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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Chundong Li, Yue Tianjin University of Technology Tianjin300384 China National Engineering Laboratory for Virus Prevention and Control Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (纸本)9798350331684

Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion. © 2023 IEEE.

关键词： Copyrights

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Automated Skin Cancer Detection using Deep Learning with Self-Attention Mechanism

Automated Skin Cancer Detection using Deep Learning with Sel...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Singh, Himanshi Suganya Devi, K. Gaur, Shraddha Singh Bhattacharjee, Ramanuj National Institute of Technology Silchar Medical Imaging Laboratory Department of Computer Science and Engineering Silchar India

ISBN: (纸本)9798350323917

Skin cancer is a prevalent and potentially fatal disease that affects a large number of individuals worldwide. Detecting skin cancer early is vital for effective treatment and positive patient outcomes. In this research, a deep learning (DL) model is presented that utilizes the VGG19 architecture with self-attention modules to automate skin cancer detection. The proposed model achieved an impressive overall accuracy of 91.4% on HAM10000 dataset and outperformed several standard classification models, proving that incorporating self-attention modules into convolutional neural networks can significantly enhance skin cancer detection systems' efficiency. Nonetheless, the model has certain limitations, such as a limited dataset and lower accuracy for the melanoma class. Thus, further studies are required to improve the model's performance, which could potentially save lives by allowing for early detection and treatment of melanoma. This proposed approach has shown promising results for automating skin cancer detection, and the potential impact of this research is significant for developing more accurate and reliable skin cancer detection systems. © 2023 IEEE.

关键词： Diseases

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Variational Inference Using High Speed Photonic Neural Networks

Variational Inference Using High Speed Photonic Neural Netwo...

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2024 Frontiers in Optics, FiO 2024

作者： Garofolo, James Shi, Taichu Prucnal, Paul Wu, Ben Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Lightwave Communications Laboratory Department of Electrical Engineering PrincetonNJ08544 United States

We propose a method of performing variational inference using high speed photonic neural accelerators. This method incurs no slowdown compared to deterministic photonic inference, affecting only the power consumption ... 详细信息

关键词： Photonics

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An Intelligent Condition Monitoring Scheme For Photovoltaic Systems using Programmable Logic Controller 3

An Intelligent Condition Monitoring Scheme For Photovoltaic ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Nedaei, Amir Eskandari, Aref Pourshahbaz, Nima Salehpour, Sherko Parvin, Parviz Livera, Andreas Makrides, George Aghaei, Mohammadreza Department of Electrical Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Department of Physics and Energy Engineering Tehran Iran PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus Department of Ocean Operations and Civil Engineering Ålesund Norway Freiburg Germany

ISBN: (纸本)9798350375923

Environmental and other stresses are constantly affecting photovoltaic (PV) components which can end in various faults. The anomalous condition can reduce the PV output power or even result in a calamitous fire hazard. Hence, PV systems must be monitored constantly in order to ensure their proper performance. This paper proposes an intelligent PV condition monitoring scheme based on Programmable Logic Controller (PLC). This scheme monitors the PV performance and detects different faults and failures on the DC-side of the array. It also takes advantage of Human-Machine Interface (HMI) which provides the operators with useful real-time data from the array. © 2024 IEEE.

关键词： Condition monitoring Fault detection Human-machine interface (HMI) Photovoltaic (PV) systems Programmable logic controller (PLC)

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Innovative Integration of the Teaching-Learning Process in Situational English Practice 2

Innovative Integration of the Teaching-Learning Process in S...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jawad, Israa Abed Mohammed, Ali Ali Saber Sulaiman, Sazan Kamal Sofir, Ban Mohsen, Abdul Hussein Al-Nisour University College Baghdad Iraq Al Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

The growing importance of English in commerce necessitates ongoing research and development in English language instruction in order to provide students a competitive edge in the marketplace (ELT). Poor language proficiency among the kids may be the result of ineffective teaching strategies. This is the motivation behind the existing system's constant exploration and change. Students' linguistic foundation is strengthened and their language proficiency is accelerated through the use of ICT technology in the curriculum. Using technology to develop a framework for English instruction and learning has several benefits. This work's primary goal is to analyze the issues surrounding the use of technology in language instruction and learning. Students that use the Integrated Content and Language Learning Method (CLILM) study both a language and a subject at the same time. For instance, if a scientific course is taught in English, students will learn the language and vocabulary while studying science. There is a particular justification for learning both at the same time when a subject is taught, and many instructors view CLILM as a more organic method of language instruction. Additionally, it has the advantage of teaching many English-language courses, which exposes kids to the language sooner. When compared to current approaches, the CLIL method enhances the following: learning outcome ratio (98.6%), vocabulary ratio (91.1%), curriculum development ratio (96.4%), performance ratio (95.5%), student learning rate (95.1%), student-teacher interaction (92.8%), and fluency rate (93.7%). © 2024 IEEE.

关键词： Contrastive Learning

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Recent Advancements and Challenges in FinTech 14

Recent Advancements and Challenges in FinTech

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350335095

The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.

关键词： Cost effectiveness

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics 46

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abrisham, Kiana Pilevar Alipour, Khalil Tarvirdizadeh, Bahram Ghamari, Mohammad Laboratory Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350371499

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) - the gold standard in this field - is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics. © 2024 IEEE.

关键词： Photoplethysmography

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Empowering Internet of Underwater Things: An Integrated Simulator for Underwater Magneto-Coupled Wireless Sensor Networks

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IEEE Internet of Things Magazine 2025年第1期8卷 109-114页

作者： Songzuo, Liu Ullah, Irfan Muzzammil, Muhammad Ahmed, Niaz Wahid, Abdul Zhen, Shao Ministry of Industry and Information Technology Key Laboratory of Marine Information China College of Underwater Acoustic Engineering Harbin Engineering University China Sanya Nanhai Innovation and Development Base of Harbin Engineering University China FAST National University of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Campus Pakistan

This research paper presents an integration of finite element method (FEM) based models into MATLAB for developing an end-to-end magneto-coupled (MC) communication system tailored for Internet of Underwater Things (IoUT)-based applications. Magneto-coupled communication has emerged as a promising solution for extreme environments like underwater where conventional wireless acoustic communication poses significant challenges. The article exploits the strengths of two software tools: MATLAB and COMSOL Multiphysics, to seamlessly integrate communication protocols with theoretical insights into magnetic channels. The article also demonstrates the development and evaluation of an end-to-end communication system with both simulation and experimental setup. We believe that this integrated approach is highly effective in achieving reliable communication performance in underwater scenarios, thereby advancing the capabilities of IoUT for various real-world applications. © 2025 IEEE.

关键词： MATLAB

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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