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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40628 条记录，以下是4901-4910 订阅

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Client Availability Aware Privacy-Preserving Federated learning for Language Models 26

Client Availability Aware Privacy-Preserving Federated Learn...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Yun, Sheng Bhuiyan, Zakirul Alam Al Hasib Sadi, Md Taufiq Fordham University Department of Computer and Information Sciences New York10458 United States North South University Department of Electrical & Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Language Models (LMs) have significantly advanced various natural language processing applications but pose challenges related to data privacy and the need for massive computational resources. Federated learning (FL) decentralizes the training across multiple devices, enhancing data privacy. Yet, real-world FL faces complexities stemming from inconsistent client availability, especially in devices affected by factors like usage patterns and timezone correlations. Recent frameworks like CA-Fed have addressed client availability discrepancies, but they are less effective when applied to the nuanced domain of LMs. In this context, we introduce Fed-Cab, a novel Client Availability Aware Privacy-preserving Federated learning framework tailored for LMs. Leveraging finite-state Markov chains, Fed-Cab simulates real-world client availability. Our framework introduces a novel dynamic weighting mechanism that understands and capitalizes on client availability, placing emphasis on clients that aid optimal convergence. By managing client contributions and introducing an innovative communication design, Fed-Cab efficiently captures client availability, determines weights, and dynamically selects contributors and updates models. Our results indicate that Fed-Cab not only maintains high accuracy but also excels in training speed and consistency over time. © 2023 IEEE.

关键词： Client Availability Client Selection Federated learning Language Models

来源：评论

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Credit Card Default Prediction Using Machine learning Models 3

Credit Card Default Prediction Using Machine Learning Models

引用

3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Yash, Hritwiz Affan Saurav, Kumar Dhanda, Sumit Singh Galgotias University Dept of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350303360

Credit card default prediction is a crucial task for financial institutions. Accurately predicting the probability of default can help banks and credit card companies to better manage risk, reduce losses, and optimize their lending strategies. In this paper, a machine learning-based strategy to predict credit card default is proposed. A dataset of credit card clients from Taiwan has been utilized for the same. Various machine learning algorithms, including logistic regression, SVM, and ANN, have been used to develop predictive models. The models are evaluated under various performance metrics, including accuracy, F1 score, etc., and a set of feature scaling techniques that help to give accurate results, SVC algorithm outperforms other algorithms with an accuracy of 82.03%. Findings suggests that machine learning techniques can be used to develop accurate credit card default prediction models that can help financial institutions make informed decisions. © 2023 IEEE.

关键词： Forecasting

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Performance Comparison of Machine learning Algorithms for Identification of Physiological Maturity of Pineapple using Optical Property 15

Performance Comparison of Machine Learning Algorithms for Id...

引用

15th international conference on computer and Automation engineering, ICCAE 2023

作者： Lapcharoensuk, Ravipat Phannote, Noppadon Kasetyangyunsapa, Dimae King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Agricultural Engineering Bangkok Thailand Rajamangala University of Technology Division of Agricultural Engineering School of Engineering Tawan-ok Chonburi Thailand

ISBN: (纸本)9798350396225

Pineapple is important fruit of Thailand which is consumed in its fresh state or in processed products. Typically, harvested dates affected to quality of pineapple fresh. Identification of pineapple harvested dates at raw material receiving state in factory is very difficult. This research aims to determination of appropriate machine learning algorithm for Identifying maturity of pineapple using optical property. Color of pineapple fruits and fresh was measured by portable colorimeter on CIE system (L*, a∗ and b∗ values). The ten algorithms were fit to the training set including naive Bayes (NB), linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB) and adaptive boosting (AB). The best model for pineapple fruit were established from ANN while DT showed highest performance for pineapple fresh. The accuracy of ANN and DT for fruit and fresh models were 83 and 92% respectively. This finding point is novel technique for identification of pineapple according to harvested dates which it can apply to quality control and assurance in pineapple industries. © 2023 IEEE.

关键词： Decision trees

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Understanding the Terminology Used in Malware Analysis 3

Understanding the Terminology Used in Malware Analysis

引用

3rd IEEE international conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Jha, Manasi Kaur, Harpreet Lovely Professional University School of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350397819

With the ongoing digitalization period coming into picture and quick progression, focusing on casualties by means of pernicious techniques like taking advantage of weakness or sending payload has acquired a spike. Malwares and pernicious assaults by means of them have flooded over the course of the last a few years. When it comes to malwares this era, threat actors are becoming sneaky by using crypting services to achieve complete undetectibility. In this paper, a meticulous study has been led on malware and terms related to malware, sorts of examination that is possible to analyze using different available tools on surface web, group of malwares, top malware attacks for the past 10 years, common infection methods used by threat actors to spread malware and how machine learning can be utilized for malware investigation and the future extent of reconciliation of machine learning for compelling examination and foresee more future missions. This paper will uncover all the terms related to malware and make a clear understanding of the terms. © 2023 IEEE.

关键词： Malware

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Feature Extraction Techniques in Sentiment Analysis Using Deep learning: A Review 3

Feature Extraction Techniques in Sentiment Analysis Using De...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Gawali, Anita Diliprao Gunjal, Baisa Laxman MET's Institute of Engineering Savitribai Phule Pune University Department of Computer Engineering Bhujbal Knowledge City Nashik Pune India

ISBN: (纸本)9798350322842

Text mining and information retrieval are fundamentally dependent on the selection of text feature items. Features must be created manually for use in traditional feature extraction methods. While it may take a lot of effort to manually build an effective feature, deep learning makes it possible to automatically learn new representations of successful features from training data in the context of new applications. Text mining has made a lot of progress thanks to deep learning, which is a new way to find features. Deep learning is different from more traditional methods because it automatically learns features from large datasets, while the latter adopts handcrafted features, which rely heavily on the prior knowledge of designers and thus make it nearly impossible to take advantage of large datasets. Feature representation in deep learning may be automatically taught using big data, which typically consists of millions of parameters. This thesis starts with a brief overview of the most common ways to extract text features. It then talks about the widespread use of deep learning methods in text feature extraction and their applications. Finally, it makes some predictions about the future of deep learning's role in feature extraction. © 2023 IEEE.

关键词： Feature extraction

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Network-Constrained Reinforcement learning for Optimal EV Charging Control 14

Network-Constrained Reinforcement Learning for Optimal EV Ch...

引用

14th IEEE international conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Wu, Tong Scaglione, Anna Surani, Adrian Petru Arnold, Daniel Peisert, Sean Cornell University Department of Electrical and Computer Engineering Cornell Tech 10044 United States Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9781665455541

This paper introduces a comprehensive control model that integrates aggregate electric vehicle (EV) charging demand with power grid systems operations, capitalizing on the flexible nature of EV charging. This innovative approach allows us to model and manage electrical loads in a scalable manner. The main contribution is the study of a constrained reinforcement learning (CRL) method for the predictive control of optimal power flow, paired with EV charging control. The CRL-based control method operates with the understanding that future EV arrivals are uncertain, while ensuring the feasibility of control actions. Our case studies, conducted on IEEE standard systems, highlight the superior performance of our approach that dynamically adapts to the evolving EV environment while consistently upholding safety constraints. © 2023 IEEE.

关键词： Reinforcement learning

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Hand Sign Classification Techniques Using Supervised and Unsupervised learning Algorithms

Hand Sign Classification Techniques Using Supervised and Uns...

引用

2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Harshitha, J L V Sri Saranya, N. Sruthi, Yekkitilli Srithar, S. Chandra, J Vijaya Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350348217

Gesture recognition is becoming a sore subject in computer vision captivating the idea of social interaction not only between humans but also create ergonomic systems that control devices ranging from time-of-flight cameras and controlling vehicles to virtual reality. Even though it is gaining ground recently, fast and robust recognition remains an unsolved problem. To our understanding, we are utilizing the Leap motion sensor captured near-infrared image dataset for gesture identification, which tides over word-level sign recognition encompassing a diverse set of hand gestures. We have suggested the approach of extracting the gesture from the image using PCA and image segmentation followed by the feature extraction stage. In this paper, we have analyzed and differentiated various methods of hand recognition counting Random forests, stochastic gradient descent, Naive Bayes, Decision tree, and Logistic regression algorithms and exploited our results. © 2023 IEEE.

关键词： Palmprint recognition

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Student Activity Recognition in Classroom Environments Using Transfer learning

Student Activity Recognition in Classroom Environments Using...

引用

2023 international conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Deshpande, Anagha Deshpande, Vedant School of Electronics and Communication Engineering Dr. Vishwanath Karad Mit World Peace University Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350313796

The recent advances in artificial intelligence and deep learning facilitate automation in various applications including home automation, smart surveillance systems, and healthcare among others. Human Activity Recognition is one of its emerging applications, which can be implemented in a classroom environment to enhance safety, efficiency, and overall educational quality. This paper proposes a system for detecting and recognizing the activities of students in a classroom environment. The dataset has been structured and recorded by the authors since a standard dataset for this task was not available at the time of this study. Transfer learning, a widely adopted method within the field of deep learning, has proven to be helpful in complex tasks like image and video processing. Pretrained models including VGG-16, ResNet-50, InceptionV3, and Xception are used for feature extraction and classification tasks. Xception achieved an accuracy of 93 %, on the novel classroom dataset, outperforming the other three models in consideration. The system proposed in this study aims to introduce a safer and more productive learning environment for students and educators. © 2023 IEEE.

关键词： Students

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Machine and Deep learning Approaches For Brain Tumor Identification: Technologies, Applications, and Future Directions

Machine and Deep Learning Approaches For Brain Tumor Identif...

引用

2023 international conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Verma, Vikram Aggarwal, Alankrita Kumar, Tajinder Chandigarh University Cse Department India Piet Samlakha Cse Department Panipat India Department of Computer Science and Engineering Mohali India Jmieti Department of Cse Department Radaur India

ISBN: (纸本)9798350323917

Brain tumor classification and detection utilizing machine learning and deep learning are increasingly becoming the focus of medical image analysis research. Thanks to the advancement of modern imaging technology and machine learning algorithms, it is now possible to accurately and efficiently classify and diagnose brain tumors using a range of methodologies. Machine learning and deep learning algorithms are currently utilized to assess medical images and categorize tumors based on traits such as shape, size, and texture. Convolutional neural networks (CNNs), a popular deep learning technique, are utilized to identify brain tumors in medical pictures precisely. These models were created using large datasets of brain MRI scans and are incredibly accurate in spotting the presence of cancer. These models have been tested and trained using a variety of freely available datasets, such as the Brain Tumour Segmentation (BraTS) dataset and the LGG/GBM dataset. Researchers have developed several methods for preprocessing the data, increasing the dataset, and fine-tuning the models to improve the models' accuracy further. Overall, categorizing and identifying brain tumors using machine learning and deep learning is a rapidly growing field promising to enhance patient outcomes. By accurately and successfully identifying brain tumors, these techniques can lead to earlier detection and more tailored treatments, ultimately improving patient outcomes. The categorization and detection of brain tumors have been enhanced thanks to machine learning and deep learning on various datasets. © 2023 IEEE.

关键词： Convolutional neural networks

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Boosting Image Classification Accuracy Leveraging Finer Grained Labels 2

Boosting Image Classification Accuracy Leveraging Finer Grai...

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2nd international conference on Image Processing, computer Vision and Machine learning, ICICML 2023

作者： Zhu, Lei Nanjing University Department of Software Engineering Nanjing210000 China

ISBN: (纸本)9798350331417

As the landscape of deep learning has evolved rapidly, numerous models and methodologies have emerged, revolutionizing the domain of image classification. In recent years, OpenAI's Contrastive Language-Image Pre-Training (CLIP) model, which uniquely bridges visual and textual information, has demonstrated robust generalization across diverse tasks, presenting fresh avenues and opportunities for image classification. Building upon the capabilities of the CLIP model, this research further explores the possibility that finer grained labels may help improve the accuracy of image classification. The proposed method is divided into three steps. First, determine existing or manually annotated sub-class labels to capture nuanced details within primary categories. Second, use CLIP as a feature extractor, augmented with a fully connected layer. This setup facilitates supervised classification, leveraging the granularity of the identified sub-class labels. Third, the classified sub-labels are mapped back to their parent categories, resulting in the final prediction. By introducing and combining the precision of finer-grained labels with CLIP's robust architecture, this method offers a promising avenue for bolstering classification accuracy. Code is available at https://***/24kcqsn/Image-Classification-Leveraging-Finer-Grained-Labels. © 2023 IEEE.

关键词： deep learning fine grained labels image classification

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