检索结果-内蒙古大学图书馆

35th International Conference on computer Applications in Industry and Engineering, CAINE 2022

作者： Kallisch, Jonas Wunck, Christoph Emden/Leer University of Applied Sciences Germany Department of Computer Science Iowa State University Ames United States OFFIS Institute for Information Technology Oldenburg Germany

This paper presents a prototype for an information exchange system, which allows information exchange between companies without actually sharing data. First, the need for such an intercompany exchange platform is explained and the value for supply chains resulting from such a platform is described. A literature review presents the existing concepts and techniques contributing to the development of an architecture. Finally. The information exchange concept and the prototype implementation are explained in detail. © 2022, EasyChair. All rights reserved.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

引用

2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Intepretative Deep Learning using Domain Adaptation for Fluorescence Spectroscopy

arXiv

引用

arXiv 2024年

作者： Michelucci, Umberto Venturini, Francesca Artificial Intelligence Research and Development TOELT LLC Duebendorf Switzerland Computer Science Department Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland

Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, widely used for applications such as environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due mainly to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, alongside a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper and meaningful insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying relevant spectral bands. This work describes significantly innovative results in the use of deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes. © 2024, CC BY-NC-ND.

关键词： Fluorescence spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics

Handling Non-IID Data in Federated Learning: An Experimental...

引用

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Marc Haller Christian Lenz Robin Nachtigall Feras M. Awayshehl Sadi Alawadi Karlsruhe University of Applied Sciences Karlsruhe Germany Institute of Computer Science Delta The University of Tartu Tartu Estonia Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can negatively impact the performance of global models constructed in federated learning. To address this concern, multiple approaches have been developed. Nonetheless, previous research lacks a cohesive overview and fails to uniformly assess these strategies, resulting in challenges when comparing and choosing relevant options for real-world scenarios. This study presents a structured survey of cutting-edge techniques for handling the Non-IID data, accompanied by proposing a metric to develop a standardized approach for assessing data skew and its harmony with the appropriate approach. The findings affirm the metric's suitability as a heuristic for assessing data skew in distributed datasets without having insight into client data, serving both scientific and practical purposes and thus supporting the selection of handling strategies. This preliminary research establishes the foundation for discussing standardizing methodologies for evaluating data heterogeneity in federated learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An hybrid security framework using internet of things for healthcare system

引用

Network Modeling Analysis in Health Informatics and Bioinformatics 2021年第1期10卷 1-10页

作者： Kumar, S. Satheesh Koti, Manjula Sanjay Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of MCA Sir M. Visvesvaraya Institute of Technology Bangalore India

In recent decades, broad range of Internet of Things (IoT) technologies are deployed such as machine to machine communication, Internet Technologies (IT), robots, smart devices, and wireless sensor networks. IoT is a spectacular technology in the modern world of IT in which the objects can do data transmission and connect using the intranet or internet networks. Data security and privacy is one of the top most challenging issue where several researchers are showing interest that to especially in healthcare domain. For this reason, a hybrid combination of Elliptic Curve Digital Signature Algorithm (ECDSA), Rivest Cipher 4 (RC4), and Secure Hash Algorithm (SHA-256) is proposed for protecting the reliable data which is transmitted from the IoT based healthcare systems. In the proposed model, the ECDSA is used for improving the RC4 in which the encryption of key is processed by ECDSA for performing the XOR operation in RC4. Furthermore, the security is ensured by transforming the incoming data using the SHA-256 technique. Experimental results show that the proposed model outperforms for 10 MB of data in terms of encryption time that obtained as 631 ms, decryption time is 616 ms, and throughput obtained by the proposed models is 11.71 MB/ms when compared with other existing techniques such as ECC+3DES+SHA-256, RC4+AES+SHA-256, and ECC+RC2+SHA-256. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

引用

Energy Reports 2023年 10卷 3113-3124页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Chemical Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems. © 2023

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Research on Large-scale Structured and Unstructured Data Processing based on Large Language Model 24

Research on Large-scale Structured and Unstructured Data Pro...

引用

2024 International Conference on Machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Li, Bohang Jiang, Gaozhe Li, Ningxin Song, Chaoda Department of Data Science Shopee Pte. Ltd. Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Fu Foundation School of Engineering and Applied Science Columbia University United States School of Engineering Department of Computer and Data Sciences United States

ISBN: (纸本)9798400709876

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types. © 2024 ACM.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Optimization Models and Educational Teaching Research in Agricultural Logistics System 2

Optimization Models and Educational Teaching Research in Agr...

引用

2nd IEEE International Conference on Advances in Computing, Communication and applied Informatics, ACCAI 2023

作者： Karthick, S. Kumar, B. Anjanee Sethi, Puneet Singh, Niraj Kr Srivastava, Supriya Prasanth, P Vishnu Srm Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Dadi Institute of Engineering and Technology Department of Electronic and Communication Engineering India Teerthanker Mahaveer University Moradabad India Amity University Amity Institute of Applied Sciences India Shoolini University Faculty of Management Sciences Solan India Mohan Babu University Tirupati India

ISBN: (纸本)9798350315905

A spic and span exhaustive disciplinesare logistics science. It is essential to the headway of the board and creation principles as well as the development of the public economy. To foster a powerful agricultural economy, the undertaking of developing new agricultural corporate substances is vital. The old techniques for agricultural item assembling and circulation are as of now not ready to fulfill the requirements of production network advancement and market informatization. To resolve the issues traditional strategies welcome, investigating new agricultural item creation and conveyance techniques is fundamental. By coordinating agricultural item store network assets, eliminating wasteful connections in the course of agricultural items, reducing expenses, accomplishing discernibility of agricultural items, and guaranteeing the quality and security of agricultural items, we make an advancement system for agricultural item supply chains, drove by enormous information specialist coops. Given this, this article proposes a model for a coordinated administration stage-based agricultural item store network, and it conducts broad research on the logistics activity rationale of the chain to give a hypothetical establishment and information support for the plan and improvement of the administration stage. © 2023 IEEE.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

引用

2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Palagati, Anusha Balan, Santhosh Kumar Arun Joe Babulo, S. Raja, Laxmi Natarajan, K.K. Kalimuthu, Rajkumar Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tamilnadu Tiruchengode India Department of Cyber Security Karpagam Academy of Higher Education Tamilnadu Coimbatore India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798331510299

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naïve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

From Points to Progression: A Scoping Review of Game Elements in Gamification Research with a Content Analysis of 280 Research Papers

引用

Proceedings of the ACM on Human-computer Interaction 2023年第CHI PLAY期7卷 748–768页

作者： Hallifax, Stuart Altmeyer, Maximilian Kölln, Kristina Rauschenberger, Maria Nacke, Lennart E. Stratford School of Interaction Design and Business University of Waterloo WaterlooON Canada School of Computer Science University of Waterloo WaterlooON Canada HCI Games Group Games Institute University of Waterloo WaterlooON Canada Saarland Informatics Campus Saarbrucken Saarland Germany University of Applied Sciences Emden/Leer Emden Germany University of Seville Sevilla Spain

We lack a shared and detailed understanding in gamification of what game elements are. To address this, we provide a scoping review of the last five years of gamification research, focusing primarily on how game elements have been applied and characterized. We retrieved the definitions of game elements from 280 research papers, conducted a content analysis, and identified their features. On the basis of this information, we provide responses regarding the frequently cited game elements, whether they are consistently characterized in the literature, and the frequently stated features of these elements. Our research has identified 15 game elements in the literature, with points, badges, and leaderboards being the most prevalent. As a first step toward clear definitions, we suggest a set of properties to characterize these game elements. The results of our review contribute to the formation of a consensus among gamification scholars about the application and definition of game elements. © 2023 Owner/Author.

关键词： computer programming

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：