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检索条件"主题词=prediction algorithms"

共 31027 条记录，以下是401-410 订阅

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Optimizing Click-Through Rate prediction: A Model Utilizing Multi-Attention Fusion Hashing algorithms

Optimizing Click-Through Rate Prediction: A Model Utilizing ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xinge Li Fayu Wang Hongtao Chen Tianjin Key Laboratory of Intelligence Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Click-through rate prediction plays a crucial role in contemporary recommender systems, which aim to refine recommended items by predicting the click-through rate to enhance the overall recommendation effectiveness. However, existing click-through rate prediction models still do not fully leverage both long-term and short-term user behavior information. Consequently, this paper proposes a novel click-through rate prediction model, named Interest Evolutionary Network with Hash and Multiple Attention Network (IEHAN). The model categorizes user behaviors into long-term and short-term categories, employing a hash sampling method to model long-term user behaviors. By directly filtering behavioral items with the same hash signature as the target item from the entire sequence, the IEHAN model effectively models long-term user interests, thereby reducing information loss. Additionally, the model employs the multi-head attention mechanism and the GRU model with an attention update gate to integrate the relevance of the user's interest state and the target item, thereby enhancing the impact of relevant interests on interest evolution and shaping short-term user interest. Experimental results demonstrate that the IEHAN model achieves higher AUC and lower LOSS evaluation metrics on the Taobao dataset and the Books subset of the Amazon dataset, surpassing other existing models and exhibiting superior prediction accuracy.

关键词： Measurement Accuracy Attention mechanisms Filtering Predictive models Logic gates Sampling methods prediction algorithms Recommender systems Cybernetics

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A Various Approaches of Machine Learning algorithms for Kidney Disease prediction

A Various Approaches of Machine Learning Algorithms for Kidn...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： G. Gomathi Sumathy V P. Sivakamasundari R. Deepa Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur TN Department of Information Technology St. Joseph's Institute of Technology Chennai TN

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Chronic kidney disease is a serious health issue that affects people around the world. Proper diagnosis and treatment must be initiated as soon as feasible. The primary purpose of this research is to detect the existence or absence of CKD in the human body by analyzing a number of features obtained from a few medical tests. This research presents an ML-CKDP model that aims to improve dataset preprocessing for CKD classification and provide a web-based application for prediction. The proposed model includes a full data pretreatment protocol for transforming categorical variables to numerical values, imputing missing data, and normalizing using Min-Max scaling. To improve the datasets, a variety of techniques are used, including correlation, chi-squared, variance threshold, recursive feature elimination, sequential forward selection, lasso regression, and ridge regression. To predict CKD, the model uses four classifiers: K-Neighbors (KNC), Random Forest Classifiers (RFC), XgBoost (XgB), and Gausian Naive Bayes (GNB). The models' effectiveness is evaluated by testing their accuracy, examining the confusion matrix, and computing the Precision, Recall, F1 score, and support. In contrast to other algorithms performing on the same dataset, K-Neighbors classifiers (KNC) achieve an 89% accuracy rate, Random Forest Classifiers (RFC) and Gausian Naive Bayes (GNB) produce an 87% accuracy rate, and XgBoost (XgB) achieve a 99% accuracy rate to predict CKDs. The XgBoost classifier has the highest performance indicators. This study concludes that current advances in machine learning, as well as the use of predictive modeling.

关键词： Accuracy Machine learning algorithms Computational modeling Predictive models prediction algorithms Feature extraction Chronic kidney disease Numerical models Bayes methods Random forests

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Liver Disease prediction using various Image Processing Techniques and Machine Learning algorithms

Liver Disease Prediction using various Image Processing Tech...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： M. S Saravanan Siva Rama Krishna Prasad S Dhanalakshmi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of IT Guru Nanak Institutions Technical Campus Ibrahimpatnam India Department of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Predicting liver disease usually entails estimating a person’s chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and the Naive Bayes algorithm. From CHAOS Grand challenge webpage, data collected for liver images. Decision tree classifiers are compared to Naive Bayes classifiers for their accuracy and precision in diagnosing liver disease in patients. When it comes to predicting liver illness, the Naive Bayes classifier is $75.15 \%$ accurate, whereas the Novel Decision tree classifier is $80.03 \%$ accurate. 0.022 is the most significant number. g-power is used to calculate the sample size for two group at 16. The 95 percent alpha value for the g power is 0.05. The prediction of liver disease at early stage achieved by comparing Novel Decision Tree to Naive Bayes, Novel Decision Tree outperforms the latter in terms of precision and accuracy.

关键词： Accuracy Machine learning algorithms Liver diseases Image processing Software algorithms prediction algorithms Software systems Naive Bayes methods Decision trees Information technology

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Diabetes prediction based on Machine Learning algorithms: MNB, Random Forest, SVM

Diabetes Prediction based on Machine Learning Algorithms: MN...

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International Conference on Advanced Information Technologies (ICAIT)

作者： Wai Wai Khine Zaw Tun Department of Information Technology Sypporting and Maintenance University of Computer Studies (Sittway) Sittway Myanmar University of Computer Studies (Sittway) Sittway Myanmar

ISBN: (数字)9798331510046

ISBN: (纸本)9798331510053

The world's diabetes population is growing, which has concerned the medical community and prompted them to look for new ways to advance their technologies. In an effort to develop intelligent and successful diabetes prediction systems, research into machine learning approaches is still ongoing. The aim of this system is to help healthcare providers identify and effectively diagnose diabetes. The system created a machine model to forecast diabetes by utilizing a number of supervised machine learning approaches. This system explores the application of machine learning algorithms—Multinomial Naive Bayes (MNB), Random Forest, and Support Vector Machine (SVM)—in predicting diabetes using clinical and lifestyle data. The Pima Indians Diabetes Dataset from the National Institute of Diabetes and Digestive and Kidney Diseases was used to train and evaluate these models. In order to improve the model accuracy, data preprocessing, including feature scaling and handling missing values, was performed. After each algorithm was trained, its performance was assessed using measures including ROC-AUC, F1 score, recall, accuracy, and precision. Random Forest demonstrated high accuracy and robustness, efficiently handling complex interactions among features, while SVM showed strong performance in high-dimensional data settings. Multinomial Naive Bayes, although simpler, provided a baseline for comparison. The results suggest that Random Forest and SVM are effective tools for diabetes prediction, offering a promising approach for early diagnosis and intervention in clinical practice.

关键词： Support vector machines Training Accuracy Predictive models prediction algorithms Diabetes Bayes methods Random forests Medical diagnostic imaging Testing

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Earthquake prediction Model Using Random Forest and Gradient Boosting algorithms

Earthquake Prediction Model Using Random Forest and Gradient...

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International Conference on Communication and Electronics Systems (ICCES)

作者： D. Bujji Babu MVN L Revathi M. Senthil A. Lakshmi Parvathi B. Sceenilai Sk. Sheema QIS College of Engineering and Technology Ongole India Department of CSE QIS College of Engineering and Technology Ongole India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Accurate earthquake prediction remains a significant challenge in geosciences. This research investigates the application of machine learning algorithms, specifically Random Forest and Gradient Boosting algorithms, for earthquake prediction. By analyzing historical seismic data, these models aim to identify patterns and predict the likelihood of future earthquake occurrences. This study explores the potential of these machine learning techniques to improve earthquake prediction accuracy and contribute to better preparedness and mitigation strategies for this natural disaster.

关键词： Machine learning algorithms Accuracy Computational modeling Earthquakes Predictive models prediction algorithms Boosting Robustness Real-time systems Random forests

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Enhancing Kyphosis Disease prediction: Evaluating Machine Learning algorithms Effectiveness

Enhancing Kyphosis Disease Prediction: Evaluating Machine Le...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Mr. K. Praveen Kumar Ramya Dondapati HemaLatha Kalavakollu Nanditha Sri Vyshnavi Patagarla Tanveer Basha Sheikh Peddiboyina T.V.V.K.P.Ch.Raju Department of Computer Science and Engineering Sri Vasavi Engineering College(A) Tadepalligudem

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Kyphosis, characterized by the inward arching of the upper back, is often colloquially referred to as “roundback” or “hunchback” when a noticeable curvature is present. This condition typically arises due to weakened spinal bones, leading to compression or fractures. In children or adolescents, other forms of kyphosis may be attributed to spinal abnormalities or a progressive twisting of the spinal bones. While kyphosis can manifest at any age, it is most prevalent in teenagers. Multiple factors, ranging from poor posture and developmental issues to structural abnormalities in the spine, can contribute to its occurrence. This research introduces a machine learning approach aimed at predicting kyphosis disease, with a focus on enhancing early detection and improving patient outcomes. This research intends to incorporate different machine-learning approaches to biological data, such as Decision Trees and Random Forests, evaluate and assess the granularity of these algorithms. The findings underscore the significance of machine learning (ML) as a valuable tool for addressing biological problems in a broader context.

关键词： Radio frequency Machine learning algorithms Accuracy Biological system modeling Surgery Predictive models prediction algorithms

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Non Invasive Early Pancreatic Cancer prediction with Gradient Boosting algorithms Machine Learning Models with Clinical dataset collected from Urinary Biomarkers

Non Invasive Early Pancreatic Cancer Prediction with Gradien...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B Vasudevan R SenthilKumaran K Immanuel M V Karthikeyan Dept. of ECE SRM Institute of Science and Technology Kattangullathur Chennai India Dept. of ECE St.Joseph’s Institute of Technology Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Pancreatic cancer has a poor prognosis, but despite continuous challenges in medical research, efforts are still underway to identify strategies to diagnose the illness early and boost survival rates. This attempt has the potential to assist a greater population affected by this terrible illness. Using the revolutionary potential of Machine Learning (ML) algorithms, the healthcare industry has led the way in creating unique techniques to recognizing and categorizing pancreatic cancer risk. This study looks at the various ways used by researchers to diagnose pancreatic cancer using machine learning models. Through our research, we shed light on the sector’s ongoing difficulties while also recognizing significant advancements in this field. Our work relies on a comprehensive analysis of several boost algorithms and tactical strategies.

关键词： Machine learning algorithms Biological system modeling Surveillance Sociology Pancreatic cancer Biomarkers prediction algorithms

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Diesel Price prediction Models with Traditional Time-Series algorithms and Neural Networks

Diesel Price Prediction Models with Traditional Time-Series ...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sumet Muanchang Piyanit Wepulanon Phattara Khumprom Alex Davila-Frias Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Bangkok Thailand Computer Engineering Department King Mongkut's University of Technology Thonburi Bangkok Thailand Facultad de Ciencias Administrativas Escuela Politecnica Nacional Quito Ecuador

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Diesel fuel price is generally considered to be one of the core factors that contributes to the cost of transportation and logistics activities. However, stand-alone time-series algorithms without considering other related logistics attributes may not be sufficient to construct well-defined diesel price-prediction models. This work used artificial Deep Neural Networks (DNN s) to construct prediction models with consideration of the Road Freight Transport Index (RFTI) and the Baltic Dry Index (BDI) as model attributes. The results from regression models with BDI and RFTI attributes were compared against models employing traditional time-series algorithms, such as moving average, Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, and seasonal forecasting. Proposed diesel price prediction models with consideration of BDI and RFTI can perform better than traditional time series in the end.

关键词： Training Machine learning algorithms Costs Transportation Predictive models prediction algorithms Transformers

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prediction Model of Postgraduate Innovation Capability Evaluation Results Using Random Forest Based on Hyperparameter Optimization algorithms

Prediction Model of Postgraduate Innovation Capability Evalu...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Rulin Yuan Liang Feng Junjie He Tiantian Liu Tao Huang Bangchun Liu Graduate School Space Engineering University Beijing China College of System Engineering National University of Defense Technology Changsha China PLAN Troops of 91039 Beijing China PLA Troops of 91731 Beijing China Department of Fundamentals Space Engineering University Beijing China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

The evaluation of postgraduate innovation capabilities holds significant importance in higher education. This study proposes a prediction model for assessing graduate innovation capabilities using the Random Forest (RF) algorithm. To balance model accuracy and computational efficiency, grid search (GS) and Bayesian optimization (BO) algorithms are employed for hyperparameter optimization. A comparative analysis with the Backpropagation Neural Network (BPNN) model is conducted to identify the optimal model. Results indicate that the RF model achieves the highest $R^{2}$ value using the Intellectual Innovation $(U_prediction)$ feature set, while RF models under the Accumulation of Basic Knowledge for Innovation $(U_{3})$ feature set show the lowest MAE, MAPE, and RMSE values. Leveraging BO's advantage in low-dimensional parameter tuning, the optimized BO-RF model achieves the highest $R^{2}$ value (0.695) and lowest prediction errors (MAE $=7.712$ , MAPE = 14.42%, $\text{RMSE}=9.731$ ), with a hyperparameter optimization time approximately 1/121 of GS-BPNN. This study provides a scientifically effective method and foundation for accurately predicting the future development of graduate innovation capabilities.

关键词： Radio frequency Technological innovation Analytical models Computational modeling Predictive models prediction algorithms Hyperparameter optimization Data models Optimization Tuning

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Diabetes Risk prediction With Different Machine Learning algorithms

Diabetes Risk Prediction With Different Machine Learning Alg...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Nurgül Gürler Emre Arslan Ibraheem Shayea Ardak Mukhamedrakhimova Istanbul Technical University Istanbul Turkey Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Diabetes is a chronic disease that causes deaths all around the world, and become worse if not detected at early stage. Therefore, it is essential to detect the disease at early stages before it starts to damage the human body. At this point, detecting diabetes becomes a significant process that need to be performed efficiently. To predict diabetes risk, machine learning algorithms can be used to make prediction when a proper dataset is given. This research introduces some potentially useful machine learning algorithms to predict diabetes risk, guides the practical usage of these algorithms and discusses the comparative results. On this manner, three different machine learning algorithms and one feature selection method are implemented to an open-source dataset. The implementation results presented the applicability of the algorithms to predict diabetes risk.

关键词： Support vector machines Training Machine learning algorithms Accuracy Feature extraction prediction algorithms Diabetes

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