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检索条件"主题词=prediction algorithms"

共 30745 条记录，以下是41-50 订阅

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Benchmarking Explicit Rating prediction algorithms for Cosmetic Products

Benchmarking Explicit Rating Prediction Algorithms for Cosme...

引用

International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Raditya Nurfadillah Fariz Darari Radityo Eko Prasojo Yasmin Amalia Faculty of Computer Science Universitas Indonesia Depok Indonesia Tokopedia-UI AI Center of Excellence Depok Indonesia Kata.ai Jakarta Indonesia

ISBN: (数字)9781728184067

ISBN: (纸本)9781728184074

Recommendation systems have become a staple feature for any e-commerce sites. The ability to predict whether a customer likes an unseen product forms the very foundation of a recommendation system. In this paper, we concern the issue of explicit rating prediction over cosmetic products. Given a dataset of cosmetic product ratings, we analyze the characteristics of the dataset and implement a wide range of algorithms, such as KNN and matrix factorization, to predict such ratings. We evaluate the performance of these algorithms using MAE and RMSE measures, and discuss factors that may contribute to their performance results. Our experiments have shown that the SVD++ technique performs the best among all with an MAE of 0.7699 and an RMSE of 0.9696. We hope that our paper can shed new light on the selection of explicit rating prediction algorithms not only in the domain of beauty products, but also in wider scenarios.

关键词： prediction algorithms Measurement Recommender systems Singular value decomposition Seminars Privacy Matrix decomposition

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Hyperparameter Optimization and Comparison of Student Performance prediction algorithms

Hyperparameter Optimization and Comparison of Student Perfor...

引用

International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Ibrahim A Alharbi Ali Jaber Almalki Cliff C. Zou dept. Computer Science University of Central Florida Orlando USA

ISBN: (纸本)9781665458429

Educational Data Mining is the process of extracting information from datasets with educational relevance. This process of information extraction can be extremely insightful and useful for policy decision making but also small scale interventions. In our research, we optimize model configurations and hyperparameters of student performance prediction pipelines. Our models target to predict student performance based on data gathered in Portuguese schools pertaining to the subjects of Mathematics and Portuguese. The target variable is either cast in two or five bins and we train separate models for each of the tasks. We search model configurations amongst three different feature selection algorithms and 7 different classifiers implemented in scikit-learn. Our search spans 2000 iterations overall different experiment setup permutations. We successfully develop novel model configurations that perform exceptionally well. Furthermore, we set an important precedent showing the utility of hyperparameter selection and model search for Educational Data Mining.

关键词： Scientific computing Pipelines Predictive models prediction algorithms Mathematical models Data models Data mining

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Analyzing and Preventing Bias in Text-based Personal Trait prediction algorithms

Analyzing and Preventing Bias in Text-based Personal Trait P...

引用

International Conference on Tools with Artificial Intelligence

作者： Isil Doga Yakut Kilic Shimei Pan University of Maryland Baltimore County

ISBN: (纸本)9781509044603

Personality prediction based on textual data is one topic gaining attention recently for its potential in large-scale personalized applications such as social media-based marketing and political campaigning. However, when applying this technology in real-world applications, users often encounter situations in which the personality traits derived from different sources (e.g., social media posts versus emails) are inconsistent. Varying results for the same individual renders the tool ineffective and hard to trust. This paper demonstrates the impact of domain bias in automated text-based personality prediction, and proposes a novel method to correct domain bias. The proposed approach is generic since it requires neither retraining the system using examples from an application domain, nor any knowledge of the original training data used by a personal trait analysis tool. We conduct comprehensive experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy (e.g., a 20-30% relative error reduction) with the proposed method.

关键词： personality trait prediction algorithms electronic mail Bias Bias Bias Statistical Biases bias(electronics) Personality Domain train data Campaigning

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Power quality prediction algorithms and analysis

Power quality prediction algorithms and analysis

引用

International Conference on System Science and Engineering (ICSSE)

作者： Gao Xin Dai Shengkui College of Electrical and Information Engineering Anhui University of Science And Technology HuaiNan China Guotou Xinji Energy Co. HuaiNan China

By using the gray prediction adaptive system whose parameter is variable and structure is fixed, this paper revises data sequence of the distortion in the power by using improved model. This method also increases pred... 详细信息

关键词： Predictive models Data models Mathematical model prediction algorithms Adaptation models Accuracy Algorithm design and analysis

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Bolstering Heuristics for Statistical Validation of prediction algorithms

Bolstering Heuristics for Statistical Validation of Predicti...

引用

International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Alex F. Mendelson Maria A. Zuluaga Brian F. Hutton Sebastien Ourselin Translational Imaging Group University College London London UK Institute of Nuclear Medicine University College London London UK Centre for Medical Radiation Physics University of Wollongong NSW Australia Dementia Research Centre University College London UK

Machine learning research in image-based computer aided diagnosis is a field characterised by rich models and relatively small datasets. In this regime, conventional statistical tests for cross validation results may no longer be optimal due to variability in training set quality. We present a principle by which existing statistical tests can be conservatively extended to make use of arbitrary numbers of repeated experiments. We apply this to the problems of interval estimation and pair wise comparison for the accuracy of classification algorithms, and test the resulting procedures on real and synthetic classification tasks. The interval coverages in the synthetic task are notably improved, and the comparison has both increased power and reduced type I error. Experiments in the ADNI dataset show that the low replicability of split-half based tests can be dramatically improved.

关键词： Accuracy Imaging Training Estimation Testing Support vector machines prediction algorithms

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Reduction of call drop-outs during handoffs using efficient signal strength prediction algorithms for personal communication systems

Reduction of call drop-outs during handoffs using efficient ...

引用

Proceedings of GLOBECOM '95

作者： G.N. Senarath D. Everitt School of Electrical Engineering and Computer Science University of Melbourne Parkville VIC Australia

In cellular mobile networks, the handoff decision is usually taken based on the past signal and interference level measurements taken at regular time intervals. Quality degradation can be minimized if a mobile can always be handed over to the base station which would provide highest quality during the next measurement cycle. For this purpose, a good prediction algorithm is needed to predict the condition of the signals received from various base stations during the next measurement cycle. One of the major objectives of this paper is to discuss whether a considerable quality improvement can be obtained using prediction. A lower-bound for the drop-out performance is obtained using a hypothetical scheme which assumes the best possible prediction. The performance of several prediction algorithms are considered under various shadowing conditions. It is found that the performance of prediction schemes greatly depends on the speed of the mobile and the shadowing correlation environment.

关键词： prediction algorithms Cellular networks Degradation Base stations Shadow mapping Fading Performance analysis

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Effects of adaptive prediction algorithms on adaptive QAM in flat Rayleigh fading channels

Effects of adaptive prediction algorithms on adaptive QAM in...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Yung-Fang Chen Hsin-Shieh Tsai Jen-Wei Peng Graduate Institute of Connnunication Engineering National Central University Chungli Taiwan

ISBN: (纸本)0780378229

The long-range prediction (LRP) algorithm was investigated by Duel-Hallen et al., (2000) such that we can predict future channel state information for more than ten milliseconds ahead. However, its adaptive implementations to avoid the matrix inversion have not been investigated in detail. This paper presents the performance comparison among the various adaptive prediction algorithms for the LRP purpose. The adaptive QAM transmission schemes with utilizing the algorithms are thus proposed for the use in the FDD-based adaptive transmission under the scenario of flat Rayleigh fading channels. The scheme is also built with the pilot symbol assist modulation and the speed zone detection techniques. The adaptive modulation schemes by utilizing the speed zone detection lead in performance improvement in the selection of the optimal prediction order under the different mobile speeds. The efficacies of the schemes are presented through the simulation study.

关键词： prediction algorithms Quadrature amplitude modulation Fading Programmable control Adaptive control Frequency Channel state information Transmitters Feedback control Predictive models

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Enhanced Fake News Detection using prediction algorithms

Enhanced Fake News Detection using Prediction Algorithms

引用

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： D. Rosy Salomi Victoria Gowtham G K. Arulan Sabari Ashwin Roberts R Gokul S Department of CSE St. Joseph’s College of Engineering Chennai India Department of CS St. Joseph’s College of Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The advent of web-based media has provided a platform for individuals to express their opinions freely, facilitating easier communication compared to the past. This convenience comes with the risk of deliberate dissemination of misinformation. The ready access to a plethora of online information sources exacerbates the problem, as people may be exposed to and potentially believes false information. It is crucial for us to be aware of and flag such content on social media platforms. Given the rapid generation of information through online media, distinguishing between authentic news and fabrications is challenging without knowing the news source. The spread of fake news can have serious consequences, emphasizing the need for measures to reduce and differentiate between genuine and false messages. Term Frequency measures how often a word appears in a document, indicating its relevance. Inverse Document Frequency assesses the significance of a term in the entire corpus, helping filter out irrelevant words. An examination of spam detection is conducted by employing diverse classifiers, including BERT and RoBERTa to evaluate accuracy and furnish comparative findings.

关键词： Fabrication Technological innovation Social networking (online) Named entity recognition Predictive models Filtering algorithms prediction algorithms Frequency measurement Classification algorithms Fake news

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Accurate Load prediction algorithms Assisted with Machine Learning for Network Traffic

Accurate Load Prediction Algorithms Assisted with Machine Le...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Yin Gao Man Zhang Jiajun Chen Jiren Han Dapeng Li Ruitao Qiu Wireless Product R&D Institute ZTE Corporation Shanghai China ZTE Corporation Tianjin China

With the increasingly higher demand on radio access networks, problems such as serious energy consumption and network load imbalance have aroused, catching more attention from the network operators. To solve these problems, load prediction has been considered as one possible solution. This paper investigates two load prediction models to predict the traffic load in cells, in order to improve the energy saving strategies. One of the proposed algorithm is a linear ensemble model which is composed of three sub-models, to predict the traffic load in terms of time, space and historical pattern respectively. This model is named as the ensemble model. Different methods such as time series analysis, linear regression and regression tree are applied to the sub-models, after which adjusted weights are calculated and allocated to each of the three sub-models to create the ensemble model. The second model investigated in this paper is convolutional neural network (CNN) based, which utilizes the residual learning network (ResNet) structure to train the collected data and at the same time improves the calculation efficiency. This model is thus referred as ResNet model in this paper. The collected data of traffic load in Milan area is put into training and verification by the proposed models. The performance of proposed methods is evaluated. The result shows that ensemble model provides a higher prediction accuracy than the other base line models and the ResNet model improves the efficiency of calculation.

关键词： Training Wireless communication Machine learning algorithms Telecommunication traffic Predictive models Tools prediction algorithms

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Research on Movie Rating prediction algorithms

Research on Movie Rating Prediction Algorithms

引用

IEEE International Conference on Big Data Analysis (ICBDA)

作者： Xiaoyue Li Haonan Zhao Zhuo Wang Zhezhou Yu College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

In the era of data explosion of movie and video websites, “informahon overload” and “information labyrinth” have brought serious troubles, but movie recommender systems can efficiently solve such problems. In order to solve these problems, we propose the RF that uses the users' activity and rating to select suitable experimental data and proved this method can efficiently reduce RMSE and MAE of various recommendation algorithms. On this basis, we propose the MCBF-SVD, which is a movie rating prediction algorithm based on explicit data and implicit data from movie datasets to predict the future ratings of movies from users with a certain degree of activity. Firstly, the MCBF-SVD uses weighting factors to discuss the impact of movie categories on predicting future rating behavior of users, and also improves the filtering method based on movie categories. Finally, the MCBF is combined with SVD algorithm which has good performance in CF. Compared with several existing algorithms, our MCBF-SVD greatly enhanced the accuracy of rating prediction, and improved the scalability and efficiency of personalized recommender systems.

关键词： Motion pictures prediction algorithms Radio frequency Recommender systems Matrix decomposition Collaboration

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