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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 13417 条记录，以下是261-270 订阅

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Actual Rate Prediction: Extension in Real Estate Prediction using machine learning 1

Actual Rate Prediction: Extension in Real Estate Prediction ...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Archana, Archana Bhatnagar, Prachi Singh, Narendra Sharma, Manoj Kumar Kumar, Gaurav Pandey, Mahima Shankar Bansal, Nidhi IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India IIMT University Department of Computer Science and Application Uttar Pradesh Meerut India Panchwati Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Meerut India Vishveshwarya Group of Institute Dadri Department of Computer Science and Engineering Uttar Pradesh GB Nagar India Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Symbiosis Institute of Technology Nagpur Campus Pune India

ISBN: (纸本)9798350356816

machine learning algorithms always perform well to solve real-life problems encountered in daily life. Due to large data sets, analysis is also becoming too complex to predict anything. A lot of calculations are being done to find the exact patterns and values to get accurate results. Here machine learning algorithms are giving tremendous performance while applying various classification techniques as it gives algorithms based on specifications for different aspects. In different cities, forecasting is necessary as people must move from one place to another due to the pandemic situation. A case study has also been conducted with real estate real data by applying machine learning algorithms. A new model has also been implemented with Jupyter Notebook in the proposed paper and has received some positive feedback. © 2024 IEEE.

关键词： Adversarial machine learning

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Accurate machine learning Algorithms for Monkey Pox Detection 3

Accurate Machine Learning Algorithms for Monkey Pox Detectio...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Bhupalem, Maneesha Keerthika, Kanchipati Rama Renukadevi, P. Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350375190

The necessity for precise diagnostic techniques and predictive models has been highlighted by the persistent threat that infectious disease outbreaks pose to public health. The present study presents a novel methodology that utilises the knowledge gained from the Covid-19 pandemic to create a precise machine learning algorithm for the early identification and forecasting of monkeypox, a zoonotic illness that is caused by the monkeypox virus (MPXV). Based on the information gathered and experiences gained during the Covid-19 outbreak, this study looks into any possible connections and common epidemiological patterns between Covid-19 and monkeypox. It looks at how data-driven methods, such machine learning, might be used to improve the precision and promptness of monkeypox diagnosis and forecasting. Ultimately, by building on the knowledge gained from the Covid-19 pandemic, this research attempts to improve the readiness and reaction capacities in the face of newly developing infectious diseases such as monkeypox. © 2024 IEEE.

关键词： Forecasting

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Multi-level Disentangled Contrastive learning on Heterogeneous Graphs 24

Multi-level Disentangled Contrastive Learning on Heterogeneo...

引用

16th international conference on machine learning and computing (ICMLC)

作者： Lin, Tong Zhou, Cangqi Li, Qianmu Hu, Dianming Nanjing Univ Sci & Technol Nanjing Peoples R China SenseDeal Intelligent Technol Co Ltd Nanjing Peoples R China

ISBN: (纸本)9798400709234

Self-supervised learning methods, including contrastive learning based on Heterogeneous graph neural networks (HGNNs), have achieved great success in learning the representations of heterogeneous information networks (HINs). However, the existing self-supervised methods usually neglect the entanglement of the latent factors behind HINs, which decreases the performance of downstream tasks. In this paper, we propose Multi-level Disentangled Heterogeneous Graph Contrastive learning method and learning disentangled HIN node representations in a self-supervised way. Specifically, we first design a tailored encoder to capture the latent factors and semantics of nodes in input HIN and learn their factorized representations. Then we propose a novel contrastive learning discrimination objective designed for disentangled HIN node representation learning. Extensive experiments conducted on various real-world datasets demonstrate the superiority of our method against state-of-the-art baselines.

关键词： Heterogeneous Graph Contrastive learning Disentangled Representation learning

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An Effective machine learning Algorithm for Forecasting the Market Value of a House 3

An Effective Machine Learning Algorithm for Forecasting the ...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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Unifying Robust Activation Functions for Reduced Adversarial Vulnerability with the Parametric Generalized Gamma Function 23

Unifying Robust Activation Functions for Reduced Adversarial...

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

作者： Alemany, Sheila Worthington, Emma Dominguez, Alberto Grapel, Ilan Pissinou, Niki School of Computing and Information Sciences Florida International University United States College of Engineering and Applied Science University of Colorado Boulder United States National Science Foundation Research Experience for Teachers Fellow United States

ISBN: (纸本)9798350374889

Adversaries minimally perturb deep learning input data to reduce a learning model's ability to produce domain-specific data-driven recommendations to solve specialized tasks. This vulnerability to adversarial perturbations has been argued to stem from a learning model's nonlocal generalization over complex input data. Given the incomplete information in a complex dataset, a learning model captures nonlinear patterns between data points with volatility in the loss surface and exploitable areas of low-confidence knowledge. It is the responsibility of activation functions to capture the nonlinearity in data and, thus, has inspired disjointed research efforts to create robust activation functions. This work unifies the properties of activation functions that contribute to robust generalization with the generalized gamma distribution function. We show that combining the disjointed characteristics presented in the literature with our parametric generalized gamma activation function provides more effective robustness than the individual characteristics alone11The source code for this research effort: https://***/sheilaalemany/***. © 2024 IEEE.

关键词： Adversarial machine learning

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Forecasting of Crime Hotspots and Analysis using machine learning

Forecasting of Crime Hotspots and Analysis using Machine Lea...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Maheswari, G. Felixa, S. Aj, Vidhya Sambath, M. Remya, K. Hindustan Institute Of Technology And Science Department Of Information Technology Chennai India

ISBN: (纸本)9798350364828

One of the most important and concerning aspects of our society is crime. Countless crimes are committed daily, and as a result, the lives of all individuals are becoming increasingly unpleasant. Thus, stopping the crime from happening is difficult. In order to prevent the worldwide community from falling victim to the certain actions of criminals or other disruptive elements, the rate of crime should be reduced through the application of machine learning techniques. As a result, crime prediction techniques are essential for foretell future crimes and decreasing overall crime rates. Understanding the crime pattern is a major step to analyzing and planning a response to any kind of criminal activity. Identifying the type of crime along with the time and place where it has occurred, is the main goal of this system. This work implies the use of machine learning methods, such as decision tree classifier and linear regression, to categorize among different criminal patterns. The accuracy attained was relatively high when compared to previously planned work. © 2024 IEEE.

关键词： Crime

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Efficient 3D Reconstruction of Multiple Plants from UAV Images with Deep learning 24

Efficient 3D Reconstruction of Multiple Plants from UAV Imag...

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16th international conference on machine learning and computing (ICMLC)

作者： Huang, Hong Wang, Zhuowei Zhao, Genping Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400709234

Acquiring the 3D structure of plants is a critical task in the agricultural industry. Existing methods of generating 3D point clouds for multiple plants require a long processing time. In this paper, a 3D reconstruction method for numerous plants is proposed. Firstly, camera parameters in different viewpoints are obtained from the aerial image of plants by incremental structure from motion. Subsequently, the learning-based multi-view stereo takes images and the corresponding camera parameters as inputs to acquire initial depth maps. Finally, the depth maps are filtered and fused to produce a complete and dense 3D point cloud. We conducted experiments on an agricultural orchard dataset to compare with other methods. Experimental results demonstrate that our method reconstructs point clouds of plants with good quality while having a lower running time.

关键词： 3D reconstruction Plant Point cloud UAV images Deep learning

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Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular data 24

Imb-FinDiff: Conditional Diffusion Models for Class Imbalanc...

引用

5th international conference on AI in Finance

作者： Schreyer, Marco Sattarov, Timur Sim, Alexander Wu, Kesheng Int Comp Sci Inst ICSI Berkeley CA 94704 USA Deutsch Bundesbank Frankfurt Germany Lawrence Berkeley Natl Lab LBNL Berkeley CA USA

ISBN: (纸本)9798400710810

Handling imbalanced datasets remains a critical challenge in financial machine-learning applications such as loan approval, credit scoring, and fraud detection. We present Imbalanced Financial Diffusion (Imb-FinDiff), a novel denoising diffusion framework designed to address class imbalance in financial tabular data. Our framework leverages embedding encodings for categorical and numerical attributes, effectively managing the complexities of mixed-type financial datasets. By incorporating a dual learning objective, (i) diffusion timestep noise and (ii) class label prediction, we synthesize minority class samples. Extensive experiments on diverse and real-world financial datasets demonstrate that Imb-FinDiff maintains the statistical properties of the original data while reducing bias caused by class imbalance. The minority class samples generated by Imb-FinDiff enhance the utility and fidelity of downstream machine learning classifiers.

关键词： neural networks denoising diffusion probabilistic models imbalanced learning synthetic data generation mixed-type tabular data

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9th IEEE/ACIS international conference on Big data, Cloud computing, and data science, BCD 2024

9th IEEE/ACIS International Conference on Big Data, Cloud Co...

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9th IEEE/ACIS international conference on Big data, Cloud computing, and data science, BCD 2024

ISBN: (纸本)9798350394191

The proceedings contain 45 papers. The topics discussed include: enhancing and validating simulator data generation integrity through frequency-based analysis;data analysis of interest continuation factors for creating core fans in anime production projects;quarterly analysis of purchasing trends using product-specific association analysis;utilizing machine learning predictive analytics to enhance early sepsis diagnosis in critical care setting;OSS fault removal system based on deep learning inspired by immune system;multi-label text classification with transfer learning;optimizing deep learning accuracy: visibility filtering approach for early wildfire detection in forest sensor images;and understanding co-working space users: aspect-based sentiment analysis.

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Leveraging EEG Signals and machine learning for Schizophrenia Classification 6

Leveraging EEG Signals and Machine Learning for Schizophreni...

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6th international conference on computing and Informatics, ICCI 2024

作者： Elfarsy, Abdelrahman El-Metwally, Sara Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

ISBN: (纸本)9798350373875

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. This paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. The dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. The three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96%), while SVM demonstrated strong precision and recall (95%). These findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis. © 2024 IEEE.

关键词： Support vector machines

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