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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33672 条记录，以下是451-460 订阅

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A Case Study of Accurate and Fair Classification 13th

A Case Study of Accurate and Fair Classification

引用

13th international conference on Computer engineering and Networks (CENet)

作者： Liu, Xiaoqian Chao, Zemin Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing 210031 Peoples R China

ISBN: (纸本)9789819992454;9789819992430;9789819992423

As artificial intelligence and human life become increasingly inseparable, the legal or ethical issues faced by artificial intelligence systems in autonomous decision-making are also increasing. During the training process, the algorithms may be influenced by human biases such as gender, race, and other factors, leading to discrimination and affecting fairness. To establish secure intelligent systems, fair machine learning has become a popular research direction. This work demonstrates the existing definitions of fairness and designs experiments to show that combining clustering algorithms into the data handling process can effectively improve the classification accuracy and fairness on bank loan dataset. In the case study, K-means clustering, hierarchical clustering and Gaussian Mixture Model are used, proving that the clustering algorithms can significantly improve the accuracy of the model and ensure the relative fairness of the classification results.

关键词： Fairness Clustering algorithm machine learning

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Building a Cancer Type Prediction Model Based on machine learning Algorithms by Analyzing the Origin Genes of Multiple Cancers Including Prostate Cancer 3

Building a Cancer Type Prediction Model Based on Machine Lea...

引用

3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Zhang, Beier Guo, Xintian Fan, Qi Wang, Tingting Xia, Sihan Meng, Fanyi Huaibei Normal Univ Sch Comp Sci & Technol Huaibei Peoples R China

ISBN: (纸本)9798350388732;9798350388725

This study identified common genes associated with various primary cancers, including prostate cancer, and established a predictive model to accurately forecast the likelihood of cancer occurrence and its specific type. The aim is to offer physicians assistance in treatment decisions and meticulous follow-up, ultimately improving patient prognosis. Tumor sample data were collected from TCGA database, and differential expression analysis was employed for feature gene selection. machine learning models were constructed to trace the origin of cancer genes. The results revealed 663 differentially expressed genes exhibiting characteristic expression in prostate cancer, squamous cell lung cancer, thyroid cancer, clear cell renal cell carcinoma, and bladder urothelial carcinoma. Logistic regression demonstrated superior stability and performance, with an average accuracy increase of 4% compared to other models. Therefore, precise prediction of cancer occurrence and its specific type based on gene expression status can be achieved, providing robust support for physicians' diagnosis and treatment decisions. This approach has the potential to enhance patient prognosis by enabling accurate predictions and targeted interventions.

关键词： TCGA database multi-cancer prediction differential expression analysis machine learning patient prediction model

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Exploring System and machine learning Performance Interactions when Tuning Distributed data Stream Applications 38

Exploring System and Machine Learning Performance Interactio...

引用

38th IEEE international conference on data engineering (ICDE)

作者： Odysseos, Lambros Herodotou, Herodotos Cyprus Univ Technol Limassol Cyprus

ISBN: (纸本)9781665481045

Deploying machine learning (ML) applications over distributed stream processing engines (DSPEs) such as Apache Spark Streaming is a complex procedure that requires extensive tuning along two dimensions. First, DSPEs have a vast array of system configuration parameters (such as degree of parallelism, memory buffer sizes, etc.) that need to be optimized to achieve the desired levels of latency and/or throughput. Second, each ML model has its own set of hyper-parameters that need to be tuned as they significantly impact the overall prediction accuracy of the trained model. These two forms of tuning have been studied extensively in the literature but only in isolation from each other. This position paper identifies the necessity for a combined system and ML model tuning approach based on a thorough experimental study. In particular, experimental results have revealed unexpected and complex interactions between the choices of system configuration and hyper-parameters, and their impact on both application and model performance. These findings open up new research directions in the field of self-managing stream processing systems.

关键词： stream processing machine learning system parameter tuning hyper-parameter tuning

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Challenges of Generalizing machine learning Models in Healthcare

Challenges of Generalizing Machine Learning Models in Health...

引用

18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Kessler, Steven Dewitz, Bastian Sundarara, Santhoshkumar Salinas, Favio Lichtenberg, Artur Schmid, Falko Aubin, Hug Digital Health Lab Düsseldorf University Clinic Düsseldorf Moorenstrasse 5 Düsseldorf Germany

Generalization problems are common in machine learning models, particularly in healthcare applications. This study addresses the issue of real-world generalization and its challenges by analyzing a specific use case: predicting patient readmissions using a Recurrent Neural Network (RNN). Although a previously developed RNN model achieved robust results on the Medical Information Mart for Intensive Care (MIMIC-III) dataset, it showed near-random predictive accuracy when applied to the local hospital’s data (Moazemi et al., 2022). We hypothesize that this discrepancy is due to patient demographics, clinical practices, data collection methods, and healthcare differences in infrastructure. By employing statistical methods and distance metrics for time series, we identified critical disparities in demographic and vital data between the MIMIC and hospital data. These findings highlight possible challenges in developing generalizable machine learning models in healthcare environments and the need to improve not just algorithmic solutions but also the process of measuring and collecting medical data. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive learning

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Leveraging channel capacity of wireless system with multibranch selection combiner impacted by k- μ fading and co-channel interference for quantum machine learning QoS level prediction 4th

Leveraging channel capacity of wireless system with multibra...

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Krstic, Dragana Suljovic, Suad Petrovic, Nenad Dobrilovic, Dalibor Gurjar, Devendra S. Yadav, Suneel Faculty of Electronic Engineering University of Nis Nis Serbia The Academy of Technical Professional Studies Belgrade Belgrade Serbia Technical Faculty "Mihajlo Pupin" University of Novi Sad Zrenjanin Serbia Department of ECE National Institute of Technology Silchar Assam India Department of ECE Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (纸本)9781032426853

This paper considers the general k-μ fading distribution, for which fading model this distribution is proposed. First, an expression for the channel capacity (CC) of multi-branch selection combiner influenced by k-μ small-term fading and co-channel interference (CCI) with k-μ distribution, is derived. k-μ distribution is matched for lineofsight (LOS) conditions in wireless systems. Then, a few figures are plotted to stand out the impact of small-term fading and CCI on the CC. Finally, the CC expression is leveraged for quantum machine learning-based Quality of Service (QoS) level and consumer number predictions inside network modeling, planning and simulation environment. © 2024 The Author(s).

关键词： Channel capacity

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Dementia Prediction Using machine learning

Dementia Prediction Using Machine Learning

引用

2022 international conference on ENTERprise Information Systems, CENTERIS 2022 - international conference on Project MANagement, ProjMAN 2022 and international conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dhakal, Sara Azam, Sami Hasib, Khan Md. Karim, Asif Jonkman, Mirjam Farhan Al Haque, A.S.M. College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Science and Engineering Ahsanullah University of Science Technology Dhaka Bangladesh Department of Electronic Systems Aalborg University Copenhagen Denmark

Dementia is a chronic and degenerative condition, which has become a major health concern among the elderly. With ever-continuing cases of dementia, it has become a very challenging task in the 21st century to provide care for patients with dementia. This paper proposes a framework for the prediction of dementia using the data collected from the OASIS (Open Access Series of Imaging Studies) project which was made available by the Washington University Alzheimer's Disease Research Centre. Different techniques have been implemented for data imputation, pre-processing and data transformation to create suitable data for training the model. machine learning approaches like Adaboost (AB), Decision Tree (DT), Extra Tree (ET), Gradient Boost (GB), K-Nearest Neighbour (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and SVM (Support Vector machine) has been used for a combination of features. These techniques have been applied to the full set of features and features selected from Least Absolute Shrinkage and Selection Operator (LASSO) techniques. A comparison between the accuracy, precision, and other metrics based on the results of the classification algorithms has been provided. The experimental results show that the highest accuracy of 96.77% was obtained by Support Vector machine (SVM) with full features. The proposed methodology is promising and if developed and deployed can be helpful for the rapid assessment of Alzheimer's Disease (AD). © 2023 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Demystifying Semantic Intelligence for Enabling Intelligent Applications 1

引用

2nd international Semantic Intelligence conference (ISIC)

作者： Jain, Sarika Natl Inst Technol Kurukshetra Kurukshetra Haryana India

ISBN: (数字)9789811971266

ISBN: (纸本)9789811971280;9789811971266;9789811971259

An intelligent agent is required to fuse heterogeneous sources of information together for which it should be equipped with both the data-driven (statistical) and knowledge-driven (symbolic) AI disciplines. Semantic Technologies make it possible by creating links between disparate and heterogeneous data. When the data is linked as well as open, it is termed as Linked Open data (LOD). The Symbolic AI and sub-symbolic AI have to go together. The symbolist approach nowadays is manifested as a knowledge graph that advanced statistics and machine learning can run on top of.

关键词： Artificial intelligence machine learning Knowledge graph Linked open data Semantic technologies

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Aggregation Techniques in Federated learning: Comprehensive Survey, Challenges and Opportunities 2

Aggregation Techniques in Federated Learning: Comprehensive ...

引用

2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sah, Mukund Prasad Singh, Amritpal Dr B R Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9781665437899

Federated learning is a new paradigm on the machine learning system that uses the traditional system of machine learning but implements privacy features on top of it. The implementation of federated learning is done in order to increase the privacy of the user and also give them access to their own rich private personalized data. But in the case of classical machine learning the implementation is that it needs all the data and model on one system and in that way we have to get the data of the user and store it in a certain place and then train the model there and get the updated model by training on that data. Let us look at the problems in this system first: the personalized data of the user is being sent to the servers and is used there. Even if the owners of the server are not exploiting the data of users it can be possible by any cyber attacks people can steal the data and use it for their own benefit and in that there is no one to blame for. In order to avoid these data breaches, there can be a solution where we instead to taking the data to the model send the model to the data, that is the core concept of the Federated learning paradigm - in this way we can preserve the privacy of the user and also train our model on the rich personalized data set. © 2022 IEEE.

关键词： machine learning

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Comparative Analysis of Fake News Identification Using machine learning Methods 4th

Comparative Analysis of Fake News Identification Using Machi...

引用

4th international conference on data, engineering, and Applications, IDEA 2022

作者： Patel, Shivangi Singh, Dheeraj Kumar Parmar, Jayshree Parul University Vadodara India

ISBN: (纸本)9789819700363

The usage of social networks is growing exponentially day by day. Users can obtain a lot of information from social networks where some genuine information as well as some misinformation are there that mislead users and may face problematic situations. Therefore, it is mandatory to identify fake content. Thus, the main purpose of this study is to examine the state of the art in the detection of fake content and the contribution of machine learning algorithms such as Random Forest, Support Vector machine, Passive Aggressive Classifier, and Logistic Regression with Term Frequency-Inverse Document Frequency vectorization technique applied for detecting fake content in English and Hindi language efficiently. Performed comparison analysis between algorithms on the FakeNewsNet dataset in English and another dataset in the Hindi language to indicate the effectiveness of classification outcome. The Support Vector machine classifier generated the greatest results out of the four. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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machine learning Approaches to Predict Movie Success 1st

Machine Learning Approaches to Predict Movie Success

引用

1st international conference on machine Intelligence and Emerging Technologies, MIET 2022

作者： Hoque, Md. Afzazul Khan, Md. Mohsin International Islamic University Chittagong Chittagong Bangladesh

ISBN: (纸本)9783031346217

The motion picture industry, commonly known as the film industry, is a huge investment sector. Furthermore, there is a massive amount of data related to movies on the internet. As a result, it became an interesting topic for research and analysis. machine learning is a novel approach to data analysis. Our thesis presents a decision support system for the movie investing sector that employs various machine learning methods. In this context, our approach will assist film investment industries in avoiding investment risks. The algorithm evaluates historical data from numerous sources such as Rotten Tomatoes, IMDb, Box Office Mojo, and Meta Critics to calculate an estimated value of a film’s success rate. The model will forecast movie success based on specific criteria such as the cast and directors, budget, month of release, runtime, several sorts of movie ratings, and movie reviews, and then process that data for categorization using machine-learning algorithms and a few other techniques. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： machine learning

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