检索结果-内蒙古大学图书馆

2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Al Telaq, Burair Hassan Hewahi, Nabil University of Bahrain Big Data Science and Analytics College of Science Sakir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakir Bahrain

ISBN: (纸本)9781665416566

The liver is one of the most vital organs of the human body. There are various disorders of liver that need early treatment by doctors. Early diagnosis and treating the patients are significant to reduce the risk. Healthcare system can benefit from various Machine Learning (ML) models to predict diseases in early stage. The aim of this study is to predict liver disease using different ML models applied on Indian Liver Patient dataset (ILPD). The models used on this work are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Network (ANN) and various versions of Ensemble Learning (EL). The results show that ensemble learning of KNN, RF, and SVM achieved the highest accuracy of 88%. Moreover, ensemble learning of KNN with RF obtained the highest True Positive Rate (TPR) of 99% on negative cases. In addition, this paper found that the Principal Component Analysis (PCA) technique has good impact to increase the accuracy. © 2021 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Exploring Machine Learning Classifiers for Medical datasets

Exploring Machine Learning Classifiers for Medical Datasets

引用

2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Taha, Ranyah Alshakrani, Sara Hewahi, Nabil University of Bahrain Big Data Science and Analytics College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

This research paper studies different classification algorithms with the aim of finding the best Machine Learning (ML) classifiers for medical datasets to help Clinical Decision Support Systems (CDSS). This will help physicians in making more accurate and early diagnoses. Many studies used single medical dataset to evaluate ML classifiers but its unsatisfying and doesn't reflect the capability of the classifier on different medical datasets. In this research paper, eight ML classifiers experimented on five medical datasets gathered from UCI machine learning repository. The Random Forest (RF) classifier obtained the highest accuracy average with 83.822 %, also Logistic regression (LR), Decision Tree (DT) and Gradient Boosting showed similar performance with 81.666%, 81.364%, 81.034%, while AdaBoost and Gaussian Naïve Bayes (GNB) obtained 79.504% and 78.126%, Moreover, the last two classifiers in this research obtained the lower accuracy averages are K Nearest Neighbor (KNN) with 68.208% and Artificial Neural Network (ANN) with 55.49%. © 2021 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization

arXiv

引用

arXiv 2024年

作者： Zhu, Shuchen Kong, Boao Lu, Songtao Huang, Xinmeng Yuan, Kun Peking University China IBM Research United States University of Pennsylvania United States National Engineering Labratory for Big Data Analytics and Applications AI for Science Institute Beijing China

This paper studies decentralized bilevel optimization, in which multiple agents collaborate to solve problems involving nested optimization structures with neighborhood communications. Most existing literature primarily utilizes gradient tracking to mitigate the influence of data heterogeneity, without exploring other well-known heterogeneity-correction techniques such as EXTRA or Exact Diffusion. Additionally, these studies often employ identical decentralized strategies for both upper- and lower-level problems, neglecting to leverage distinct mechanisms across different levels. To address these limitations, this paper proposes SPARKLE, a unified Single-loop Primal-dual AlgoRithm frameworK for decentraLized bilEvel optimization. SPARKLE offers the flexibility to incorporate various heterogeneity-correction strategies into the algorithm. Moreover, SPARKLE allows for different strategies to solve upper- and lower-level problems. We present a unified convergence analysis for SPARKLE, applicable to all its variants, with state-of-the-art convergence rates compared to existing decentralized bilevel algorithms. Our results further reveal that EXTRA and Exact Diffusion are more suitable for decentralized bilevel optimization, and using mixed strategies in bilevel algorithms brings more benefits than relying solely on gradient tracking. © 2024, CC BY.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Tweets during COVID-19 Pandemic Using BLSTM

Sentiment Analysis of Tweets during COVID-19 Pandemic Using ...

引用

2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Tareq, Alaa Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

social media is the new way for people to express and share their thoughts and it plays a huge role in spreading the anxiety widely during the pandemics and Twitter is one of the social media channels with high number of users and daily tweets. Clarifying and understanding what people thinks and their shared opinions during such hard times can reduce the burden on the health systems and entities and redirects the concerned entities by highlighting the areas to spread the awareness in. The aim of this study is to analyze and assess the sentiment of the Tweets shared during the COVID-19 pandemic and testing the Bidirectional Long Term Short Memory (BLTSM) of Recurrent Neural Network (RNN) in predicting the sentiment class which are Negative, Positive and Neutral. The results show slight difference between the positive and negative tweets which needs an attention to spread the awareness and hence, reduce the negativity. Furthermore, the BLSTM predicted the sentiment classes (Negative, Positive and Neutral) and obtained 86.15% accuracy rate. The high accuracy concludes that BLSTM can adapt and predict the sentiment of a text. © 2021 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research

引用

Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology 2022年第1期7卷 365-374页

作者： Sourav Biswas Malay Bhattacharyya Sanghamitra Bandyopadhyay Indian Statistical Institute Kolkata India. University of Calcutta Kolkata India. Machine Intelligence Unit Centre for Artificial Intelligence and Machine Learning Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute Kolkata India.

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.

关键词： Citation Network Graphlet and Graphlet Degree Distribution Multi-scenario Graphs SARS-CoV-2 Topic Similarity Network Topological Analysis

来源：评论

学校读者我要写书评

暂无评论

Urban Sound Classification Using DNN, CNN LSTM a Comparative Approach

Urban Sound Classification Using DNN, CNN LSTM a Comparative...

引用

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Bubashait, Mohamed Hewahi, Nabil Big Data Science and Analytics Master Program College of Science University of Bahrain Sakhier Bahrain University of Bahrain Department of Computer Science College of IT Sakhier Bahrain

ISBN: (纸本)9781665440325

Like air pollution, sound pollution has grown to be a major concern for city residents, designers, and developers. Detecting and recognizing sound types and sources in cities and suburban areas or any environment have become a necessity for the quality of life as well as security. In recent years, researchers have explored many models using Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Neural Network, and different combinations of these techniques, which produced promising results when combined with spectrogram images, or its different variations, to classify urban sounds. This research uses the DNN model performance as a baseline to compare the CNN and LSTM models' performance for classifying urban sound using Mel scale cepstral analysis (MEL) spectrum images using an open-source library called Librosa for sound processing. Models' performance was evaluated using the UrbanSound8k dataset. The CNN model underperformed with an accuracy rate of 87.15% and f1 score of 85.63% compared to both the DNN base model and the LSTM model. In contrast, comparing the LSTM model with CNN, LSTM shows better accuracy performance on test data with 90.15%, and f1 score of 90.15%. © 2021 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Multi-Feature Driver Drowsiness Detection

Multi-Feature Driver Drowsiness Detection

引用

2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Abdulrahman, Hajar Taha, Ranyia Al Haj, Duaa Al-Ammal, Hesham College of Science University of Bahrain Sakhir Big Data Science and Analytics Bahrain College of Information Technology University of Bahrain Sakhir Department of Computer Science Bahrain

ISBN: (纸本)9781665416566

Driver drowsiness is one of the leading causes of deadly car accidents, and therefore has become of great interest in the field of Artificial Intelligence. In this paper, convolutional neural networks (CNNs) are used to determine the state of the driver (i.e. Drowsy or not), based on 4 observed features: open eyes, closed eyes, yawn and no yawn. The data was collected from two open source website, which was severely imbalanced between the open/closed eyes images and yawn/no yawn images, the former two classes being the majority class. Augmentation techniques were applied, and three different model structures were investigated, where the best architecture yielded an accuracy rate of 95%, and a loss of 13%. The paper discusses the success and limitations of these models and possible future work on approaching this problem. © 2021 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Primordial magnetic non-Gaussianity with generic vacua and detection prospects in CMB spectral distortions

arXiv

引用

arXiv 2023年

作者： Bhaumik, Arko Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700 108 India

Assuming a slow-roll inflationary model where conformal invariance of the Maxwell action is broken via a non-minimal kinetic coupling term, we investigate the non-Gaussian three-point cross-correlation function between the primordial curvature perturbation and the primordial magnetic field, under a fairly general choice of initial vacua for both the scalar and the gauge field sectors. Among the possible triangular configurations of the resulting cross-bispectrum, we find that the squeezed limit leads to local-type non-Gaussianity allowing a product form decomposition in terms of the scalar and magnetic power spectra, which is a generic result independent of any specific choice of the initial states. We subsequently explore its detection prospects in the CMB via correlations between pre-recombination µ-type spectral distortions and temperature anisotropies, sourced by such a primordial cross-correlation. Our analysis with several proposed next-generation CMB missions forecasts a low value of the signal-to-noise ratio (SNR) for the µT spectrum if both the vacua are assumed to be pure Bunch-Davies. On the contrary, the SNR may be enhanced significantly for non-Bunch-Davies initial states for the magnetic sector within allowed bounds from current CMB data. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Incentive Mechanism Design for Federated Learning With Dynamic Network Pricing

引用

IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Gao, Lin Huang, Jianwei Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou China Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen Shenzhen518172 China

Federated learning protects users' data privacy by sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under three interaction structures of the participants. We show that the coordinated structure is better than the two uncoordinated structures, as it avoids the selfish behaviors of the network operator and the server;the vertically uncoordinated structure is better than the horizontally uncoordinated structure, as it avoids the interests misalignment between the server and the network operator. We also propose multi-period network pricing to reduce the implementation complexity of dynamic pricing. Numerical results based on real-world datasets show that our proposed mechanisms decrease the server's cost by up to 24.87% and increase the network operator's profit by up to 1245.25%, compared with the state-of-the-art benchmarks. © 2025 IEEE. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media 4

Tuning Hyperparameters of Machine Learning Methods for Afan ...

引用

4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Defersha, Naol Bakala Kekeba, Kula Kaliyaperumal, Karthikeyan College of Electrical and Mechanical Engineering Centre of Excellence for HPC Big Data Analytics Addis Ababa Science and Technology Ethiopia Centre of Excellence for HPC and Big Data Analytics College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Ethiopia Ambo University Institute of Technology Department of Information Technology Ambo Ethiopia

ISBN: (纸本)9781665414470

With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92% values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://***/for further research. © 2021 IEEE.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：