the health-care industry in many perspectives for instance, predictive medicine, customer relationship management, fraud and also abuse discovery, healthcare monitoring, and also keeping track of the efficiency of certain therapies, etc. This paper has presented a comparison of various data mining algorithms and also highlights applications and challenges of data mining in healthcare.

关键词： data mining Healthcare data mining algorithms

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A Review on Lung Cancer Diagnosis Using data mining algorithms

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CURRENT MEDICAL IMAGING 2021年第1期17卷 16-26页

作者： Heydari, Farzad Rafsanjani, Marjan Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman *** Iran

Due to the serious consequences of lung cancer, medical associations use computer-aided diagnostic procedures to diagnose this disease more accurately. Despite the damaging effects of lung cancer on the body, the lifetime of cancer patients can be extended by early diagnosis. data mining techniques are practical in diagnosing lung cancer in its first stages. This paper surveys a number of leading data mining-based cancer diagnosis approaches. Moreover, this review draws a comparison between data mining approaches in terms of selection criteria and presents the advantages and disadvantages of each method.

关键词： Lung cancer machine learning data mining algorithms detection accuracy dignosis MRI

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Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第2期195卷 319-319页

作者： Masoudi, Reyhaneh Mousavi, Seyed Roohollah Rahimabadi, Pouyan Dehghan Panahi, Mehdi Rahmani, Asghar Univ Tehran Tehran Iran Univ Zanjan Fac Agr Water Engn Dept Zanjan Iran

This study aims to compare three popular machine learning (ML) algorithms including random forest (RF), boosting regression tree (BRT), and multinomial logistic regression (MnLR) for spatial prediction of groundwater quality classes and mapping it for salinity hazard. Three hundred eighty-six groundwater samples were collected from an agriculturally intensive area in Fars Province, Iran, and nine hydro-chemical parameters were defined and interpreted. Variance inflation factor and Pearson's correlations were used to check collinearity between variables. Thereinafter, the performance of ML models was evaluated by statistical indices, namely, overall accuracy (OA) and Kappa index obtained from the confusion matrix. The results showed that the RF model was more accurate than other models with the slight difference. Moreover, the analysis of relative importance also indicated that sodium adsorption ratio (SAR) and pH have the most impact parameters in explaining groundwater quality classes, respectively. In this research, applied ML algorithms along with the hydro-chemical parameters affecting the quality of ground water can lead to produce spatial distribution maps with high accuracy for managing irrigation practice.

关键词： Groundwater quality Hydro-chemical Random forest Boosting regression tree Multinomial logistic regression data mining algorithms

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Association of antiviral drugs and their possible mechanisms with DRESS syndrome using data mining algorithms

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JOURNAL OF MEDICAL VIROLOGY 2023年第3期95卷 e28671-e28671页

作者： Sharma, Akash Roy, Sweta Sharma, Ruchika Kumar, Anoop Delhi Pharmaceut Sci & Res Univ DPSRU Dept Clin Res New Delhi India Delhi Pharmaceut Sci & Res Univ DPSRU Dept Pharmacol New Delhi India Delhi Pharmaceut Sci & Res Univ DPSRU Ctr Precis Med & Pharm New Delhi India Delhi Pharmaceut Sci & Res Univ DPSRU Dept Pharmacol Dept Clin Res New Delhi 110017 India

Antiviral drugs are not known for drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome. The current study aims is to find out the association of antiviral drugs and their possible mechanism with DRESS. data mining algorithms such as proportional reporting ratio that is, PRR (>= 2) with associated chi(2) value (>4), reporting odds ratio that is, ROR (>= 2) with 95% confidence interval and case count (>= 3) were calculated to identify a possible signal. Further, molecular docking studies were conducted to check the interaction of selected antiviral drugs with possible targets. The potential signal of DRESS was found to be associated with abacavir, acyclovir, ganciclovir, lamivudine, lopinavir, nevirapine, ribavirin, ritonavir, and zidovudine among all selected antiviral drugs. Further, subgroup analysis has also shown a potential signal in different age groups and gender. The sensitivity analysis results have shown a decrease in the strength of the signal, however, there was no significant impact on the outcome except for acyclovir. The docking results have indicated the possible involvement of human leukocyte antigen (HLA)*B1502 and HLA*B5801. The positive signal of DRESS was found with selected antiviral drugs except for acyclovir.

关键词： antiviral drugs data mining algorithms docking study DRESS

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Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID)

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JOURNAL OF CLEANER PRODUCTION 2020年 266卷

作者： Farzin, Saeed Chianeh, Farideh Nabizadeh Anaraki, Mahdi Valikhan Mahmoudian, Fatemeh Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Semnan Univ Dept Appl Chem Fac Chem Semnan Iran

The contamination of waters by persistent organic pollutants, especially pharmaceutical contaminants, is one of the concerns all over the world. To date, among the treatment methods, the efficient EAOPs method have shown a high ability to treat this type of pollutant. However, conducting adequate tests for taking into account almost all possible conditions to predict the amount of pollutant removal in different conditions is still a challenge. On the other hand, achieving this aim requires a lot of cost and time. The superiority of data mining based methods over conventional mathematical methods have made these methods a good solution to solve this problem. Hence, in present study a model by employing data mining algorithms includes Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree, least-square support vector machine (LSSVM) and hybrid of LSSVM and firefly optimization algorithm (FFA), scatter interpolation method, and multi-criteria decision making, namely DID, is presented for modeling of drugs removal. For this purpose, four different inputs include current density, electrolyte concentration, pH, and electrolysis time are used for electrochemical removal of Ciprofloxacin (CIP) as a model pollutant. Subsequently, Scatter interpolation method is used for generating enough data for more accurate modeling and more reliable results. In the final part of the survey, the TOPSIS method under six scenario, is employed for ranking of algorithms by considering accuracy and time criteria. In defined scenarios for TOPSIS, six different weights are considered for time criteria as well as the weights of accuracy are considered as equal in each scenario. Also, the sum of scores of each algorithm in all scenarios is used for final decision. The finding results by TOPSIS for original data showed the superiority of LSSVM_FFA. After generating new data, the M5 and the ANFIS have better results in 0.25 time weight. However, by decreasing t

关键词： Drug removal modeling data mining algorithms Scatter interpolation Multi criteria decision analysis Ciprofloxacin

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