检索结果-内蒙古大学图书馆

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Mathur, Hritesh Panwar, Harsh Karki, Rahul Jangra, Vivek Yadav, Ramnaresh Kapoor, Anjali Amity University Department of Information Technology Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. This work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. The model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. Therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices. © 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

Deep Learning Based License Plate Number Recognition for Smart Cities

引用

computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

来源：评论

学校读者我要写书评

暂无评论

Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 348-363页

作者： Shaik, Reddi Khasim Priya, S. Shanmuga Saranya, N. R, Kotteeswaran Ramya, S. Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India SRM Institute of Science and Technology Tiruchirappalli India Department of EIE St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Information Technology Prathyusha Engineering College Chennai India

Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variables such as temperature, precipitation, and soil moisture is crucial for a wide range of applications, including agriculture, water resource management, and disaster preparedness. Traditional models have often struggled to capture the complex, nonlinear relationships and temporal dependencies inherent in climate data. In this study, we present a novel Hybrid MLP-GRU Network for climate prediction, leveraging the combined strengths of Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models. This hybrid approach aims to capture both complex, nonlinear relationships and temporal dependencies in climate data, enhancing the accuracy and reliability of predictions. We utilized comprehensive datasets from the Copernicus Climate Change Service (C3S), focusing on essential variables such as temperature, precipitation, soil moisture, and vegetation indices. The data underwent rigorous preprocessing, including normalization, temporal alignment, and feature extraction, to ensure high-quality inputs for the model. The model was trained and validated on extensive climate datasets, demonstrating superior performance metrics with an accuracy of 97.77%, RMSE of 0.140, and MAE of 0.120 on validation data. Comparative analysis with other models, including Linear Regression, Random Forest, LSTM, and CNN, highlights the efficacy of our hybrid approach. The Hybrid MLP-GRU Network consistently outperformed these models in terms of RMSE, MAE, and accuracy. Additionally, hyperparameter tuning, cross-validation, and deployment assessments were conducted to optimize the model’s performance and ensure robust predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate data Climate prediction Data preprocessing Feature extraction Gated Recurrent Unit Hybrid MLP-GRU Network Machine learning Multi-Layer Perceptron Nonlinear relationships Temporal dependencies

来源：评论

学校读者我要写书评

暂无评论

Unmasking Toxicity: A Comprehensive Analysis of Hate Speech Detection in Banglish 6

Unmasking Toxicity: A Comprehensive Analysis of Hate Speech ...

引用

6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Islam, Md. Hasibul Farzana, Kaniz Khalil, Ibrahim Ara, Shaneen Shazid, Md.Ruhul Amin Kabir Mehedi, Md Humaion Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

As the digital landscape expands, the rise of online hate speech presents a pressing challenge, necessitating sophis-ticated tools for effective detection and mitigation. This project focuses on the intricate linguistic landscape of Banglish a hybrid language amalgamating Bengali and English striving to develop robust models tailored to its unique characteristics. The dataset, comprising 5000 Banglish comments categorized into various hate speech types, serves as the foundation for model exploration. Our approach spans a wide variety of models, including traditional machine learning (SVM, Logistic Regression,random forest), advanced deep learning architectures and innovative hybrid models (CNN+BiLSTM). Approaches for feature extraction such word embedding, TF-IDF, and Bag-of-Words and sentiment analysis scores are adapted to the nuances of Banglish. Ethical considerations guide our development, addressing algorithmic bias and user rights. The experimental results provide a nuanced understanding of model performance, in- cluding accuracy (90%), precision, recall, and F1 score. Insights derived from these analyses contribute to the ongoing refinement of hate speech detection methodologies, advancing the field of computational linguistics and ethical artificial intelligence. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Quasi-Sensitivity Privacy Reserving for Securing Personalized Records Using Hyper-spectral Neural Classification for Improved Cloud Security

引用

SN computer science 2024年第1期5卷 180页

作者： Deepika, K.M. Sanjay, H.A. Mohan Murthy, M.K. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India IBM India Pvt. Lt. Bangalore India

In the modern era, ensuring personalized security has become a significant concern, particularly when it comes to safeguarding sensitive information within centralized cloud storage systems. This is especially critical for healthcare data, which demands increased focus on transaction security and the preservation of privacy. Within the realm of cloud services, a key challenge arises from treating both sensitive and non-sensitive attributes equally during the feature selection process. This approach can lead to security breaches, resulting in the unintended exposure of sensitive information through methods like k-anonymization. To address this issue, it is crucial to distinguish between sensitive and non-sensitive attributes to safeguard them separately and effectively. To tackle this problem, we propose a solution called “Pragmatic Quasi-Sensitivity Identification (PQSI),” which is designed to enhance cloud security by preserving the privacy of personalized records using hyper-spectral feature classification. The process begins by estimating the sensitivity fitness value of features using a metric called “Sensitive Frequent Transaction Rate (SFTR).” Additionally, we estimate the residual terms of relative significant feature rates (RSF), which are then clustered based on marginal subset features. By incorporating a sensitivity threshold frequency rate and applying a pragmatic quasi-sensitive identifier, we extract features and employ Hyper-Spectral Neural Classification (HSNC) to classify sensitive and non-sensitive records. This classification is based on the frequency fitness weight, enabling us to segregate and store these records individually within a private cloud environment. As an added layer of security, sensitive records are organized into data blocks and secured within a blockchain framework. This proposed system not only achieves higher prediction accuracy compared to alternative methods but also outperforms them in terms of sensitivity and specificity ra

关键词： Cloud computing Integrity proofing Privacy preserving Quasi-sensitivity identifier Secure storage Transaction security

来源：评论

学校读者我要写书评

暂无评论

TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

引用

computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

来源：评论

学校读者我要写书评

暂无评论

Hybrid Technique for Constructing Footprint Extraction System from Satellite Imaginary 2

Hybrid Technique for Constructing Footprint Extraction Syste...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Raghavendran, N. Menaga, P. Prabu, V. Vignesh Manimaran, V. Robinson, Y. Harold Ramanan, K. Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Rajalakshmi Engineering College Department Of Computer Science & Business Systems Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Civil Engineering Tamil Nadu India National Engineering College Department of Information Technology Tamil Nadu Kovilpatti India NPR College of Engineering and Technology Natham Department of Computer Science and Engineering Tamil Nadu Dindigul India

ISBN: (纸本)9798350365269

The building extraction of the footprint from the satellite imagination has been a research problem that needs to be solved efficiently. The hybrid semantic segmentation framework is used to increase the footprint extraction system as the Inria Aerial Image Labeling Dataset is involved in improving the extraction of the footprint, the proposed technique is framed with the CGAN model and Mask RCNN for utilizing the instance segmentation which manages the data augmentation and semantic segmentation process. The performance evaluation shows that the proposed technique has an improved F1 score and accuracy as the effect of every functionality for the footprint extraction has been analyzed subsequently. © 2024 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

CoBiAt: A Sentiment Classification Model Using Hybrid ConvNet-Dual-LSTM with Attention Techniques

引用

Informatica (Slovenia) 2023年第4期47卷 523-536页

作者： Ranjan, Roop Daniel, A.K. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology UP Gorakhpur India

Many researchers have recently turned their attention to emotion analysis as a resultant to the number of social reviews of various services. User behaviour may be better understood with the plethora of data, which makes it possible to work toward enhancing QoS. The critical areas of research in language processing are text categorization, which places unorganised data into relevant categories. In several natural language processing (NLP) applications, LSTM and CNN are utilised for text classification. CNN models use techniques to obtain top-level features. In this study, an attention-based model using Dual-LSTM and ConvNet has been proposed. For effectiveness verification of the model, it is trained using two unique datasets. The proposed hybrid model has demonstrated a significant performance gain when compared to previous deep learning techniques. In comparison to other traditional machine learning models, the suggested approach yields outcomes with a higher level of accuracy. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Zema Dataset: A Comprehensive Study of Yaredawi Zema with a Focus on Horologium Chants

Zema Dataset: A Comprehensive Study of Yaredawi Zema with a ...

引用

2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： Muluneh, Mequanent Argaw Peng, Yan-Tsung Degife, Worku Abebe Tadesse, Nigussie Abate Demeku, Aknachew Mebreku Su, Li Academia Sinica Institute of Information Science Taipei Taiwan National Chengchi University Department of Computer Science Taipei Taiwan Gondar University Department of Information Science Gondar Ethiopia National Taiwan University of Science and Technology Department of Information Management Taipei Taiwan National Taiwan University of Science and Technology Department of Material Science and Engineering Taipei Taiwan

ISBN: (纸本)9798331527792

Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles in the world. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music research. This paper contributes to this field by introducing a new dataset specifically tailored for the analysis of EOTC chants, also known as Yaredawi Zema. This work provides a comprehensive overview of a 10 hours of dataset, 369 instances, creation and curation process, including rigorous quality assurance measures. Our dataset has a detailed word-level temporal boundary and reading tone annotation along with the corresponding chanting mode label of audios. Moreover, we have also identified the chanting options associated with multiple chanting notations in the manuscript by annotating them accordingly. Our goal in making this dataset available to the public1 is to encourage more research and study of EOTC chants, including lyrics transcription, lyric-to-audio alignment, and music generation tasks. Such research work will advance knowledge and efforts to preserve this distinctive liturgical music, a priceless cultural artifact for the Ethiopian people. © 2024 IEEE.

关键词： computer music

来源：评论

学校读者我要写书评

暂无评论

3D Brain MR Image Segmentation using a Fuzzy Entropy-Based Fuzzy Clustering Algorithm 2

3D Brain MR Image Segmentation using a Fuzzy Entropy-Based F...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ray, Madhumita Sing, Jamuna Kanta Meghnad Saha Institute of Technology Computer Science and Engineering department Kolkata India Jadavpur University Computer Science and Engineering department Kolkata India

ISBN: (纸本)9798350371314

This paper presents a fuzzy clustering algorithm by incorporating fuzzy entropy for segmentation of 3D brain magnetic resonance (MR) images. Intensity inhomogeneity (IIH) and noise strongly affect brain MR images because of improper scanning environment or patient's unconscious movements at the time of image acquisition. The occurrence of this phenomenon makes the soft tissue regions blurry, specifically at the boundary region and imposes uncertainty for clustering the soft tissue regions. The presented algorithm utilizes fuzzy entropy, which associates with the local membership functions to mitigate the underlying uncertainty for voxel classification. It integrates fuzzy entropy with the membership functions of local neighbouring regions of the voxel. The objective function conjugates global membership functions with the distance of the cluster center and the voxel along with a controlling factor as a first term. The multiplicative factor of the local membership function, the mean distance of neighboring voxels from the cluster center, possibility parameter and a complemented controlling factor is added as the second term. Finally, the third term is the fuzzy entropy defined with the local membership function. The suggested algorithm has tested in a qualitative and quantitative manner by utilizing 6 simulated image volumes and 4 real patient volumetric brain MR images. The results of the experiments demonstrate outperforms other competitive algorithms developed recently. The dice score similarity of the proposed method by using simulated image volume 77.26%. The outcome of real patient image volume is measured by partition entropy and partition coefficient not by dice similarity coefficient as for real image ground truth images are not available. © 2024 IEEE.

关键词： Image segmentation

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：