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IEEE Frontiers in Education Conference (FIE)

作者： Motschnig, Renate Guever, Seyhan Univ Vienna Fac Comp Sci CSLEARN Educ Technol Vienna Austria

ISBN: (纸本)9781509059201

Technical solution skills - though important - are not enough for effective, multicultural teamwork. Despite broad consensus on the vital function of communication- and social skills, they tend to be underrepresented in the training of software-engineers and project managers. This is partly because such skills are less explicit than technical methods, tools, and artefacts. To address this deficiency, an openly accessible web-based communication model for multicultural teams (comMCT) was developed. It consists of four modules addressing structural, functional, organizational, and interpersonal/behavioral aspects of multicultural communication. After presenting the empirically validated comMCT model, two scenarios of introducing comMCT into two academic courses within the computer science curriculum at the University of Vienna are discussed, along with the students' reactions and an online questionnaire capturing students' feedback on comMCT. In a nutshell, thoughtful, person-centered application of the model is expected to enhance learners' knowledge, practice, and reflection on communication issues and contribute to more effective and satisfied multicultural teams. Importantly, instructors as well as life-long learning program designers will find inspiration on including comMCT into their courses.

关键词： multicultural teams communiction skills web-based model person-centered learning experiential learning project-management education

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Correction of article errors in machine translation using web-based model

Correction of article errors in machine translation using We...

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International Conference on Natural Language Processing and Knowledge Engineering

作者： Peng, J Araki, K Hokkaido Univ Language Media Lab Kita Ku Sapporo Hokkaido Japan

ISBN: (纸本)0780393619

In this paper, an approach is proposed for correcting article errors in English translation results in order to improve the performance of a NIT system. We check the article and the singular/plural form of the headword in a NP at the same time. This is different from most of early researches in which only articles are considered. Our correcting algorithm is based on simple, viable n-gram model whose parameters can be obtained using the WWW search engine Google. Using much less features than those used in the early researches, we experimentally showed that our approach could perform the promising results with a precision of 86.2% on all classes of article errors.

关键词： Internet language translation natural languages English translation web-based model article error correction machine translation n-gram model

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Development of web-based combine harvester custom-hiring model for rice-wheat cropping system

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CURRENT SCIENCE 2019年第1期116卷 108-111页

作者： Parray, Roaf Ahmad Mani, Indra Kumar, Adarsh Khura, Tapan Kumar ICAR Indian Agr Res Inst New Delhi 110012 India

A web-based custom hiring model was developed to help farmers and custom-hiring service providers take decisions regarding owning/custom hiring of combine harvester for rice-wheat cropping system. It also gives the break-even acreage for owning a combine harvester along with various cost economics. The model was evaluated for two situations: situation I with own area of 100 acres and custom-hiring catchment area of 160 acres combined under rice and wheat, and situation II with own area and custom-hiring catchment area being 60 and 276 acres respectively. For situation I the model guided the user to opt for custom-hiring, while for situation II it gave a decision to own a combine harvester.

关键词： Custom hiring combine harvester rice-wheat cropping system web-based model

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Improved Prediction of Significant Prostate Cancer Following Repeated Prostate Biopsy by the Random Forest Classifier

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2023年第1期43卷 83-92页

作者： Stojadinovic, Miroslav Milicevic, Bogdan Jankovic, Slobodan Clin Ctr Kragujevac Dept Urol Clin Urol & Nephrol ZmajJovina 30 Kragujevac 34000 Serbia Univ Kragujevac Fac Med Sci Kragujevac Serbia BioIRCKragujevac Bioengn Res & Dev Ctr Kragujevac Serbia Univ Kragujevac Fac Engn Kragujevac Serbia Univ Kragujevac Fac Med Sci Pharmacol & Toxicol Dept Kragujevac Serbia

Purpose Numerous strategies and diagnostic tests were proposed in patients suspected of clinically significant (cs) prostate cancer (PCa) after an initial negative prostate biopsy. The study aimed to create a Random Forest (RF) classifier for predicting the probability of csPCa in specimens taken by the repeated systematic prostate biopsy (SBx), and to determine its diagnostic accuracy and clinical utility. Methods This retrospective, single-center study included patients who underwent repeated SBx due to clinical suspicion of cancer. Data on patient age, serum prostate-specific antigen (PSA) levels, prostate volume, digital rectal examination, first-degree family history, and histology findings from the SBx were collected for all patients. The area under the curve (AUC), and secondary metrics of clinical prediction models were used to assess their discriminative abilities. Clinical usefulness of final model was tested by the decision curve analysis (DCA). The explainability and website placement of the ML model were also performed. Results In total, 204 patients were eligible for analysis. The csPCa was detected in 26% (n = 53) patients. The AUC, accuracy, sensitivity, and specificity for detection of csPCa were 0.94, 0.91, 0.84, and 0.98, respectively. With an optimal threshold of 0.8, about 34% of unnecessary biopsies would be avoided, but correct diagnosis would be delayed in 4.4% csPC cases. PSA level, prostate volume, and age were the top-ranked variables in the RF model. Conclusion The RF classifier predicts csPCa with good accuracy and may help urologists when deciding whether the repeated biopsy is necessary to avoid being too invasive.

关键词： Prostate cancer Repeated prostate biopsy Risk calculator Random Forest algorithm web-based model

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Enhanced PSA Density Prediction Accuracy When based on Machine Learning

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2023年第3期43卷 249-257页

作者： Stojadinovic, Miroslav Milicevic, Bogdan Jankovic, Slobodan Clin Ctr Kragujevac Clin Urol & Nephrol Dept Urol Kragujevac Serbia Univ Kragujevac Fac Med Sci Kragujevac Serbia Bioengn Res & Dev Ctr BioIRC Kragujevac Kragujevac Serbia Univ Kragujevac Fac Engn Kragujevac Serbia Univ Kragujevac Fac Med Sci Pharmacol & Toxicol Dept Kragujevac Serbia Clin Ctr Kragujevac Dept Urol Clin Urol & Nephrol Zmaj Jovina 30 Kragujevac 34000 Serbia

PurposeThe prostate-specific antigen (PSA) density (PSAD) in prostate cancer (PCa) detection has limited applicability and is probably caused by moderate accuracy. The purpose of this study was to create a machine learning (ML) PSAD model that incorporates PSAD predictors for forecasting clinically significant (cs) prostate cancer (PCa) probability and compare its performance to that of the traditional *** and prostate volume (PV) were retrieved from the 725 patients that were subjected to prostate biopsy. After resampling and splitting data, we used the training set to create seven ML algorithms. We chose the RF model that was the most accurate. The area under the curve (AUC) accuracy, precision, sensitivity, and specificity of PSAD and RF PSAD diagnostic performance were compared. Additionally, the ML model's explainability and its website placement were *** was found in 140 males (19.3%). The proposed novel model exhibited much higher evolution metrics than PSAD. AUC for the PSAD and RF PSAD were 0.757 and 0.942, respectively. The reliability diagram indicates that the RF model fits the data well. For the RF model, the decision curve analysis revealed a net benefit of more than 5%, and 40% subjects could avoid unnecessary biopsy. PV was the more important determinant for csPCa. PSA and PV had non-monotonic relationships and a lot of *** RF PSAD model demonstrated strong discrimination and clinical value, which could aid urologists in determining whether a prostate biopsy is required.

关键词： Prostate cancer Prostate biopsy PSA density Random forest web-based model

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Performance of the streamlined quality outcomes database web-based calculator: internal and external validation

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SPINE JOURNAL 2024年第4期24卷 662-669页

作者： Carreon, Leah Y. Nian, Hui Archer, Kristin R. Andersen, Mikkel O. Glassman, Steven D. Norton Leatherman Spine Ctr 210 East Gray StSuite 900 Louisville KY USA Ctr Spine Surg & Res Ostre Hougvej 55 DK-5500 Middelfart Region Of South Denmark Univ Southern Denmark Inst Reg Hlth Res Winslowparken 193 DK-5000 Odense Denmark Vanderbilt Univ Med Ctr Dept Biostat 1211 Med Ctr Dr Nashville TN 37232 USA Vanderbilt Univ Vanderbilt Orthopaed Inst Ctr Musculoskeletal Res Med Ctr Nashville TN 37232 USA Vanderbilt Univ Osher Ctr Integrat Hlth Dept Phys Med & Rehabil Med Ctr 2201 Childrens WaySuite 1318 Nashville TN 37212 USA

BACKGROUND CONTEXT: With an increasing number of web-based calculators designed to provide the probabilities of an individual achieving improvement after lumbar spine surgery, there is a need to determine the accuracy of these models. PURPOSE: To perform an internal and external validation study of the reduced Quality Outcomes Database web-based Calculator (QOD-Calc). PATIENT SAMPLE: Patients enrolled study-wide in Quality Outcomes Database (QOD) and patients enrolled in DaneSpine at a single institution who had elective lumbar spine surgery with baseline data to complete QOD-Calc and 12-month postoperative data. back and leg pain, EuroQOL-5D (EQ-5D). METHODS: Baseline data elements were entered into QOD-Calc to determine the probability for each patient having Any Improvement and 30% Improvement in NRS leg pain, back pain, EQ-5D and ODI. These probabilities were compared with the actual 12-month postop data for each of the QOD and DaneSpine cases. Receiver-operating characteristics analyses were performed and caliRESULTS: 24,755 QOD cases and 8,105 DaneSpine lumbar cases were included in the analysis. in the QOD cohort and moderate to acceptable ability (AUC: 0.658-0.747) to predict 30% Improvement. QOD-Calc had acceptable to exceptional ability (AUC: 0.669-0.734) to predict Any improvement and moderate to exceptional ability (AUC: 0.619-0.862) to predict 30% Improvement in the DaneSpine cohort. AUCs for the DaneSpine cohort was consistently lower that the CONCLUSION: QOD-Calc performs well in predicting outcomes in a patient population that is similar to the patients that was used to develop it. Although still acceptable, model performance was slightly worse in a distinct population, despite the fact that the sample was more homogenous. model performance may also be attributed to the low discrimination threshold, with close to 90% of cases reporting Any Improvement in outcome. Prediction models may need to be developed that are highly specific to the characteristi

关键词： Back pain External validation Internal validation Leg pain Lumbar spine surgery Oswestry Disability Index Predictive model web-based model

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Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate biopsy

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BJU INTERNATIONAL 2020年第6期126卷 694-703页

作者： Suh, Jungyo Yoo, Sangjun Park, Juhyun Cho, Sung Yong Cho, Min Chul Son, Hwancheol Jeong, Hyeon Seoul Natl Univ Hosp Hosp Med Ctr Dept Urol Seoul South Korea Seoul Natl Univ Dept Urol Coll Med Seoul South Korea Seoul Metropolitan Govt Seoul Natl Univ Boramae M Dept Urol 5 Gil 20Boramae Rd Seoul 07061 South Korea Asan Med Ctr Dept Urol Seoul South Korea Seoul Natl Univ Hosp Dept Urol Seoul South Korea

Objectives To develop and validate a risk calculator for prostate cancer (PCa) and clinically significant PCa (csPCa) using explainable artificial intelligence (XAI). Patients and Methods We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator using five-fold cross-validation with hyperparameter tuning following feature selection in the development set. The model feature importance was determined based on the Shapley value. The area under the curve (AUC) of the receiver operating characteristic curve was analysed for each validation set of the calculator. Results Approximately 1216 (32.7%) and 562 (14.8%) patients were diagnosed with PCa and csPCa. The data of 2843 patients were used for development, whereas the data of 948 patients were used as a test set. We selected the variables for each PCa and csPCa risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PCa model was 0.869 (95% confidence interval [CI] 0.844-0.893), whereas that of the csPCa model was 0.945 (95% CI 0.927-0.963). The prostate-specific antigen (PSA) level, free PSA level, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasonography, and testosterone level were found to be important parameters in the PCa model. The number of previous biopsies was not associated with the risk of csPCa, but was negatively associated with the risk of PCa. Conclusion We successfully developed and validated a decision-supporting tool using XAI for calculating the probability of PCa and csPCa prior to prostate biopsy.

关键词： machine learning explainable AI decision-supporting tool prostate cancer prediction model web-based model

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A web-HOSTED INFORMATION MANAGEMENT SYSTEM FOR PREDICTING THE BREAKDOWN OF OFF-HIGHWAY PLANT

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2006年第1期4卷 29-45页

作者： Oloke, David Edwards, David J. Wright, Bruce Love, Peter E. D. Loughborough Univ Technol Bldg & Civil Engn Dept Off Highway Plant & Equipment Res Ctr OPERC Loughborough LE11 3EJ Leics England

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming;and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web-based data capture and information management system. Specifically, the system represents integration of a web-enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average - ARIMA)time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge based Management System (KBMS) to interface with the RDBMS.

关键词： Off-highway plant plant breakdown web-based model time series database model bas

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