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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

共 20366 条记录，以下是731-740 订阅

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid 25

Large Capacity Image Watermarking Model using SWT and Laplac...

引用

25th IEEE International conferences on High Performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Baowei Yang, Fan Wu, Yufeng Dai, Changyu Huang, Wenjue Zhao, Xingyuan Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350330014

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. this is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance. © 2023 IEEE.

关键词： Deep neural networks

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Machine Learning Approaches to Predict thyroid Cancer Recurrence: A Comparative Study 9

Machine Learning Approaches to Predict Thyroid Cancer Recurr...

引用

9th International conference on Computer Science and engineering, UBMK 2024

作者： Ozturk, Candide Sagir, Ozgur Vural, Ulas Kocaeli Health and Technology University Faculty of Engineering and Natural Sciences Kocaeli Turkey

thyroid cancer recurrences have significant impacts on patients9; health. the current clinical methods have limited prediction accuracy because of inadequate incorporation of individual patient variability. therefo... 详细信息

ISBN: (纸本)9798350365887

thyroid cancer recurrences have significant impacts on patients' health. the current clinical methods have limited prediction accuracy because of inadequate incorporation of individual patient variability. therefore, there is a need for advanced techniques that can help medical experts to make accurate and reliable predictions. Recent efforts on the topic are focused on machine learning algorithms. this study aims to assess the performance of these algorithms for predicting thyroid cancer recurrence. Four prediction models are trained using Gradient Boosting, Random Forest, XGBoost, and AdaBoost algorithms. the performances of the models are evaluated with metrics such as accuracy, F1 score, F-05 score, and area under the ROC curve (AUC). Experiments with the K-Fold (5-fold) method have revealed how effective these models can be in the prediction of thyroid cancer recurrence. the performance of the machine learning models was evalu-ated using accuracy, Fl-score, F05-score, and AUC score metrics. the XGBoost model outperformed others, achieving an accuracy of 95,3%, Fl-score of 91,8%, F05-score of 91,9%, and AUC score of 98,2%. these results show that machine learning-based solutions may contribute to incorporating more accurate and individualized treatment decisions in clinical practice. © 2024 IEEE.

关键词： Prediction models

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Cloud based Daily Periodic Inspection Dozer Komatsu D31P Using Google Appsheet Development Platform 9

Cloud Based Daily Periodic Inspection Dozer Komatsu D31P Usi...

引用

9th International conference on Wireless and Telematics, ICWT 2023

作者： Kurniawan, Aditya Wardani, Kholilatul Zaki Hamidi, Eki Ahmad Akademi Teknik Alat Berat Indonesia Heavy Equipment Engineering Department Malang Indonesia Politeknik Kota Malang Telecommunication Engineering Department Malang Indonesia Uin Sunan Gunung Electrical Engineering Department Djati Indonesia

ISBN: (纸本)9798350305029

this research paper explores the development of a cloud-based daily periodic inspection system for the Komatsu D31P dozer using the Google AppSheet development platform. the objective is to leverage the capabilities of AppSheet to create a user-friendly, efficient, and digitally-driven solution for conducting daily inspections, ensuring equipment safety, and streamlining maintenance processes. the paper outlines the design, implementation, and benefits of the developed system, highlighting the potential impact on enhancing equipment performance and maintenance efficiency and also opening new possibility to further develop smart maintenance management analysis and decision making software such as AI driven maintenance management and others. the overall user experience is based on current questionnaire shows that the system migration from paper based, manual data collection to cloud based, real-time data collection is indicating a positive response from the user or PI examiner. © 2023 IEEE.

关键词： Decision making

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A Secure and Efficient Privacy Scheme for Location-based Services in Cloud Environments 24

A Secure and Efficient Privacy Scheme for Location-Based Ser...

引用

9th International conference on Cloud Computing and Internet of things, CCIOT 2024

作者： Pu, Tongyu Zeng, Kai Bai, Fenhua Zhang, Chi Zhang, Xiaohui Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China

ISBN: (纸本)9798400717161

Preserving privacy has become crucial in the field of location-based services (LBS) to leverage their full potential while protecting users from untrustworthy LBS providers. Given that LBS users typically use resource-constrained mobile devices, many existing privacy-preserving methods focus on anonymization. However, these methods have certain limitations regarding privacy protection and efficiency. this paper introduces a novel Privacy-preserving LBS scheme to address these issues. We develop an efficient query mechanism where users send encrypted distance queries to the server. the server then employs a PFR-tree and homomorphic encryption to filter Points of Interest (POIs) in a hierarchical manner. the PFR-tree quickly eliminates irrelevant regions based on their locations, minimizing unnecessary computations and enhancing query efficiency. Experimental results indicate that this PFR-tree-based scheme significantly reduces query time compared to traditional methods without a PFR tree, especially as the number of POIs increases, demonstrating superior performance and efficiency in scenarios with many POIs. this approach effectively meets privacy protection needs for indoor navigation in cloud environments while maintaining high efficiency and security. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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AI for Smart Agriculture - A Deep Learning based Turmeric Leaf Disease Detection 9

AI for Smart Agriculture - A Deep Learning Based Turmeric Le...

引用

9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Selvaraj, R. Devasena, M. S. Geetha Satheesh, T. Sathishkumar, C. Dr.N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Dr.N.G.P. Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Department of Computer Science and Engineering RVS Technical Campus-Coimbatore Coimbatore India

ISBN: (纸本)9798350377972

Plants is one of the major food sources for world population. In addition to that, some plants and their outcomes are used in food preparation. One of the plants which is widely used in foods and having huge medicinal value is turmeric. Curcuma long the botanically named spice turmeric has large anti-inflammatory properties, antioxidants, etc., the tropical perennial plant is cultivated worldwide. Diseases in plant leaves is one of the serious factors that affects the turmeric cultivation. Leaf disease reduces the production and it requires continuous monitoring to avoid total loss. Manual monitoring is challenging when the crop is planted in large fields. Due to industrial revolution and smart agriculture concepts, artificial intelligence- based disease detection procedure is presented in recent times. thus, in this research work a deep learning-based turmeric leaf disease detection model is presented using AlexNet. the leaf disease classification model is experimented with our own dataset which includes diseases like Leaf Spot Disease, Leaf Blight, Leaf Rot, and Leaf Curl. the experimentation results provide the maximum accuracy of proposed leaf disease classification model as 95.5% which is better than traditional machine learning models. © 2024 IEEE.

关键词： AlexNet classification Deep learning Turmeric Leaf disease

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software Failure Prediction based on thermal Images 9

Software Failure Prediction based on Thermal Images

引用

9th International conference on Dependable Systems and their Applications, DSA 2022

作者： Gong, Yiang Lu, Minyan Kong, Shiyi Yan, Liang The Key Laboratory on Reliability and Environment Engineering Technology China School of Reliability and Systems Engineering Beihang University Beijing China Beijing High Quality of Systems Technology Co. Beijing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software failure prediction is one of the most critical issues in software reliability research. And there are several crucial challenges for software failure prediction, which includes complex data, intrusive monitoring that brings potential risk to the system and monitoring costs. We proposed the FPbTI approach to predict software failure by using thermal images taken from outside of the hardware. In this approach, a data processing method is proposed and a convolutional neural network model is constructed. Additionally, corresponding experimental system was designed to verify the effectiveness of the approach. We experimentally demonstrate that our proposed approach achieves the goal of software failure prediction and shows remarkable performance. © 2022 IEEE.

关键词： software reliability

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Insights into the Potential of Fuzzy Systems for Medical AI Interpretability 16

Insights into the Potential of Fuzzy Systems for Medical AI ...

引用

16th International conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Ouifak, Hafsaa Idri, Ali Faculty of Medical Sciences Mohammed VI Polytechnic University Ben Guerir Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat Morocco

ISBN: (纸本)9789897587160

Machine Learning (ML) solutions have demonstrated significant improvements across various domains. However, the complete integration of ML solutions into critical fields such as medicine is facing one main challenge: interpretability. this study conducts a systematic mapping to investigate primary research focused on the application of fuzzy logic (FL) in enhancing the interpretability of ML black-box models in medical contexts. the mapping covers the period from 1994 to January 2024, resulting in 67 relevant publications from multiple digital libraries. the findings indicate that 60% of selected studies proposed new FL-based interpretability techniques, while 40% of them evaluated existing techniques. Breast cancer emerged as the most frequently studied disease using FL interpretability methods. Additionally, TSK neuro-fuzzy systems were identified as the most employed systems for enhancing interpretability. Future research should aim to address existing limitations, including the challenge of maintaining interpretability in ensemble methods. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Topic Modeling Enhanced Tripartite Graph for Recommendation Using Metapaths 9

Topic Modeling Enhanced Tripartite Graph for Recommendation ...

引用

9th International conference on Computer Science and engineering, UBMK 2024

作者： Yilmaz, Yaren Işlek, Irem Ögüdüc, Şule Gündüz Istanbul Technical University Department of Computer Engineering Istanbul Turkey Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

In this paper, we propose a novel recommendation system approach by using the advantage of item representations with topic modeling. Our proposed model exploits topics as the sub-categories of the items and enriches the heterogeneous user-item graph by transforming it into user-item-topic tripartite graph. Unlike most of the existing methods, the sub-categories are constructed by using only item titles as features which makes it an easy-to-apply methodology that is applicable in real-world systems even if the items do not have description texts. We also utilize meta-paths to traverse the obtained tripartite graph and try to solve the sparsity problem in recommender systems by learning node embeddings using meta-path based Metapath2Vec algorithm. In order to observe the performance of our proposed model, we used Amazon datasets from two different categories and the results show that our model performs 3.8% better than even the closest baseline result when evaluated for both datasets according to the hit ratio. Further analysis verifies that incorporating sub-category information into graph structure yields an increase of recommendation accuracy especially when recommending long-tail items. © 2024 IEEE.

关键词： Real time systems

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Development of Controller Hardware-in-the-Loop Platform-based Battery Management System for Hybrid PV/Battery System 9

Development of Controller Hardware-in-the-Loop Platform-base...

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9th International conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Doan, Duc-Hieu Nguyen, Tuyet-Quynh-Nhu Pham, Huu-thai Trinh, Hoai-An Vu, Van-Phong Truong, Dinh-Nhon Hcmc University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam Saigon University Faculty of Electronics and Telecommunications Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331505042

this paper presents a battery management system (BMS) for a hybrid power system that integrates the lithium-ion battery and the stand-alone photovoltaics (PV) with the purpose of ensuring system safety, meeting the load power demand, and stabilizing the DC bus voltage. In order to implement the proposed system, a hardware-in-the-loop (HIL) platform of Typhoon HIL404 device is employed for designing the hybrid system and testing the BMS control algorithm by using a TMS320F28069 microcontroller. Finally, real-time simulations using Typhoon-HIL software are conducted to validate the system performance and effectiveness of the proposed control strategy. the obtained results illustrate that the proposed hybrid system and control strategy achieve the system effectiveness, energy balancing of the battery system, and the stable DC bus voltage under different working conditions. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

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Recent Challenges, Issues, and Initiatives for Cloud Computing Adoption in Healthcare: A Survey 9

Recent Challenges, Issues, and Initiatives for Cloud Computi...

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9th International conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Al-Tawara, Feda'a Khalifeh, Ala' Princess Sumaya University for Technology Department of Software Engineering Amman Jordan German Jordanian University Department of Software Engineering Amman Jordan

ISBN: (纸本)9798350311402

there are many opportunities and advantages that cloud computing adoption can bring to the healthcare field, as experts believe that it can help improve the level of healthcare services significantly and contribute to its regular and continuous innovation. this paper provides a comprehensive review of the current literature on the common limitations and problems facing the cloud in healthcare. A brief introduction to cloud computing and the benefits of its adoption is also provided, in addition to a review of the most important security protection standards applied in this field. this paper highlights the most important problems and challenges that still exist in cloud-based healthcare systems. Finally, relevant recent research is reviewed and the most recent initiatives that contribute to solving problems for healthcare applications are identified. It was concluded that these solutions and initiatives are not comprehensive in nature as they partially address issues related to data security in the cloud. © 2023 IEEE.

关键词： Health care

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