检索结果-内蒙古大学图书馆

9th International Workshop on Advanced Analytics and Learning on Temporal data, AALTD 2024

作者： O’Connor, Ciaran Prestwich, Steven Visentin, Andrea SFI CRT in Artificial Intelligence School of Computer Science & IT University College Cork Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Cork Ireland

ISBN: (纸本)9783031770654

Integrating the erratic production of renewable energy into the electricity grid poses numerous challenges. One approach to stabilising market prices and reducing energy losses due to curtailments is the deployment of batteries. Efficient electricity arbitrage is crucial to make investments in storage systems financially viable;trading solutions to achieve this rely on price forecasting techniques. This study delves into the application of Conformal Prediction (CP) techniques, including Ensemble Batch Prediction Intervals (EnbPI) and Sequential Predictive Conformal Inference for Time Series (SPCI), for generating probabilistic forecasts in the Irish electricity market. Recent advancements in CP have addressed temporal considerations inherent in time series forecasting, eliminating the need for exchangeability assumptions. Our study demonstrates that despite potential efficiency trade-offs, CP methods consistently yield precise and reliable prediction intervals, ensuring comprehensive coverage. We assess the impact of CP on the financial results of a simulated trading algorithm. Monetary outcomes achieved with EnbPI and SPCI outperform those of both split CP and traditional quantile regression models, highlighting the practical superiority of CP in electricity price forecasting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Revocable Key-Aggregate Searchable Encryption for Group data Sharing in Cloud-Assisted Industrial IoT

引用

IEEE Internet of Things Journal 2025年第11期12卷 16899-16911页

作者： Zhang, Kai Hu, Xinyi Zhao, Jian Wei, Lifei Ning, Jianting Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Shanghai University of Electric Power College of Electrical Engineering Shanghai China Shanghai Maritime University College of Information Engineering Shanghai China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products-making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in Industrial IoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: (i) the user revocation needs interaction process between data owner and cloud;(ii) the non-revoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves non-interactive user revocation and trusted revoked user management. Technically, the result is non-trivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with re-designing trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with 13.4×~15× time efficiency accelerating for data retrieval and runs more than 16× faster for user revocation. © 2025 IEEE.

关键词： Industrial Internet of Things Cloud computing Aggregates Encryption Blockchains Permission Cryptography data retrieval Production facilities Costs

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Network Traffic data Using Deep Learning 2

Anomaly Detection in Network Traffic Data Using Deep Learnin...

引用

2nd International Conference on Computing and data Analytics, ICCDA 2024

作者： Ramya, S. Selvi, S.Annal Ezhil Bishop Heber Collge Department of Data Science Thiruchirappalli India Bishop Heber College Department of Computer Science Thiruchirappalli India

ISBN: (纸本)9798331542573

In the Tech Renaissance, spotting network traffic anomalies has become a game changer for security. With the rapid growth of network traffic and the increasing frequency of cyberattacks, detecting anomalies and intrusions in real-time is critical. The growing complexity and volume of modern traffic patterns have rendered traditional security measures inadequate for identifying sophisticated cyber threats. This research aims to detect unusual behaviors in network traffic that could signal security breaches, misuse, or attacks. Current detection methods, like rule-based intrusion detection systems (IDS) and signature-based approaches, depend on known threat signatures and predefined rules. These methods are limited in detecting new or unknown threats, prone to high false positive rates, and lack the adaptability needed to respond to evolving threats. To address these limitations, this paper proposes the use of advanced machine learning and deep learning techniques, such as autoencoders, RNNs, CNNs, and GANs, which can learn from evolving traffic patterns and improve detection accuracy. By adapting to both known and unknown anomalies, these models significantly reduce false positives and enhance the overall security of network infrastructures. Organizations and businesses will benefit from improved detection rates, a reduced risk of cyberattacks, and enhanced protection of sensitive data, leading to a more secure and resilient network environment. The proposed models anticipate a notable reduction in false positives and an increase in anomaly detection accuracy, making them a more reliable solution than traditional methods. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model 2

Word-of-Mouth Promotion: How to Attract Consistent Consumers...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Gunavathi, R. Vijayalakshmi, S. Savithri, M. Thamarai Selvi, S.B. Christ University School of Data Science India K.S.Rangasamy College of Technology Computer Science and Engineering India

ISBN: (纸本)9798350384277

The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. © 2024 IEEE.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Progressive Index Modulation Based CH Selection in ICIC Scheme for Multi-Hop WSN 11

Progressive Index Modulation Based CH Selection in ICIC Sche...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Monika, P. Vijayalakshmi, S. Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Sri Ramakrishna College of Arts and Science for Women Department of Data Analytics Coimbatore India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350357

Wireless Sensor network is the communication network in which the sensor nodes are located in various parts of the network and the base station in same network. The sensor nodes gather the data and communicate gathered information to the base station. The major issue in the transmission process of WSN is energy consumption. Some techniques and algorithms are suggested in many researches for reducing energy consumption to make the node to sustain in network for longer duration. This paper proposes the progressive index modulation ICIC strategy for cluster head selection and used indexing transmitting data. In intra cluster mode, cluster head is chosen based on probability factor and minimized energy utilized route is identified and indexing is initialized for that route in intra clustering scheme. The simulation is performed using different metrics like packet delivery rate, number of dead nodes and energy utilization. All the above metrics are evaluated in two different strategies like number of rounds and number of nodes. Proposed evaluation results provides better results when compared with ICIC strategy and it helps to utilize less energy and better data transmission. © 2024 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering 24

PrimeComposer: Faster Progressively Combined Diffusion for I...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yibin Zhang, Weizhong Zheng, Jianwei Jin, Cheng School of Computer Science Fudan University Shanghai China School of Data Science Fudan University Shanghai China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798400706868

Image composition involves seamlessly integrating given objects into a specific visual context. Current training-free methods rely on composing attention weights from several samplers to guide the generator. However, since these weights are derived from disparate contexts, their combination leads to coherence confusion and loss of appearance information. These issues worsen with their excessive focus on background generation, even when unnecessary in this task. This not only impedes their swift implementation but also compromises foreground generation quality. Moreover, these methods introduce unwanted artifacts in the transition area. In this paper, we formulate image composition as a subject-based local editing task, solely focusing on foreground generation. At each step, the edited foreground is combined with the noisy background to maintain scene consistency. To address the remaining issues, we propose PrimeComposer, a faster training-free diffuser that composites the images by well-designed attention steering across different noise levels. This steering is predominantly achieved by our Correlation Diffuser, utilizing its self-attention layers at each step. Within these layers, the synthesized subject interacts with both the referenced object and background, capturing intricate details and coherent relationships. This prior information is encoded into the attention weights, which are then integrated into the self-attention layers of the generator to guide the synthesis process. Besides, we introduce a Region-constrained Cross-Attention to confine the impact of specific subject-related tokens to desired regions, addressing the unwanted artifacts shown in the prior method thereby further improving the coherence in the transition area. Our method exhibits the fastest inference efficiency and extensive experiments demonstrate our superiority both qualitatively and quantitatively. The code is available at https://***/CodeGoat24/PrimeComposer. © 2024 ACM.

关键词： Diffusers (optical)

来源：评论

学校读者我要写书评

暂无评论

A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Williams, Lorrayya Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.;(ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Many-Objective Evolutionary Algorithm Based on Improved Dominance Relation 18th

Many-Objective Evolutionary Algorithm Based on Improved Domi...

引用

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pei, Zhen-Yu Yang, Ze-Yuan Liu, Geng-Geng Pan, Jeng-Shyang College of Computer and Data Science Fuzhou University Fujian350116 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong266590 China

ISBN: (纸本)9789819906048

With the increase of the number of objectives in multi-objective optimization problems, the proportion of non-dominated individuals in the population will increase exponentially, and the multi-objective evolutionary algorithm based on the traditional Pareto dominance relation lacks sufficient selection pressure, which degenerates the convergence of the algorithm. In addition, the effectiveness of existing density estimation approaches to distinguish the individuals in high-dimensional space decreases, resulting in poor performance in maintaining diversity. In view of the above problems, this paper proposes a many-objective evolutionary algorithm based on improved dominance relation. First, an improved dominance relation is proposed, the individual with the best convergence is kept in each niche, which increases the selection pressure. Second, a dynamic fitness function is proposed so that individuals with better convergence and diversity can be retained adaptively. Third, an adaptive student distribution crossover operator is proposed, which further improves the convergence and diversity of our algorithm. The experimental results show that the population obtained by MaOEA-IDR is better than the existing algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Drug Recommendation based on drug details and Optimization using Bee Algorithm

Drug Recommendation based on drug details and Optimization u...

引用

2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Ariyachaipong, Kornpong Senaluang, Pattayapon Songmuang, Pokpong Kongkachandra, Rachada Thammasat Universtiy Data Science And Innovation College Of Interdisciplinary Studies Pathum Thani Thailand Thammasat Universtiy Department Of Computer Science Bangkok Thailand Thammasat Universtiy Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand

ISBN: (纸本)9798350370096

In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aims to address the challenge of medication selection in resource-constrained settings by providing physicians with informed recommendations tailored to patient *** proposed system utilizes drug data sourced from ***, encompassing approximately 34,284 drug entries. To ensure high-quality recommendations, the data undergoes a rigorous preprocessing phase involving null value imputation and feature extraction using techniques like TF-IDF. Following preprocessing, a combination of Drug Recommendation with cosine similarity and the Bee Algorithm is employed. Cosine similarity establishes a baseline for drug similarity, while the Bee Algorithm optimizes the selection process by considering additional factors beyond just similarity, such as potential side effects and cost-effectiveness. © 2024 IEEE.

关键词： Recommender systems

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：