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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Sherif, Khaled Azmy, Mohamed Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute Department of Communications Mansoura Egypt Delta Higher Institute Department of Civil Engineering Mansoura Egypt University of Tishreen Department of Statistics and Programming Faculty of Economics Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia

ISBN: (纸本)9798350323511

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series' sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes. © 2023 IEEE.

关键词： Forecasting

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Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis 3

Forecasting Global Monkeypox Infections Using LSTM: A Non-St...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Osama, Omnia M. Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura Egypt University of Tishreen Faculty of Economics Department of Statistics and Programming Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia Delta Higher Institute Department of Communications Mansoura Egypt

ISBN: (纸本)9798350323511

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating the significant potential of advanced machine learning techniques in epidemiological forecasting. Our LSTM model effectively navigates the challenges posed by non-stationary time-series data, a common issue in epidemiological studies. It successfully captures the underlying patterns in the data, producing reliable forecasts. The model's performance was evaluated using several metrics, including RMSE, MSE, MAE, and R2, all of which pointed to its robust and satisfactory predictive capabilities. Our findings underscore the significant role LSTM models can play in informing the development of timely and effective disease control and prevention strategies. They thereby contribute to enhancing public health responses to emerging infectious diseases such as Monkeypox. However, despite the promising results, the study highlights the ongoing challenge of enhancing the interpretability of LSTM models, an area that warrants further research. As a future direction, efforts should focus on refining LSTM models to bolster their interpretability, ensuring their broader adoption and utility in public health practice. © 2023 IEEE.

关键词： Long short-term memory

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A Seamless Phase II/III Design with Dose Optimization for Oncology Drug Development

arXiv

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arXiv 2024年

作者： Li, Yuhan Zhang, Yiding Mi, Gu Lin, Ji Department of Statistics University of Illinois Urbana-Champaign United States Biostatistics and Programming Sanofi France

The US FDA’s Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel Seamless Phase II/III Design with Dose Optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both Accelerated Approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power. © 2024, CC BY.

关键词： Oncology

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PREDICTION OF HEART DISEASE USING CLASSIFICATION AND FEATURE SELECTION METHODS 17

PREDICTION OF HEART DISEASE USING CLASSIFICATION AND FEATURE...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Akarçay Pervin, Özlem Yapici Pehlivan, Nimet Weber, Gerhard Wilhelm KTO Karatay University Department of Computer Programming Konya Turkey Selçuk University Department of Statistics Konya Turkey Poznan University of Technology Poznan Poland Middle East Technical University Institute of Applied Mathematics Ankara Turkey

ISBN: (纸本)9789616165617

Heart disease, known as cardiovascular disease has been one of the main causes of death worldwide in recent years. It is affected by various risk factors such as high blood pressure, high cholesterol, diabetes, smoking, obesity, physical inactivity, etc. In this study, Cleveland database of UCI repository of heart disease patients is used. The dataset includes 303 instances and 76 variables which of 14 variables are considered. At first, three different feature selection techniques, namely LASSO, Elastic Net and Adaptive LASSO under specific tuning parameters determined by cross-validation, are applied to reduce the number of independent variables used for the prediction. Then, five different classification techniques, K-nearest neighbor, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression are performed to predict heart disease. Performances of these methods are measured by different metrics, such as accuracy, sensitivity, specificity, and Matthew correlation coefficient. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Decision trees

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ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUCTURED ORNSTEIN-UHLENBECK VARIATIONAL AUTOENCODER 10

ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUC...

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10th International Conference on Learning Representations, ICLR 2022

作者： Moreta, Lys Sanz Rønning, Ola Al-Sibahi, Ahmad Salim Hein, Jotun Theobald, Douglas Hamelryck, Thomas Probabilistic Programming Group PLTC Section University of Copenhagen Copenhagen Denmark Department of Statistics University of Oxford Oxford United Kingdom Brandeis University Biochemistry Department MA United States Probabilistic Programming Group SCARB PLTC Section Department of Biology Computer Science University of Copenhagen Copenhagen Denmark

We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary process. The model makes use of a tree-structured Ornstein-Uhlenbeck process, obtained from a given phylogenetic tree, as an informative prior for a variational autoencoder. We show the model performs well on the task of ancestral sequence reconstruction of single protein families. Our results and ablation studies indicate that the explicit representation of evolution using a suitable tree-structured prior has the potential to improve representation learning of biological sequences considerably. Finally, we briefly discuss extensions of the model to genomic-scale data sets and the case of a latent phylogenetic tree. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Genome

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Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

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Electronic Engineering (ICEEM), International Conference on

作者： Khaled Sherif Mohamed Azmy Khder Alakkari Mostafa Abotaleb El-Sayed M. El-Kenawy Department of Communications Delta Higher Institute Mansoura Egypt Department of Civil Engineering Delta Higher Institute Mansoura Egypt Department of Statistics and Programming Faculty of Economics University of Tishreen Tartous Syria Department of System Programming South Ural State University Chelyabinsk Russia

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series’ sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes.

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Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis

Forecasting Global Monkeypox Infections Using LSTM: A Non-St...

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Electronic Engineering (ICEEM), International Conference on

作者： Omnia M. Osama Khder Alakkari Mostafa Abotaleb El-Sayed M. El-Kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Statistics and Programming Faculty of Economics University of Tishreen Tartous Syria Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications Delta Higher Institute Mansoura Egypt

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating the significant potential of advanced machine learning techniques in epidemiological forecasting. Our LSTM model effectively navigates the challenges posed by non-stationary time-series data, a common issue in epidemiological studies. It successfully captures the underlying patterns in the data, producing reliable forecasts. The model’s performance was evaluated using several metrics, including RMSE, MSE, MAE, and R 2 , all of which pointed to its robust and satisfactory predictive capabilities. Our findings underscore the significant role LSTM models can play in informing the development of timely and effective disease control and prevention strategies. They thereby contribute to enhancing public health responses to emerging infectious diseases such as Monkeypox. However, despite the promising results, the study highlights the ongoing challenge of enhancing the interpretability of LSTM models, an area that warrants further research. As a future direction, efforts should focus on refining LSTM models to bolster their interpretability, ensuring their broader adoption and utility in public health practice.

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A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials

arXiv

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arXiv 2023年

作者： Jiang, Zhenghao Mi, Gu Lin, Ji Lorenzato, Christelle Ji, Yuan Department of Statistics University of Chicago 5747 South Ellis Avenue ChicagoIL60637 United States Biostatistics and Programming Sanofi 450 Water Street CambridgeMA02141 United States Biostatistics and Programming Sanofi R&D Vitry-sur-Seine France Department of Public Health Science University of Chicago 5841 South Maryland Avenue ChicagoIL60637 United States

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://***/mats/. Copyright © 2023, The Authors. All rights reserved.

关键词： Oncology

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Conditional borrowing external data to establish a hybrid control arm in randomized clinical trials

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Journal of Biopharmaceutical statistics 2022年第6期32卷 954-968页

作者： Hongfei Li Ram Tiwari Qian H. Li a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USAb Department of Statistics University of Connecticut Storrs Connecticut USA a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USA c Department of Biostatistics and Programming Statistics & Data Corporation Tempe Arizona USA

ABSTRACTABSTRACTUtilizing external data from the real world, including data from historical clinical trials, has received increasing interest in drug development. The use of external data to support drug evaluation in clinical trials has mainly been through using various matching methods for baseline characteristics to form external control arms in single-arm trials or to augment control arms of randomized controlled trials in hybrid approaches. However, matching the baseline characteristics between the trial and the external subjects can only guarantee comparability on the level of baseline characteristics. Differences in outcomes between the two data sources may still exist due to contemporaneous and operational characteristics. Similarity between the outcomes in the trial control and the external subjects with similar baseline characteristics can be critical in leveraging the external subjects in the clinical trials. In this paper, a resampling method for augmenting control arms in randomized controlled trials is proposed under the conditional borrowing framework. The new method establishes empirical distributions for the hazard ratio in outcomes between the external and trial control subjects. The borrowing decision is then derived from this empirical distribution using a measure of similarity. Once the borrowing decision is established, the borrowing weights for the external subjects, based on the similarity measure, are incorporated in the weighted partial likelihood to evaluate the treatment effect. The operating characteristics of the hybrid control arm, under both the conditional borrowing and unconditional borrowing frameworks, are evaluated. Simulation is conducted to evaluate Type I error, bias, and power. An illustrative example using simulated data is also presented.

关键词： Conditional borrowing hybrid control real-world data bias Type I error weighted partial likelihood

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Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata

arXiv

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arXiv 2023年

作者： Wesolowski, Jacek Wieczorkowski, Robert Wójciak, Wojciech Programming Coordination of Statistical Surveys and Registers Department Statistics Poland Aleja Niepodleglości 208 Warsaw00-925 Poland Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland

The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified π estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository. Copyright © 2023, The Authors. All rights reserved.

关键词： Sampling

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