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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Amin, Sherif Tawfik Eishah, Hassan Abu Hamdan, Alfadil Ahmed Metwally, Abdelnasser Abdelwakil Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t

关键词： Risk assessment

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Time Series Forecasting Using LSTM to Predict Stock Market Price in the First Quarter of 2024

Time Series Forecasting Using LSTM to Predict Stock Market P...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Maharani, Farrah A Ivana, Sharen Fithriyah, Belva Zakiyyah, Alfi Yusrotis Sihotang, Erna Fransisca Angela Bina Nusantara University School of Computer Science Mathematics Department Jakarta Indonesia Bina Nusantara University Jakarta School of Computer Science Statistics Department Indonesia

ISBN: (纸本)9798350364101

Predictions on the stock market are critical because they significantly influence the world economy. The value of share prices usually experiences continuous fluctuations. Therefore, predicting share price growth is very important. Notable stocks dominating global markets include Amazon, Apple, Microsoft, and Google. The paper discusses predictions for developing these four shares for the next two months. The research uses the Long Short-Term Memory (LSTM) Network method to predict stock prices for the next two months using an input sequence of previous stock values. LSTM methods demonstrate the capacity to retain long-term memory while reducing the influx of irrelevant information and superior efficiency in data processing, prediction, and classification. After analysis, the results show that the close price value from Microsoft shows the highest results, reaching 374.736. © 2024 IEEE.

关键词： Long short-term memory

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Goodness of fit for the Waring distribution

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Statistical Theory and Related Fields 2025年第1期9卷 1-11页

作者： Yanlin Tang Jinglong Wang Menghan Yi Zhongyi Zhu KLATASDS-MOE School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China Department of Statistics and Data Science Fudan UniversityShanghaiPeople’s Republic of China

The Waring distribution is an important two-parameter discrete distribution,commonly used in fields such as ecology,linguistics,and information science,where heavy tails are often *** this paper,we propose a new goodness-of-fit test for the Waring distribution,which is established through the hazard rate and a linear equivalent definition of the Waring *** establish an asymptotic Chi-square null distribution for the proposed test and show that it is more powerful than classical methods in simulation ***,we apply the test to analyze the authorships of published papers on computer science.

关键词： Waring distribution goodness-of-fit test long-tailed distribution hazard rate

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Robust and Space-Efficient Dual Adversary Quantum Query Algorithms 31

Robust and Space-Efficient Dual Adversary Quantum Query Algo...

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Czekanski, Michael Kimmel, Shelby Witter, R. Teal Department of Statistics and Data Science Cornell University IthacaNY United States Department of Computer Science Middlebury College VT United States Department of Computer Science and Engineering New York University BrooklynNY United States

ISBN: (纸本)9783959772952

The general adversary dual is a powerful tool in quantum computing because it gives a query-optimal bounded-error quantum algorithm for deciding any Boolean function. Unfortunately, the algorithm uses linear qubits in the worst case, and only works if the constraints of the general adversary dual are exactly satisfied. The challenge of improving the algorithm is that it is brittle to arbitrarily small errors since it relies on a reflection over a span of vectors. We overcome this challenge and build a robust dual adversary algorithm that can handle approximately satisfied constraints. As one application of our robust algorithm, we prove that for any Boolean function with polynomially many 1-valued inputs (or in fact a slightly weaker condition) there is a query-optimal algorithm that uses logarithmic qubits. As another application, we prove that numerically derived, approximate solutions to the general adversary dual give a bounded-error quantum algorithm under certain conditions. Further, we show that these conditions empirically hold with reasonable iterations for Boolean functions with small domains. We also develop several tools that may be of independent interest, including a robust approximate spectral gap lemma, a method to compress a general adversary dual solution using the Johnson-Lindenstrauss lemma, and open-source code to find solutions to the general adversary dual. © Michael Czekanski, Shelby Kimmel, and R. Teal Witter;licensed under Creative Commons License CC-BY 4.0.

关键词： Boolean functions

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Selective machine learning of doubly robust functionals (vol 111, pg 517, 2024)

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BIOMETRIKA 2024年第3期111卷 517-535页

作者： Cui, Y. Tchetgen, E. J. Tchetgen School of Management & Center for Data Science Zhejiang University 866 Yuhangtang Road Hangzhou Zhejiang 310058 China Department of Statistics and Data Science The Wharton School University of Pennsylvania 265 South 37th Street Philadelphia Pennsylvania 19104 USA

While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine learning algorithms are available for estimating the nuisance parameters. We introduce a new selection criterion aimed at bias reduction in estimating the functional of interest based on a novel definition of pseudo risk inspired by the double robustness property. Intuitively, the proposed criterion selects a pair of learners with the smallest pseudo risk, so that the estimated functional is least sensitive to perturbations of a nuisance parameter. We establish an oracle property for a multi-fold cross-validation version of the new selection criterion that states that our empirical criterion performs nearly as well as an oracle with a priori knowledge of the pseudo risk for each pair of candidate learners. Finally, we apply the approach to model selection of a semiparametric estimator of average treatment effect given an ensemble of candidate machine learners to account for confounding in an observational study that we illustrate in simulations and a data application.

关键词： Average treatment effect Doubly robust functional Influence function Machine learning Model selection

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Exploring Cosine and Euclidean Metrics in K-Nearest Neighbors for Breast Cancer Diagnosis 7

Exploring Cosine and Euclidean Metrics in K-Nearest Neighbor...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Prasetyo, Simeon Yuda Nabiilah, Ghinaa Zain Sihotang, Erna Fransisca Angela Minor, Kelvin Asclepius Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350375886

Breast cancer is a prevalent and heterogeneous disease posing a significant global health burden, with millions of new cases diagnosed annually. Early detection is paramount for improving patient outcomes and reducing mortality rates associated with breast cancer. Machine learning techniques offer promising avenues for enhancing early detection and diagnosis. This research explores the efficacy of Cosine and Euclidean metrics within the K-Nearest Neighbors (KNN) algorithm for breast cancer diagnosis. Utilizing a comprehensive dataset, experiments were conducted varying the number of neighbors and distance metrics. Results indicate that both Cosine and Euclidean metrics yield robust performance, with average accuracies of approximately 94.85% and 94.96%, respectively. The best-performing model achieved an accuracy of 95.61% for both metrics. These findings underscore the potential of the KNN algorithm in accurately predicting breast cancer outcomes. Future research could explore alternative distance metrics and machine learning models to further improve diagnostic accuracy in breast cancer detection, thereby advancing clinical practice and patient care. This study contributes valuable insights into the utilization of machine learning algorithms for breast cancer diagnosis and emphasizes the importance of leveraging computational techniques for early detection, ultimately leading to improved prognosis and patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Development of an ML model of a wind turbine for fault detection 1

Development of an ML model of a wind turbine for fault detec...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Adelshiyev, Syrym Kurmashev, Dias Department of Computational and Data Sciences Computer Science Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

In recent years, the advancement of renewable energy technologies has gained significant momentum, with wind energy emerging as a prominent source of clean electricity generation. The efficient operation and maintenance of wind turbines are crucial for maximizing energy production while minimizing costs. This paper presents the development of a machine learning (ML) model tailored specifically for wind turbine performance optimization. The proposed ML model integrates various data sources including meteorological conditions, turbine sensor data, historical performance records, and maintenance logs. Leveraging these datasets, the model employs sophisticated algorithms such as neural networks, decision trees, and ensemble methods to predict turbine performance, detect anomalies, and optimize maintenance schedules. ©2024 IEEE |

关键词： Wind turbines

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Salary Classification & Prediction based on Job Field and Location using Ensemble Methods

Salary Classification & Prediction based on Job Field and Lo...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Jocelyn Verna Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary. © 2023 IEEE.

关键词： Decision trees

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Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling 41

Theory of Consistency Diffusion Models: Distribution Estimat...

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41st International Conference on Machine Learning, ICML 2024

作者： Dou, Zehao Chen, Minshuo Wang, Mengdi Yang, Zhuoran Department of Statistics and Data Science Yale University New Haven United States Electrical and Computer Engineering Princeton University Princeton United States

Diffusion models have revolutionized various application domains, including computer vision and audio generation. Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. In response, consistency models have been developed to merge multiple steps in the sampling process, thereby significantly boosting the speed of sample generation without compromising quality. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem. Our analysis yields statistical estimation rates based on the Wasserstein distance for consistency models, matching those of vanilla diffusion models. Additionally, our results encompass the training of consistency models through both distillation and isolation methods, demystifying their underlying advantage. Copyright 2024 by the author(s)

关键词： computer vision

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