检索结果-内蒙古大学图书馆

5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Mohamed, Noor El-Deen M. El-Dakroury, Mohamed M. Ibrahim, Abdulrahman A. Nfady, George A. Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350381030

In recent years, domestic and industrial users have shifted from traditional meters to smart meters. A solution implemented called the Next-Generation Smart Grid Meter (NGSM) is a smart meter device for monitoring electricity consumption. It measures the energy consumption of a household and acts as a smart node in the electricity grid to apply the next generation of Advanced Metering Infrastructure (AMI) systems connected via a smart grid for cluster communication. Various features would be included such as measuring the power, energy...etc., and calculating the costs with a developed Head-End System (HES) which is divided into a web-based system and a smartphone mobile application for the overall monitoring across the supported region and displaying the readings on the meter LCD TFT screen for the user, and tamper detection mechanisms. The smart grid connection is made via the internet which transmits the data over the grid. Moreover, it applies a deep learning model using the data collected in its data storage to make classification and prediction of the working household appliances at different times. The concluded NGSM system is an Embedded Linux-based meter for real-time on-edge processing with a complete HES, web and mobile application. Household appliance detection using the aggregated load achieved 93.75% accuracy with 3 devices operating at the same time. © 2023 IEEE.

关键词： Domestic appliances

来源：评论

学校读者我要写书评

暂无评论

End-to-End Verifiable Decentralized Federated Learning 6

End-to-End Verifiable Decentralized Federated Learning

引用

6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Lee, Chaehyeon Heiss, Jonathan Tai, Stefan Hong, James Won-Ki Postech Department of Computer Science and Engineering Pohang Korea Republic of Tu Berlin Information Systems Engineering Berlin Germany

ISBN: (纸本)9798350316742

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Methodology for Identifying the Similarity Between Languages with Levenshtein Distance 1

引用

6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Janardhana Rao, Peluru Nageswara Rao, Kunjam Gokuruboyina, Sitaratnam Neeraja, Kondamudi Naga Department of Computer Science and Systems Engineering Andhra University College of Engineering Andhra University Visakhapatnam India Visakhapatnam India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

This article compares the multilingual texts that are used for bilingual lexicon extraction and plagiarism detection. A collection of related sentences and sentences that are translations of one another, a parallel corpus, is used to compare multilingual content. In order to determine the similarity between the sentences and words in a multilingual content, Levenshtein Distance based String Similarity technique is used along with three other techniques such as Sequence matcher, Fuzzy-Wuzzy (Ratio), and Spacy Similarity techniques in the literature. The Comparative study is presented in this article to compare similar kind of work implemented with proposed techniques. The Levenshtein Distance based string similarity technique out performs in terms of Accuracy compared to Sequence matcher, Fuzzy-Wuzzy and Spacy similarity techniques for the identification of similarity between the languages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intellectual property

来源：评论

学校读者我要写书评

暂无评论

Extensive Numerical Analysis of PDF Parameters for Wind Energy in Brazil: A Study Across 27 Cities for 60 Years Wind Speed

引用

IEEE Access 2025年 13卷 84555-84568页

作者： Badawi, Ahmed Souza, Amaury Elzein, I.M. Ali, Hassan Zyoud, Alhareth College of Engineering Technology University of Doha for Science and Technology Qatar Federal University of Mato Grosso do Sul Brazil Birzeit University Faculty of Engineering Department of Electrical and Computer Engineering Birzeit Ramallah Palestine

This research conducts a comprehensive numerical assessment of wind energy potential across Brazil, leveraging six decades of wind speed data collected from 27 cities to analyze Probability Density Function (PDF) parameters and enhance wind resource mapping. The study employs the Weibull distribution, defined by its shape factor k and scale factor c, to represent variations in wind speed. A total of nine numerical practices and methods including the Maximum Likelihood Method (MLM), Method of Moments (MM), Empirical Method (EM), Standard Deviation Method (STDM), Modified Maximum Likelihood Method (MMLM), Second Modified Maximum Likelihood Method (SMMLM), Graphical Method (GM), Least Mean Square Method (LSM), and Energy Pattern Factor Method (EPF) were utilized to achieve optimal parameter estimation. Statistical analysis was performed using various metrics such as the Index of Agreement (IA), Root Mean Square Error (RMSE), Chi-square test (X2), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE). The findings indicated that MLM was the most precise method, followed by STDM, EM, MM, and EPF, while GM and LSM demonstrated lower effectiveness. The analysis determined a scale factor range of 2.17-2.36 m/s and a shape factor of 2.5-2.78, resulting in a bell-shaped Gaussian PDF. The Mean Wind Speed (MWS) was recorded at 2.06 m/s, which, despite being categorized as low, indicates the potential for effective deployment of small-scale wind turbines for energy production. This study offers significant insights for wind energy development in Brazil, presenting a methodological approach for optimizing the placement of wind turbines and minimizing electricity generation costs. © 2013 IEEE.

关键词： Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

引用

computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

来源：评论

学校读者我要写书评

暂无评论

Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

New exact solutions of the Mikhailov-Novikov-Wang equation via three novel techniques

引用

Journal of Ocean engineering and science 2023年第1期8卷 103-110页

作者： Arzu Akbulut Melike Kaplan Mohammed K.A.Kaabar Mathematics-Computer Department Art-Science FacultyEski¸s ehir Osmangazi UniversityEski¸s ehirTurkey Department of Computer Engineering Engineering and Architecture FacultyKastamonu UniversityKastamonuTurkey Institute of Mathematical Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur 50603Malaysia

The current work aims to present abundant families of the exact solutions of Mikhailov-Novikov-Wang equation via three different *** adopted methods are generalized Kudryashov method(GKM),exponential rational function method(ERFM),and modified extended tanh-function method(METFM).Some plots of some presented new solutions are represented to exhibit wave *** results in this work are essential to understand the physical meaning and behavior of the investigated equation that sheds light on the importance of investigating various nonlinear wave phenomena in ocean engineering and *** equation provides new insights to understand the relationship between the integrability and water waves’phenomena.

关键词： Exact solutions Generalized Kudryashov method Modified extended tanh-function method Symbolic computation PDEs

来源：评论

学校读者我要写书评

暂无评论

Secure speaker identification in open and closed environments modeled with symmetric comb filters

引用

Multimedia Tools and Applications 2024年第18期84卷 1-43页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

Banana Leaf Disease Detection using Advanced Convolutional Neural Network

Banana Leaf Disease Detection using Advanced Convolutional N...

引用

2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Mohanraj, E. Hariharan, R. Keerthana, S. Cavin, K.B. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

Any plant's ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and production quantity. If the disease and its origin are not appropriately diagnosed, illness control procedures might be a waste of effort and resources, causing additional plant losses. Plant disease tracking by manual process is quite challenging. It requires a considerable amount of work, expertise in plant diseases, and protracted processing durations. Traditional neural networks require a large number of parameters to train effectively, which can make them slow and computationally expensive. The previous study includes the leaf disease detection performed by CNN, R-CNN, and SVM models. Using the Faster R-CNN, which outperforms other models in terms of computation time and accuracy, this study intends to develop a banana leaf disease detection system. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Beyond Playlists: AI-Driven Emotion-Based Music Recommendation systems 2

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

引用

2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shiralaskar, Hrishikesh Mendhe, Sameer Datta Meghe Institute of Higher Education md Research Department of Computer Science & Engineering Faculty of Engineering & Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science & Design Faculty of Engineering & Technology Maharashtra India

ISBN: (纸本)9798331505745

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems. © 2025 IEEE.

关键词： Deep learning

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：