检索结果-内蒙古大学图书馆

14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Keshtkar, Mahsa Tahayori, Hooman Department of Computer Science and Engineering IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9798331511272

— In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these impacts can be beneficial in various fields, ranging from developing healthcare surveillance systems for the elderly to identifying adverse emotions in those engaged in high-stakes occupations, such as pilots, sailors, and long-haul truckers. This paper presents a solution based on the application of interval type two fuzzy sets and systems to detect individuals' emotions using their physiological signals. The physiological signals on the same emotion varies from person to person and even for the same person in different situations. This variability has led us to employ interval type-2 fuzzy sets. We defined five footprints of uncertainty for each physiological signal to cover its whole range and employed instance selection technique to optimize the rule base. We develop a rule-based fuzzy system that employs the fewest feasible number of rules for the purpose of emotion recognition. In order to enhance the comprehensibility of the system, we have also utilized a minimal set of features in the construction of the rules. This study implemented on physiological signals from the K-EmoCon dataset. Obtained results shows that the proposed method outperform other existing method with lower computational complexity. ©2024 IEEE.

关键词： Fuzzy rules

来源：评论

学校读者我要写书评

暂无评论

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

引用

CAAI Transactions on Intelligence Technology 2023年第3期8卷 914-930页

作者： Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos School of Computer Science Wuhan UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina School of Electronics Electrical Engineering and Computer ScienceQueen's University BelfastBelfastUK

Recent years have witnessed the rapid development of service‐oriented computing *** boom of Web services increases software developers'selection burden in developing new service‐based systems such as *** recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software *** service recom-mendation approaches are mainly designed for mashup development in the single‐round *** is hard for them to effectively update recommendation results according to developers'requirements and behaviours(*** service selection).To address this issue,the authors propose a service bundle recommendation framework based on deep learning,DLISR,which aims to capture the interactions among the target mashup to build,selected(component)services,and the following service to ***,an attention mechanism is employed in DLISR to weigh selected services when rec-ommending a candidate *** authors also design two separate models for learning interactions from the perspectives of content and invocation history,respectively,and a hybrid model called *** on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

关键词： attention deep learning mashup development recommender systems service bundle

来源：评论

学校读者我要写书评

暂无评论

Unique Image Identification and Ownership Monitoring using Blockchain

Unique Image Identification and Ownership Monitoring using B...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kour, Kanwal Preet Singh Bali, Malvinder Sameeksha Manipal University Jaipur School of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798331508685

Blockchain technology has largely revolutionized modern life. The impact of blockchain with its immutable and irreversible transactions has given rise to many modern technological models, and the same is true for the unique identification of images. Numerous issues related to the unique identification of images and owner tracing like signature verification, secure image transactions and much more, this can be addressed using blockchain. In the present era, it is exceptionally easy to steal or alter digital artwork and photos. This research, which focuses mostly on digital artwork and images, offers promise in addressing issues related to intellectual property rights. The research paper proposes a method for tracking ownership of data and maintaining authentic historic records related to image creation using technologies like blockchain, steganography and digital watermarking. Many models have been proposed to counter the issues related to image owner identification. Blockchain technology is making rapid progress in this field to solve problems and improve existing solutions. By incorporating multiple hashing techniques like Phash, Dhash, and cryptographic hashing and other technologies and concepts like steganography, we aim to establish an efficient system for uniquely identifying images and tracing their owners. © 2025 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

引用

computer Modeling in engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

引用

China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

来源：评论

学校读者我要写书评

暂无评论

Active self-training for weakly supervised 3D scene semantic segmentation

引用

Computational Visual Media 2024年第3期10卷 425-438页

作者： Gengxin Liu Oliver van Kaick Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Carleton UniversityOttawa K1S 5B6Canada

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

关键词： semantic segmentation weakly supervised self-training active learning

来源：评论

学校读者我要写书评

暂无评论

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

引用

Journal of Mountain science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

来源：评论

学校读者我要写书评

暂无评论

An eco-friendly and highly sensitive loofah@CF/CNT 3D piezoresistive sensor for human activity monitoring and mechanical cotrol

引用

science China(Technological sciences) 2022年第11期65卷 2667-2674页

作者： ZHU Jie SONG YongTao XUE XiaoFei LIU ZhiLong MAO QingYuan JIA ZhenHua School of Computer Science and Engineering North China Institute of Aerospace EngineeringLangfang 065000China

Three-dimensional(3D)porous piezoresistive sensors are widely used because of their simple fabrication and convenient signal ***,because of the dependence on organic skeleton materials and the complexity of conductive coating preparation,the electrical and mechanical properties of 3D wearable piezoresistive sensors have gradually failed to accommodate many emerging ***,a new flexible 3D piezoresistive sensor(NF3PS)with high sensitivity and a wide measurement range is proposed,which comprises a natural porous loofah as a flexible framework and carbon fiber/carbon nanotube(CF/CNT)multiscale composite as a conductive *** of cellulose and lignin,the irregular,porous loofah has excellent mechanical strength,elasticity,and toughness,ensuring a repeated compression/recovery behavior of the *** addition,compared with the single-size carbon coating,the coupling of multiscale CF/CNT composite coating improves sensitivities over a range of *** NF3PS demonstrates a sensitivity of 6.94 kPa^(-1) with good linearity in the pressure range of 0–11.2 kPa and maintains a sensitivity of 0.28 kPa^(-1) in an ultrawide measurement range of 11.2–84.6 *** flexibility,robustness,and wide-ranging linear resistance variation,the feasibility of the NF3PS in human activity monitoring,mechanical control,and smart homes is *** work provides a novel strategy for a new generation of 3D flexible pressure sensors for improving sensitivity and measurement range and demonstrates attractive applications in wearable sensors.

关键词： 3D piezoresistive sensor loofah framework CF/CNT multiscale composite high sensitivity human activity monitoring mechanical control

来源：评论

学校读者我要写书评

暂无评论

Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

引用

Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

引用

Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive 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：