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Creation of data mining algorithms as Functional Expression for Parallel and Distributed Execution 13th

13th International Conference on Parallel Computing Technologies (PaCT)

作者： Kholod, Ivan Petukhov, Ilya St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9783319219097;9783319219080

The article describes extension of lambda-calculation for creation of parallel data mining algorithms. The proposed approach uses presentation of the algorithm as a consequence of pure functions with unified interfaces. For parallel execution we use special function that allows to change a structure of the algorithm and to implement various strategies for processing of data set and model.

关键词： Parallel algorithms data mining Parallel data mining Distributed data mining data mining algorithms

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Framework for Multi Threads Execution of data mining algorithms

Framework for Multi Threads Execution of Data Mining Algorit...

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IEEE North West Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2015 ElConRusNW)

作者： Kholod, Ivan St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

ISBN: (纸本)9781479973064

the present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must have unified interfaces and implement pure functions. The framework allows create new data mining algorithms from existing blocks and improves the existing algorithms by optimizing single blocks or the whole structure of the algorithms. This becomes possible due to a number of important properties such as thread-safety inherent in pure functions and hence functional blocks.

关键词： data mining parallel data mining data mining algorithms distributed data mining parallel algorithms

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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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Research on Optimal Traditional Chinese Medicine Treatment of Knee Ostarthritis with data mining algorithms 2012

Research on Optimal Traditional Chinese Medicine Treatment o...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Guo, Da Li, Jian Zhang, Gang Lu, Weixiang Xu, Shaojian Liu, Jun Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Guangdong Peoples R China Guangdong Provincal Hosp Integrated Tradit Chines Guangzhou Peoples R China GuangDong Univ Technol Fac Automat Guangzhou Peoples R China

ISBN: (纸本)9781467327466;9781467327459

At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.

关键词： data mining algorithms optimal treatment knee ostarthritis Traditional Chinese Medicine

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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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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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Exploring and validating data mining algorithms for use in data ascription

Exploring and validating data mining algorithms for use in d...

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作者： Daniel P. Huynh Naval Postgraduate School

学位级别：硕士

Digital forensics is a growing and important field of research for current intelligence, law en- forcement, and military organizations today. As more information is stored in digital form, the need and ability to analyze and process this information for relevant evidence has grown in complexity. Today analysis is reliant upon trained experts. This, compounded with the sheer volume of evidence obtained from the field, means that analysis frequently takes too long. Cur- rent forensic tools focus on decoding and visualization and not data reduction or correlation. This thesis fills an important void. The first goal is to determine whether it is possible to use file metadata accurately to ascribe ownership of files based upon a hard drive with multiple users. The second is to explore and validate existing algorithms that may support and aid data ascrip- tion. The last goal of this work is to compare and measure the accuracy of these algorithms. This work facilitates further research into developing an automated analysis and reporting framework for media exploitation in computer forensics.

关键词： data mining algorithms metadata file ascription data carving multi-user hard drives

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Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 114卷 103474-000页

作者： Hadi, Wa'el El-Khalili, Nuha AlNashashibi, May Issa, Ghassan AlBanna, Abed Alkarim Univ Petra Comp Informat Syst Amman Jordan Univ Petra Software Engn Amman Jordan Univ Petra Comp Sci Amman Jordan

Driving daily through traffic congestion has been recognised as a major cause of stress. High levels of stress while driving negatively impact the driver's decisions which could potentially lead to accidents and other long-term health hazards. Accordingly, there is a great need to determine stress levels for drivers based on measuring and predicting the major causes (features or classes) that increase stress levels. In this paper, the problem of predicting automobile drivers' stress levels, as experienced during actual driving, is investigated through the application of five different data mining algorithms, namely K-Nearest Neighbour (KNN), Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). An experiment was conducted on 14 drivers taking various routes in Amman Jordan, with a wearable biomedical device attached to the driver to instantly collect physiological data. The collected data (dataset) is grouped into two different categories, namely 'Yes' to signify the presence of stress and 'No' to signify the absence of stress. In order to efficiently apply data mining algorithms to the data set, oversampling was used to avoid the negative effect of driver samples with a lesser class on the prediction of stress. The findings are evaluated in relation to stress prediction and accordingly contrasted alongside standard reference approaches that do not consider oversampling and/or feature selection using the Friedman rank test. The proposed approach, in combination with RF, was seen to surpass any others in terms of accuracy, AUC, specificity, and sensitivity. The accuracy, AUC, specificity, and sensitivity rates produced by RF utilising our proposed approach were 98.92%, 99.91%, 98.46%, and 99.36%, respectively.

关键词： data mining algorithms Stress prediction Feature selection Oversampling

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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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