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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7157 条记录，以下是841-850 订阅

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Real Estate Price Predictor - A Literature Review 11

Real Estate Price Predictor - A Literature Review

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Verma, Abhinav Deepu, Satyam Kumar Bajaj, Parveen Chandigarh University Department of Computer Science and Engineering Sahibzada Ajit Singh Nagar Punjab 140413 India

ISBN: (纸本)9798350318159

the expectation of property prices stands as a vital pursuit within the real estate geography, impacting opinions of buyers, investors, and assiduity professionals likewise. this review paper navigates the multifaceted realm of property price prediction by illuminating its significance and checking the different methodologies and statistical ways employed. the conflation of this converse extends a palpable frame for casing inventors and experimenters, enabling them to navigate the maze of attributes bolstering house prices and offering perceptive recommendations for apt machine learning models. As a capstone, this review underscores the indispensability of nuanced position understanding and advanced prediction methodologies, therefore fortifying the precision of property price predictions within the ever-evolving real estate terrain. © 2023 IEEE.

关键词： Housing

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Enhancing Transfer learning of LLMs through Fine-Tuning on Task-Related Corpora for automated Short-Answer Grading 22

Enhancing Transfer Learning of LLMs through Fine-Tuning on T...

引用

22nd international conference on Machine learning and Applications (ICMLA)

作者： Kazi, Nazmul Kahanda, Indika Univ North Florida Sch Comp Jacksonville FL 32224 USA

ISBN: (纸本)9798350345346

automated short-answer grading (ASAG) is a crucial element of any intelligent tutoring platform. Machine learning (ML) has shown great promise for ASAG. However, this task remains challenging even for Deep learning (DL) approaches and Large Language Models (LLMs), requiring semantic inference and textual entailment recognition. the SemEval-2013 Task 7, the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, is a benchmark widely used for research on ASAG. the SciEntsBank data included in this collection contains nearly 11,000 answers to 197 assessment questions in 15 different science domains. Despite the popularity, only a few researchers have explored the potential of DL or LLMs for this task. In this project, we explore the effectiveness of the RoBERTa Large model, an LLM trained on an extensive text corpus for language comprehension. By fine-tuning the model on the Multi-Genre Natural Language Inference (MNLI) corpus for semantic inference and subsequently on the SciEntsBank dataset, with a focus on the 3-way labels of correct, incorrect, and contradictory, we achieved a weighted F1-score of 0.77, 0.72, and 0.72 on unseen answers, questions, and domains, respectively. Notably, our model significantly benefits from fine-tuning on the MNLI corpus, particularly in enhancing its performance on the contradictory class (which constitutes only 10% of the dataset) through transfer learning leading to significant improvements on the more challenging test sets: unseen questions and unseen domains.

关键词： automated Short Answer Grading ASAG Large Language Models Transfer learning Semantic Inference Recognizing Textual Entailment MNLI SemEval SciEntsBank

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Homes Power Profiles Classification and Enhancement Using Machine learning Techniques and PSO Algorithm 11

Homes Power Profiles Classification and Enhancement Using Ma...

引用

11th IEEE international conference on Systems and Control, ICSC 2023

作者： Arab, Marwa Ben Rekik, Mouna Krichen, Lotfi Electrical Engineering Department Sfax3038 Tunisia

ISBN: (纸本)9798350304886

A crucial part of digital solutions, Machine learning (ML) is a subset of artificial intelligence. the main goal of this work is to extract knowledge from data and known experiences to automate decision-making procedures. thus, ML has the potential to change the way we manage and design cities' power demand. Firstly, this paper presents a multiples homes classification into five categories depending mainly on their peak powers, off-peak powers, average powers, and enhanced powers. In this operation, two ML techniques are used to determines the best model: decision tree and K-Nearest Neighbors. Secondly, the paper proposes a Home Energy Management Approach (HEMA) which is divided into three layers. the goal of the approach is to smooth a home power profile that has a high power by integrating Wind Turbine (WT) and Plug-in Electric Vehicle (PEV) using particle swarm optimization (PSO) algorithm. © 2023 IEEE.

关键词： Wind turbines

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DEEP DECLARATIVE DYNAMIC TIME WARPING FOR END-TO-END learning OF ALIGNMENT PAthS 11

DEEP DECLARATIVE DYNAMIC TIME WARPING FOR END-TO-END LEARNIN...

引用

11th international conference on learning Representations, ICLR 2023

作者： Xu, Ming Garg, Sourav Milford, Michael Gould, Stephen Queensland University of Technology Australia Australian National University Australia

this paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead propose a DTW layer based around bi-level optimisation and deep declarative networks, which we name DecDTW. By formulating DTW as a continuous, inequality constrained optimisation problem, we can compute gradients for the solution of the optimal alignment (with respect to the underlying time series) using implicit differentiation. An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself. this naturally occurs, for instance, when learning to improve the accuracy of predicted alignments against ground truth alignments. We evaluate DecDTW on two such applications, namely the audio-to-score alignment task in music information retrieval and the visual place recognition task in robotics, demonstrating state-of-the-art results in both. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Alignment

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Machine learning solutions with MediaPipe 11

Machine Learning solutions with MediaPipe

引用

11th international conference on Software Process Improvement (CIMPS) - Applications in Software engineering

作者： Quinonez, Yadira Lizarraga, Carmen Aguayo, Raquel Univ Autonoma Sinaloa Fac Informat Mazatlan Mazatlan Sinaloa Mexico

ISBN: (纸本)9798350398960

this document introduces the basic concepts of Artificial Intelligence (AI) and Machine learning (ML), and some typical applications are mentioned;It also describes some of the most used AI platforms to develop projects using ML algorithms. Specifically, the implementation of Machine learning solutions is addressed using the MediaPipe framework developed by Google. MediaPipe uses pre-trained models in TensorFlow, OpenCV to manipulate video, and FFmpeg to handle audio data;in addition, it is available for Android, iOS, C++, Python, and JavaScript.

关键词： Artificial Intelligence Machine learning AI platforms MediaPipe

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Advances in intelligent Traffic and Transportation Systems - Proceedings of the 6th international conference on intelligent Traffic and Transportation, ICITT 2022, Incorporating the 6th international conference on Communication and Network Technology, ICCNT 2022

Advances in Intelligent Traffic and Transportation Systems -...

引用

6th international conference on intelligent Traffic and Transportation, ICITT 2022

ISBN: (纸本)9781643683843

the proceedings contain 15 papers. the topics discussed include: e-learning training tool for automated transport systems;a review and analysis of traffic data sources;high-speed railway earthquake early warning testing system based on LabVIEW;distributed vision-based passenger flow monitoring system for light rail networks;connected car platforms, a field trial: are they ready for usage-based insurance?;digital transformation of inland terminals;understanding micromobility user preferences for future infrastructure and policy improvements in Abu Dhabi;compact models for the precedence-constrained minimum-cost arborescence problem;simulation analysis of slug dispersion process during bypass pigging process in vertical riser;user acceptance factors of usage-based insurance;and mathematical models for order batching and assignment in a deep-frozen warehouse.

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Integrating Multiscale Mapper Features with Machine learning Techniques to Improve Breast Cancer Classification Accuracy 11

Integrating Multiscale Mapper Features with Machine Learning...

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11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Netshifhire, Rabelani Visaya, Maria Vivien University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg South Africa University of Johannesburg Department of Mathematics and Applied Mathematics Johannesburg South Africa DSI-NRF CoE-MaSS Johannesburg South Africa

ISBN: (纸本)9798350367300

Breast cancer classification is critical for early detection and treatment planning. the complexity of breast cancer data poses feature engineering challenges for conventional machine learning (ML) methods, which often fail to capture these *** employ the Mapper algorithm, a topological data analysis (TDA) tool, for feature engineering. Two variants of Mapper algorithm are utilized, namely Singlescale Mapper (SM) and Multiscale Mapper (MM). SM is constrained by scale sensitivity, dimensionality limitations, and parameter tuning challenges. MM enhance SM by capturing topological features at multiple resolutions, offering a robust hierarchical view of *** the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, three feature sets namely conventional, SM-derived, and MM-derived features are evaluated across various selected ML models to assess their potential to enhance classification *** demonstrated that MM-derived features integrated with ML models improved breast cancer classification accuracy, outperforming both conventional and SM-derived features. this findings highlight the potential of TDA's MM as a feature engineering tool to improve breast cancer classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Design Method of intelligent Substation Configuration File Security Control System Based on AOP and MVC Architecture 11

Design Method of Intelligent Substation Configuration File S...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Cheng, Jiafu Yu, Weiguo Liu, Shidan Li, Jin Liu, Wei Tan, Qian Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Guangzhou China Cyg Sunri Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368345

A smart substation configuration file security control system based on AOP and MVC structures is proposed to address the issue of close coupling between traditional intelligent substation configuration file security logic and business logic, resulting in complex and difficult to maintain systems. Firstly, an analysis was conducted on the configuration file process, lifecycle process, and control technology within the intelligent substation. then, based on this, the design models of AOP structure and MVC structure were analyzed separately, and the traditional MVC pattern was improved based on AOP. A configuration file structure based on AOP and MVC structure was designed. Finally, the proposed method was compared and analyzed with the other three methods under the same database conditions through system testing. the results show that the proposed method achieves the minimum average response time (RTT) and memory occupancy rate (MU) under the same experimental conditions, and under large data volume, the response time is not more than 5 seconds, and the memory occupancy rate does not exceed 75%. Its performance meets user requirements. © 2024 IEEE.

关键词： Integrated circuit design

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Network Anomaly Flow Detection Framework Based on Collaborative Cloud-edge in the Smart Grid Network 11

Network Anomaly Flow Detection Framework Based on Collaborat...

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Yang, Xiaolin Zhang, Hao Yang, Kai Lu, Yifei Changzhou Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. Changzhou213004 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350345346

In recent years, the proliferation of Internet of things devices in power grids has exposed these critical infrastructure systems to numerous malicious network attacks. these attacks are characterized by their extensive scale, scope, and potential for harm. Concurrently, smart power grids in China utilize poor-performing domestic devices, owing to policy mandates. Consequently, the centralized deep learning techniques commonly leveraged for anomaly detection on the Internet have limited applicability in power grids. To address this problem, we propose an online anomaly flow detection framework based on cloud-edge collaboration according to the actual distributed situation of power grids. High-performance cloud servers train online learning models, which are then deployed at substations for distributed detection. We evaluate the feasibility of our proposed solution through both offline and online testing. © 2023 IEEE.

关键词： Deep learning

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Prediction of Covid-19 Recovery Chances Using Supervised Machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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