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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是81-90 订阅

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Precipitation Climate data Analysis using Power BI

Precipitation Climate Data Analysis using Power BI

引用

2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Akshara, A. Latha, B. Sri Sairam Engineering College Department Of Computer Science And Engineering Chennai44 India

ISBN: (纸本)9798350352689

This comprehensive report integrates findings from two separate but connected projects: a Power BI dashboard that shows historical rainfall patterns and trends, and a machine learning prediction model that estimates future rainfall using the Random Forest method. The Power BI dashboard provides a comprehensive analysis of almost a century's worth of historical rainfall data from India using data visualization techniques, offering illuminating insights into seasonal patterns, trends, and anomalies. The machine learning prediction model, on the other hand, makes use of complex algorithms and historical data to effectively anticipate future rainfall based on environmental factors. Combining the findings from both programs, this research aims to advance our understanding of rainfall patterns, boost the accuracy of rainfall forecasts, and facilitate well-informed decision-making. © 2024 IEEE.

关键词： machine learning

来源：评论

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Assessing IT Job Candidates Through Interpretable machine learning Model 10

Assessing IT Job Candidates Through Interpretable Machine Le...

引用

10th international conference on Smart computing and Communication, ICSCC 2024

作者： Sukardi, Andrew Saputra, Jayagatha Hariman, Sebastian Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic technology industry. This paper aims to address the challenges in recruiting proficient applicants by proposing an interpretable machine learning model for assessing IT job candidates. The study conducts experiments comparing various machine learning algorithms, including Logistic Regression, Random Forest, and Decision Tree, using data from the 'IT job applicant' public dataset. Evaluation methods such as the confusion matrix, accuracy, recall, and precision are employed to assess the model's performance. The findings reveal that the decision tree exhibits an accuracy exceeding 99.8%, establishing it as the top-performing model. The key contributing factor to this accuracy is identified as the 'ComputerSkills' feature. Additionally, within the decision tree, *** and Microsoft SQL Server are highlighted as influential factors positively impacting individual decision interpretation. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Application of machine learning Methods for Vector of RC States Analysis 27

Application of Machine Learning Methods for Vector of RC Sta...

引用

27th international conference on Soft computing and Measurements, SCM 2024

作者： Tikhonova, Anna I. Radabolsky, Vladislav S. Mamedov, Geidar A. Gismelbari, Mohamed A. Kosnevich, David A. Saint Petersburg Electrotechnical University 'LETI' Dept. of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363708

Modern local navigation systems have a large number of data sources, which often have low accuracy and a large amount of noise. The Kalman filter can minimize the impact of these noises on the data, but for nonlinear problems this cannot be done due to the difficulty of determining the distribution of the noise. Various machine learning models are used to find such non-trivial dependencies. This paper presents a review of ways to improve the accuracy of the Kalman vector result for robotic systems (RC) local navigation using machine learning methods. © 2024 IEEE.

关键词： Kalman filters

来源：评论

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Quantum machine learning: Bridging the Gap Between Theory and Practice 1

引用

1st international conference on Intelligent computing and Big data Analytics

作者： Mounika, M. Reddy, Sana Pavan Kumar Abhinaya, A. Akshay, G. BV Raju Inst Technol Dept AI&DS Narsapur Telangana India

ISBN: (数字)9783031746826

ISBN: (纸本)9783031746819;9783031746826

This study examines the intersection of quantum computing with machine learning, focusing on the potential, difficulties, and present progress in this emerging topic. The study explores the theoretical foundations of quantum machine learning algorithms, explaining the core ideas that utilize quantum mechanics to improve computing skills. The text explores many quanta computing paradigms, including quantum annealing, quantum circuits, and quantum-inspired algorithms. It evaluates their suitability and effectiveness in addressing intricate machine learning challenges. The study provides a thorough examination of current literature and recent advancements to explain the changing field of quantum machine learning frameworks. It emphasizes their potential to transform data analysis, optimization, and pattern identification. Furthermore, it discusses the practical factors and technological challenges related to the implementation of quantum machine learning algorithms on current quantum hardware, highlighting the need of reliable error correction, qubit coherence, and scalable architectures. The study offers valuable insights into the significant influence of quantum machine learning on diverse fields such as banking, healthcare, and cybersecurity by connecting theoretical principles with real-world applications. Furthermore, it delineates prospective areas of study and possible methods for fully utilizing quantum computing in machine learning applications, thus promoting cooperation among researchers, practitioners, and stakeholders in shaping the future of this dynamic and interdisciplinary domain.

关键词： Quantum computing machine learning Theory Practice Algorithms Interdisciplinary

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Predictive Modeling of Cryptocurrency Prices Using machine learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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A Method for Wal-Mart Sales Forecasting Based on machine learning 24

A Method for Wal-Mart Sales Forecasting Based on Machine Lea...

引用

2024 international conference on Cloud computing and Big data, ICCBD 2024

作者： Xu, Ruiheng College of Chemistry Jilin University China

ISBN: (纸本)9798400710223

With the widespread use of big data science, there is a growing need for data-driven decision-making in the retail industry. As one of the largest retailers in the world, Walmart's sales forecasting through machine learning methods is crucial to its business operations, which directly affects inventory management, supply chain optimization, and promotional strategy development and helps the company to save huge amounts of costs. In this paper, a prediction method combining multiple machine-learning techniques is proposed using historical sales data. The experimental results verify the method's effectiveness and significantly improve the accuracy of sales forecasting, which can provide an important reference for Walmart's inventory management and supply chain optimization. © 2024 ACM.

关键词： feature engineering machine learning model selection Sales Forecasting

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A Portrait Segmentation Algorithm Combining MobileNeXt and Attention Mechanism 24

A Portrait Segmentation Algorithm Combining MobileNeXt and A...

引用

2024 international conference on computing, machine learning and data science, cmlds 2024

作者： Cheng, Yan Huang, Zou Wang, Guixi Lai, Jianming Xiong, Yuqi School of Software Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China School of Digital Industry Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China

ISBN: (纸本)9798400716393

Existing portrait segmentation methods are easily affected by the background. To address this challenge, we propose a simple convolution-based portrait segmentation algorithm to solve the problems of complex background, low accuracy and poor efficiency in real environments. Combined with multi-scale convolution, we design a multi-scale sandglass module structure (MultiScale-Sandglass-CA, MSC), which expands the depth and width of the network without increasing the number of parameters, and enhances the feature learning ability of the model for complex backgrounds;and the MSC module is used for feature extraction, and the introduction of coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the network's ability to learn the key information. The introduction of the coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the focusing of the network on the key information;at the same time, the introduction of the hybrid loss function, which helps to obtain clear portrait edges. Experiments are conducted on Matting and EG1800 public datasets, and the results show that the algorithm can significantly improve the mean intersection and merger ratio (mIoU) and pixel accuracy (Acc), etc. The mIoU can reach up to 97%, and the pixel accuracy can reach up to 10%. Among them, the mIoU index can reach 97.91% (Matting) and 94.72% (EG1800). Compared with the mIoU predicted by PortraitNet, it is 1.29% and 1.19% higher, respectively. In addition, experiments are conducted for the MSC structure proposed in this paper, which proves the superiority of the model in this paper. © 2024 ACM.

关键词： Convolution

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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep learning, XGBoost and Statistical Survival Analysis 23

Variational Encoder Based Synthetic Alzheimer's Data Generat...

引用

23rd IEEE international conference on machine learning and Applications, ICMLA 2024

作者： Musto, Henry Stamate, Daniel Stahl, Daniel University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom School of Health Sciences The University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Kings College London Department of Biostatistics London United Kingdom

ISBN: (纸本)9798350374889

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted. © 2024 IEEE.

关键词： Adversarial machine learning

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Abdominal Trauma Detection using Hybrid Quantum machine learning

Abdominal Trauma Detection using Hybrid Quantum Machine Lear...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Islam, Md Majedul He, Jing Kennesaw State University Department of Computer Science Marietta United States

ISBN: (纸本)9798350362480

machine learning methods have made huge improvements to medical imaging, particularly in the utilization of CT scans for intricate trauma cases. This paper initiates an exploratory journey, employing a hybrid quantum machine learning (HQML) approach to enhance the detection accuracy of abdominal trauma. Our study meticulously curates an extensive dataset from abdominal CT scans, navigating the inherent challenges presented by the voluminous nature and complex features of such medical data. We use quantum transfer learning techniques in a new way by combining the complex details of these scans into a computing framework that combines the powerful pattern recognition of quantum computing with the stability of classical machine learning. Our approach positions itself at the lead of medical innovation, poised to refine diagnostic precision and accelerate therapeutic protocols through an advanced analytical perspective. By looking at how well classical ResNet architectures and quantum models work, we find small differences in how well they work. This shows that quantum algorithms could decode medical images with a level of good accuracy. Our findings do not merely highlight the transformative potential of quantum computing in medical diagnostics but also pave the way for ensuing explorations in the domain of hybrid quantum-classical machine learning solutions. © 2024 IEEE.

关键词： Computerized tomography

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Enhanced machine learning Algorithms for Classification in Agricultural Advisory Systems and Agricultural data Analysis

Enhanced Machine Learning Algorithms for Classification in A...

引用

2024 IEEE international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Logesh, M. Domnic, S. Nelson Raja, J. Tamil Nadu Thanjavur India National Institute of Technonology Department of Computer Application Tamil Nadu Tiruchirappalli India Thiagarajar College Department of Computer Science Tamil Nadu Madurai India

ISBN: (纸本)9798331522988

Agricultural technology, ensuring reliability, and optimizing processes are paramount. This work explores the application of machine learning algorithms (ML) to two critical areas: agricultural advisory systems (AAS) and agricultural data analysis (ADA). By leveraging historical data and advanced algorithms, this work aims to enhance predictive accuracy and operational efficiency in ADA and AAS. The proposed work focuses on various ML models for classification tasks, comparing their performance metrics, and discussing their applicability in agricultural sector. The findings highlight the ML model to significantly improve outcomes in agricultural management. © 2024 IEEE.

关键词： Adversarial machine learning

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