检索结果-内蒙古大学图书馆

science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Segun I. Popoola Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria Department of Engineering Manchester Metropolitan University Manchester United Kingdom

Preprocessing in biometrics is the process of fine-tuning input data or features extracted from data by applying varying techniques to improve recognition performance. In most speaker recognition literature, researchers are silent about how the decision for such parameters used was chosen. This work systematically arrived at such decisions by carrying out several experiments on preprocessing and enhancement of spectrograms to see the effect on speaker recognition performance using a Convolutional Neural Network. First, different preprocessing experiments were carried out, one preprocessing parameter, in turn, was varied while the others were kept constant and eventually, the best parameters for all the preprocessing methods were combined to produce a spectrogram that yielded the best accuracy. The second part consists of enhancement experiments, where a series of image improvement techniques were applied to the spectrograms to further improve the initial accuracy. With the right parametric combination of the preprocessing techniques, speaker recognition improved by 25%. Spectrogram enhancements improved performance by a further 1%. Experimental results revealed that the dimensionality of a spectrogram can be significantly reduced with a very negligible drop in the overall performance which can enhance storage and computational requirements.

关键词： Training Deep learning Systematics Quantization (signal) Data preprocessing Feature extraction Speaker recognition

来源：评论

学校读者我要写书评

暂无评论

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

引用

Computational Intelligence and Neuroscience 2022年第0期2022卷 2933015页

作者： Alzubi, Ahmad Ali Tiwari, Shailendra Walia, Kuldeep Alanazi, Jazem Mutared Alzobi, Firas Ibrahim Verma, Rohit Computer Science Department Community College King Saud University Riyadh Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Sri Guru Granth Sahib World University Fatehgarh Sahib Punjab India Department of Information Systems and Networks Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing National College of Ireland Dublin Ireland

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve. © 2022 Ahmad Ali AlZubi et al.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion and Intensity of Emotion Classes from Meme Images using Single and Multi Modalities 1

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion ...

引用

1st Workshop on Multimodal Fact-Checking and Hate Speech Detection, DE-FACTIFY 2022

作者： Nguyen, Thanh Tin Pham, Nhat Truong Nguyen, Ngoc Duy Nguyen, Hai Nguyen, Long H. Kim, Yong-Guk Human Computer Interaction Lab Department of Computer Engineering Sejong University Seoul Korea Republic of Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia Khoury College of Computer Sciences Northeastern University Boston United States Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

Nowadays, memes found on internet are overwhelming. Although they are innocuous and sometimes entertaining, there exist memes that contain sarcasm, offensive, or motivational feelings. In this study, several approaches are proposed to solve the multiple modality problem in analysing the given meme dataset. The imbalance issue has been addressed by using a new Auto Augmentation method and the uncorrelation issue has been mitigated by adopting deep Canonical Correlation Analysis to find the most correlated projections of visual and textual feature embedding. In addition, both stacked attention and multi-hop attention network are employed to efficiently generate aggregated features. As a result, our team, i.e. HCILab, achieved a weighted F1 score of 0.4995 for sentiment analysis, 0.7414 for emotion classification, and 0.5301 for scale/intensity of emotion classes on the leaderboard. This results are obtained by using concatenation between image and text model and our code can be found at https://***/JMRa8. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark

arXiv

引用

arXiv 2025年

作者： Wang, Jiahao Cao, Xiangyu Zhong, Jiaru Zhang, Yuner Yu, Haibao He, Lei Xu, Shaobing School of Vehicle and Mobility Tsinghua University Beijing China Xingjian College Tsinghua University Beijing China School of Mechanical Engineering Beijing Institute of Technology Beijing China Faculty of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States Department of Computer Science The University of Hong Kong Hong Kong

Despite significant advancements, autonomous driving systems continue to struggle with occluded objects and long-range detection due to the inherent limitations of single-perspective sensing. Aerial-ground cooperation offers a promising solution by integrating UAVs’ aerial views with ground vehicles’ local observations. However, progress in this emerging field has been hindered by the absence of public datasets and standardized evaluation benchmarks. To address this gap, this paper presents a comprehensive solution for aerial-ground cooperative 3D perception through three key contributions: (1) Griffin, a large-scale multi-modal dataset featuring over 200 dynamic scenes (30k+ frames) with varied UAV altitudes (20-60m), diverse weather conditions, and occlusion-aware 3D annotations, enhanced by CARLA-AirSim co-simulation for realistic UAV dynamics;(2) A unified benchmarking framework for aerial-ground cooperative detection and tracking tasks, including protocols for evaluating communication efficiency, latency tolerance, and altitude adaptability;(3) AGILE, an instance-level intermediate fusion baseline that dynamically aligns cross-view features through query-based interaction, achieving an advantageous balance between communication overhead and perception accuracy. Extensive experiments prove the effectiveness of aerial-ground cooperative perception and demonstrate the direction of further research. The dataset and codes are available at https://***/wang-jh18-SVM/Griffin. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

来源：评论

学校读者我要写书评

暂无评论

Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

Cognitive Workload and Usability of Virtual Reality Simulation for Prosthesis Training

Cognitive Workload and Usability of Virtual Reality Simulati...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Junho Park Austin Music Daniel Delgado Joseph Berman Albert Dodson Yunmei Liu Jaime Ruiz He Huang David Kaber Maryam Zahabi Department of Industrial & Systems Engineering Wm Michael Barnes '64 Texas A&M University College Station TX USA Department of Computer & Information Science & Engineering University of Florida Gainesville FL USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Joint department of Biomedical Engineering North Carolina State University University of North Carolina at Chapel Hill Raleigh NC USA Department of Industrial and Systems Engineering University of Florida Gainesville FL USA

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Usability and security of different authentication methods for an electronic health records system 14

Usability and security of different authentication methods f...

引用

14th International Conference on Health informatics, HEALTHINF 2021 - Part of the 14th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2021

作者： Purkayastha, Saptarshi Goyal, Shreya Oluwalade, Bolu Phillips, Tyler Wu, Huanmei Zou, Xukai Department of BioHealth Informatics Indiana University-Purdue University IndianapolisIN46202 United States Department of Computer Science Indiana University-Purdue University IndianapolisIN46202 United States Department of Health Services Administration and Policy Temple University College of Public Health PA19122 United States

ISBN: (纸本)9789897584909

We conducted a survey of 67 graduate students enrolled in the Privacy and Security in Healthcare course at Indiana University Purdue University Indianapolis. This was done to measure user preference and their understanding of usability and security of three different Electronic Health Records authentication methods: single authentication method (username and password), Single sign-on with Central Authentication Service (CAS) authentication method, and a bio-capsule facial authentication method. This research aims to explore the relationship between security and usability, and measure the effect of perceived security on usability in these three aforementioned authentication methods. We developed a formative-formative Partial Least Square Structural Equation Modeling (PLS-SEM) model to measure the relationship between the latent variables of Usability, and Security. The measurement model was developed using five observed variables (measures). - Efficiency and Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results obtained highlight the importance and impact of these measures on the latent variables and the relationship among the latent variables. From the PLS-SEM analysis, it was found that security has a positive impact on usability for Single sign-on and bio-capsule facial authentication methods. We conclude that the facial authentication method was the most secure and usable among the three authentication methods. Further, descriptive analysis was done to draw out the interesting findings from the survey regarding the observed variables. Copyright © 2021 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Melanoma Using Deep Learning

引用

Mathematical Problems in Engineering 2021年 2021卷

作者： Alazzam, Malik Bader Alassery, Fawaz Almulihi, Ahmed Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia

When compared to other types of skin cancer, melanoma is the deadliest. However, those who are diagnosed early on have a better prognosis for the purpose of providing a supplementary opinion to experts;various methods of spontaneous melanoma recognition and diagnosis have been investigated by different researchers. Because of the imbalance between classes, building models from existing information has proven difficult. Machine learning algorithms paired with imbalanced basis training approaches are being evaluated for their performance on the melanoma diagnosis challenge in this study. There were 200 dermoscopic photos in which patterns of skin lesions could be extracted using the VGG16, VGG19, Inception, and ResNet convolutional neural network architectures with the ABCD rule. After employing attribute selection with GS and training data balance using Synthetic Minority Oversampling Technique and Edited Nearest Neighbor rule, the random forest classifier had a sensitivity of nearly 93% and a kappa index (k-index) of 78%. © 2021 Malik Bader Alazzam et al.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

PARCIV: Recognizing physical activities having complex interclass variations using semantic data of smartphone

PARCIV: Recognizing physical activities having complex inter...

引用

作者： Usman Sarwar, Muhammad Rehman Javed, Abdul Kulsoom, Farzana Khan, Suleman Tariq, Usman Kashif Bashir, Ali Department of Computer Sciences Air University and ASET: Ambient Systems and Emerging Technologies Lab Islamabad Pakistan National Center for Cyber Security Air University and ASET: Ambient Systems and Emerging Technologies Lab Islamabad Pakistan Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy College of Computer Science Engineering Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom

Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real-time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two-layered approach called PARCIVthat first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two-layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine-grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities. PARCIV recognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self-collected dataset. Furthermore, PARCIV achieve 95% accuracy on the publicly available dataset used by state-of-the-art studies. PARCIVoutperforms various state-of-the-art studies by 8%-17% for simple activities as well as complex activities. © 2020 John Wiley & Sons, Ltd.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Progression and Challenges of IoT in Healthcare: A Short Review

arXiv

引用

arXiv 2023年

作者： Atikur Rahman, S.M. Ibtisum, Sifat Podder, Priya Saokat Hossain, S.M. Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El PasoTX79968 United States Department of Computer Science Missouri University of Science and Technology RollaMO United States Dhaka National Medical College Dhaka1100 Bangladesh Department of CSE Jahangirnagar University Savar1342 Bangladesh

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments. © 2023, CC BY.

关键词： Health care

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：