scholarly journals Novel SDDM Rating Prediction Models for Recommendation Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101197-101206
Author(s):  
Diao Zhou ◽  
Shengnan Hao ◽  
Haiyang Zhang ◽  
Chenxu Dai ◽  
Yongli An ◽  
...  
Keyword(s):  
Prediction Models ◽  
Recommendation Systems ◽  
Rating Prediction

scholarly journals Investigating Recommendation Algorithms for Escape Rooms

2019 ◽  
Vol 06 (04) ◽  
pp. 377-388
Author(s):  
Sagi Bazinin ◽  
Guy Shani
Keyword(s):  
Current System ◽  
Recommendation Systems ◽  
Limited Time ◽  
Empirical Comparison ◽  
Recommendation Algorithms ◽  
Puzzle Solving ◽  
Rating Prediction ◽  
A Current

An escape room is a physical puzzle solving game, where participants solve a series of riddles within a limited time to exit a locked room. Escape rooms differ in their theme, environment, and difficulty, and people hence often differ on their preferences over escape rooms. As such, recommendation systems can help people in deciding which room to visit. In this paper, we describe the properties of the escape rooms recommendation problem, with respect to other popular recommendation problems. We describe a dataset of reviews collected within a current system. We provide an empirical comparison between a set of recommendation algorithms over two problems, top-N recommendation and rating prediction. In both cases, a KNN method performed the best.

scholarly journals RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression

2018 ◽  
Vol 10 (12) ◽  
pp. 4620 ◽  
Author(s):  
Lei Wang ◽  
Qingjian Zhao ◽  
Zuomin Wen ◽  
Jiaming Qu
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Forest Fire ◽  
Prediction Models ◽  
Fire Danger ◽  
Fire Prevention ◽  
Short Term ◽  
Forest Fire Prevention ◽  
Rating Prediction ◽  
Forest Fire Danger

Forest fire prevention is important because of human communities near forests or in the wildland-urban interfaces. Short-term forest fire danger rating prediction is an effective way to provide early guidance for forest fire managers. It can therefore effectively protect the forest resources and enhance the sustainability of the forest ecosystem. However, relevant existing forest fire danger rating prediction models operate well only when applied to distinct climates and fuel types separately. There are desires for an effective methodology, which can construct a specific short-term prediction model according to an evaluation of the data from that specific region. Moreover, a suitable method for prediction model construction needs to deal with some big data related computing challenges (i.e., data diversity coupled with complexity of solution space, and the requirement of real-time forest fire prevention application) when massively observed heterogeneous parameters are available for prediction (e.g., meteorology factor, the amount of litter in the area, soil moisture, etc.). To capture the influences of multiple prediction factors on the prediction results and effectively learn from fast cumulative historical big data, artificial intelligence methods are investigated in this paper, yielding a short-term Ratings of Forest Fire Danger Prediction via Multiclass Logistic Regression (or RAFFIA) model for forest fire danger rating online prediction. Experimental evaluations conducted on a sensor-based forest fire prevention experimental station show that RAFFIA (with 98.71% precision and 0.081 root mean square error) is more effective than the Least Square Fitting Regression (LSFR) and Random Forests (RF) prediction models.

scholarly journals Evidence Of Nonstationarity In the Bond Rating Process For Newly Issued Bonds: A Note

2011 ◽  
Vol 8 (1) ◽  
pp. 124 ◽  
Author(s):  
Mai E. Iskandar
Keyword(s):  
Empirical Evidence ◽  
Prediction Models ◽  
Parameter Estimates ◽  
Lower Grade ◽  
Investment Strategies ◽  
Bond Rating ◽  
Rating Agencies ◽  
Rating Process ◽  
Rating Prediction ◽  
Grade One

This study documents nonstationarity of the bond rating process. The empirical evidence suggests that not only the parameter estimates exhibit nonstationarity but also the bond rating process itself. The source of nonstationarity is found to be externally caused and non agency-specific. Further examination leads us to stipulate that rating agencies apply stricter standards to lower grade issues than to higher grade one when the economy is in a recession. The results have implications for bond investment strategies as well as for the utilization of bond rating prediction models.

Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques

2020 ◽  
Vol 54 (2) ◽  
pp. 151-168
Author(s):  
Jinwook Choi ◽  
Yongmoo Suh ◽  
Namchul Jung
Keyword(s):  
Credit Rating ◽  
Prediction Models ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Rating Data ◽  
Content Type ◽  
Financial Variables ◽  
Corporate Credit ◽  
Rating Prediction ◽  
Corporate Credit Rating

PurposeThe purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.Design/methodology/approachThis study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.FindingsExperimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.Originality/valueThis study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

Two-stage credit rating prediction using machine learning techniques

Kybernetes ◽  
2014 ◽  
Vol 43 (7) ◽  
pp. 1098-1113 ◽  
Author(s):  
Hsu-Che Wu ◽  
Ya-Han Hu ◽  
Yen-Hao Huang
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Prediction Models ◽  
Data Preprocessing ◽  
Machine Learning Techniques ◽  
Risk Level ◽  
Two Stage ◽  
Content Type ◽  
Learning Techniques ◽  
Rating Prediction

Purpose – Credit ratings have become one of the primary references for financial institutions to assess credit risk. Conventional credit rating approaches mainly concentrated on two-class classification (i.e. good or bad credit), which lacks adequate precision to perform credit risk evaluations in practice. In addition, most of previous researches directly focussed on employing various data mining techniques, but rare studies discussed the influence of data preprocessing before classifier construction. The paper aims to discuss these issues. Design/methodology/approach – This study considers nine-class classification (i.e. nine credit risk level) to credit rating prediction. For the development of more accurate classifiers, the paper adopts two-stage analysis, which integrates multiple data preprocessing and supervised learning techniques. Specifically, the first stage applies feature selection, data clustering, and data resampling methods to preprocess the data, and then the second stage utilizes several classification techniques and classifier ensembles to construct prediction models. Findings – The results show that Bagging-DT with data resampling method achieves excellent accuracy (82.96 percent), indicating that the proposed two-stage prediction model is better than conventional one-stage models. Originality/value – Practical implication of this study can lower credit rating expenses and also allow corporations to gain credit rating information instantly.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

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