检索结果-内蒙古大学图书馆

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Economopoulou, Emmanouela-Electra Vonitsanos, Gerasimos Mylonas, Phivos Kanavos, Andreas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9783031632266

In the rapidly evolving technology landscape, smart homes are becoming increasingly common, driven by the demand for integrated management of information and services. However, despite advancements, managing electricity consumption efficiently remains a significant challenge, primarily due to the lack of detailed usage data and the complexity of predicting device behavior. This study addresses these challenges by utilizing the DinRail Cerberus meter for granular data collection on household electricity use and applying the DBSCAN clustering algorithm for unsupervised learning. Our research aims to develop a forecasting system that accurately discerns the operational status of household devices-active or inactive-based on energy consumption patterns. This innovative approach promises to revolutionize energy management in smart homes, offering detailed insights into device usage that facilitate more informed decisions for efficient electricity consumption. © IFIP International Federation for Information Processing 2024.

关键词： Energy management

来源：评论

学校读者我要写书评

暂无评论

An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Enhancing Aviation Efficiency Through Big Data and Machine Learning for Flight Delay Prediction 4th

Enhancing Aviation Efficiency Through Big Data and Machine ...

引用

4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Vonitsanos, Gerasimos Gounaridis, Ioannis Kanavos, Andreas Mylonas, Phivos Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Flight delays pose significant challenges to the aviation industry, leading to increased operational costs and passenger dissatisfaction. This paper explores the use of machine learning (ML) and big data analytics to enhance the accuracy and efficiency of flight delay predictions. Utilizing data from the Federal Aviation Administration (FAA) covering the period from 2018 to 2022, we analyze critical factors influencing delays and develop predictive models employing techniques such as Random Forest, Gradient Boosting Machines, Decision Trees, and k-Nearest Neighbors. Our analysis demonstrates that these ML techniques significantly outperform traditional models, improving the accuracy of delay predictions and thereby supporting airlines in optimizing operational efficiency and enhancing passenger satisfaction. The paper also discusses the practical implementation of these findings in real-time airline operations and outlines future research directions to further improve predictive accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Evaluating Machine Learning Techniques for Enhanced Prediction of Building Energy Consumption 9

Evaluating Machine Learning Techniques for Enhanced Predicti...

引用

9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Mylonas, Phivos University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

Accurate prediction of energy usage is crucial for optimizing resource allocation, enhancing energy efficiency, and reducing environmental impact, pivotal for sustainable development. This study examines electricity consumption in three Cornell University buildings, utilizing advanced machine learning techniques to tackle the challenges of sustainable energy management effectively. We specifically evaluated the performance of Support Vector Machine (SVM), Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) in forecasting electricity usage. Our findings reveal that SVM consistently outperforms the other models across various performance metrics, including accuracy and efficiency. These results provide vital insights into the efficacy of these algorithms in predicting energy consumption, thereby supporting strategic energy management decisions in educational institutions and potentially other similar settings. © 2024 IEEE.

关键词： Sustainable building

来源：评论

学校读者我要写书评

暂无评论

Social Media Insights into Climate Change: Sentiment Analysis Using VADER and RoBERTa 19

Social Media Insights into Climate Change: Sentiment Analysi...

引用

19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Bardis, Georgios Mylonas, Phivos University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798331504502

Sentiment analysis, a critical branch of Natural Language Processing (NLP), is pivotal for uncovering the emotional undertones within textual data, thereby revealing public sentiments on diverse topics. This study conducts a comparative analysis of two prominent sentiment analysis tools - VADER, a lexicon and rule-based approach, and RoBERTa, a transformer-based deep learning model. It focuses on their efficacy in analyzing tweets related to climate change, a topic of global significance that elicits a wide range of public opinions. The nuanced and dynamic nature of social media language poses unique challenges, particularly in contexts such as climate change discussions. We assess how effectively each model discerns positive, neutral, and negative sentiments across different categories of climate change-related tweets, delineated into Pro, Neutral, News, and Anti stances. Our findings indicate that RoBERTa outperforms VADER in capturing contextual nuances and providing detailed sentiment classifications. This detailed capability allows RoBERTa to better reflect the complex public opinions on climate change, offering invaluable insights to policymakers, researchers, and environmental advocates. This study not only aids in better understanding and engaging with public discourse on social media but also highlights the potential of advanced NLP tools in shaping environmental communication and policy formulation. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Variable-Sized Tiling and Rate Allocation for 360° Video Streaming

引用

IEEE Transactions on Vehicular Technology 2024年第5期74卷 6979-6990页

作者： Su, Qichen Huang, Jiawei Li, Weihe Zhong, Ping Gao, Feng Li, Zhaoyi Liu, Jingling Zhou, Shengwen Lu, Bo Song, Zhiming Wang, Jianxin Central South University School of Computer Science and Engineering China University of Edinburgh School of Informatics United Kingdom

In recent years, the quality of 360° video over bandwidth-constrained networks has received a lot of attention. To reduce the bandwidth cost, some tile-based 360 ° video streaming systems split video spatially into variable-sized coarse-grained tiles by grouping areas with the similar quality levels. However, these approaches do not minimize the number of tiles, resulting in considerable overhead in re-encoding the boundaries between small tiles. To improve the coding efficiency of 360° video, we propose a new video tiling scheme called TRAS, by using the convex decomposition of concave polygon. Meanwhile, to maximize user's quality of experience (QoE), we propose a greedy bitrate selection algorithm by considering the tile size and coding efficiency. The experiment results demonstrate that, compared with the state-of-the-art approaches, TRAS reduces the coding overhead by about 18% and significantly improves the video quality by about 5 dB. © 1967-2012 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based knowledge-aware semantic communications for remote driving image transmission

引用

Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

来源：评论

学校读者我要写书评

暂无评论

Muli-Quay Combined Berth and Quay Crane Allocation Using the Cuckoo Search Algorithm 10

Muli-Quay Combined Berth and Quay Crane Allocation Using the...

引用

10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Aslam, Sheraz Michaelides, Michalis P. Herodotou, Herodotos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus

ISBN: (纸本)9789897587030

This study investigates the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) while considering a multi-quay setting. First, a mixed integer linear programming mathematical model is developed based on various constraints and real port settings. Then, the multi-quay combined BAP and QCAP is solved using both the exact method and a metaheuristic optimization method, namely, the cuckoo search algorithm (CSA). This analysis pertains to a one-week planning scenario, utilizing data from a real port. The results of the comparative analysis show that the proposed CSA can provide a near-optimal solution ( Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Towards LLM-Generated Affective Phrase Sets for Text Entry Evaluation 24

Towards LLM-Generated Affective Phrase Sets for Text Entry E...

引用

23rd International Conference on Mobile and Ubiquitous Multimedia, MUM 2024

作者： Komninos, Andreas Simou, Ioulia Fotopoulos, Angelos Michanetzi, Eleftheria Lito Xenos, Michalis Computer Engineering & Informatics Department University of Patras Rio Greece

ISBN: (纸本)9798400712838

We present a novel method to generate affective phrase sets using Large Language Models (LLMs), for use in text entry study transcription tasks. These phrases could be used to adjust participant emotions, and enable performance assessment under various affective conditions. We found that the affective phrases impacted participants' valence, but not arousal or text entry performance. Though partially succesful, our work provides a foundation for exploring the relationship between emotion and text entry performance in lab studies. © 2024 Copyright held by the owner/author(s).

关键词： Text Entry

来源：评论

学校读者我要写书评

暂无评论

Comparison of Elbow, Silhouette and DBI Methods for Clustering Nutritional Status of Toddlers Using K-Means Clustering 12

Comparison of Elbow, Silhouette and DBI Methods for Clusteri...

引用

12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Muharram, Asep Taufik Nalawati, Rizki Elisa Warsuta, Bambang Malik Matin, Iik Muhamad Pradiptyas, Ayres Natanael, Michael Politeknik Negeri Jakarta Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798331528799

Nutritional status is a measure of a person's health condition as seen from the food consumed. The implementation of nutritional monitoring or nutritional surveillance at the Community Health Center begins with data collection and reporting of toddler nutritional status with the Electronic Community-Based Nutrition Recording and Reporting System (E-PPGBM), the recording process must input toddler data from each integrated health post so that the process takes a long time until each integrated health post has measured each toddler. Furthermore, the data is processed and analyzed with Excel to produce a health information report in the form of toddler nutritional status data from each integrated health post which is reported to the health office. In the final process, namely dissemination, namely decision making for follow-up actions to carry out nutritional improvement programs. The nutritional surveillance process still has motivational constraints and performance becomes less than optimal, such as late reporting and coaching because all implementation processes are only carried out by one health center nutrition officer. The health center needs a data clustering system for the nutritional status of toddlers at each integrated health post to facilitate the decision-making process and monitoring of the nutritional health of each integrated health post so that information can be obtained to help nutrition officers prioritize integrated health posts for nutritional improvement service programs in order to achieve improvements in the nutritional health of toddlers at the Abadijaya Health Center. Evaluation of the number of optimal clusters using the Elbow, Silhouette, and Davies Bouldin Index (DBI) methods. The Elbow method obtained 4 optimal clusters, while the Silhouette method obtained 2 optimal clusters and the Davies Bouldin Index (DBI) obtained 2 clusters. © 2024 IEEE.

关键词： Electronic health record

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：