检索结果-内蒙古大学图书馆

13th international Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Saji, Alen Charuvila Ramalakshmi, K. Senbagavalli, M. Gunasekaran, Hemalatha Ebenezer, Shamila Computer Science and Engineering K.I.T.S. India Computer Science and Engineering Alliance University Bangalore India Information Technology Alliance University Bangalore India IT Department University of Technology and Applied Sciences Oman

ISBN: (纸本)9781713857938

The deaf-mute community utilises sign language for interacting among themselves and others. The introduction of standard sign language has made their lives much easier. This paper proposes an effective hand-sign recognition method using a deep learning technique and is based on YOLOv5, which is a real-time object detection algorithm which detects a hand sign and outputs the corresponding text. The proposed model utilises various sub-models namely, Cross Stage Partial Network (CSPNet), Path Aggregation Network (PANet), Dense Prediction. This model can be conveniently deployed into an android application with a user-friendly interface. © Grenze Scientific Society, 2022.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Multi-agent policy transfer via task relationship modeling

引用

science China(information sciences) 2024年第8期67卷 102-114页

作者： Rongjun QiN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

来源：评论

学校读者我要写书评

暂无评论

A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

引用

18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within Ai research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient Ai model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of Ai in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of Ai within music analysis. © 2023 iEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

引用

18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 iEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

ExpressionHash: Securing Telecare Medical information Systems Using BioHashing

引用

computers, Materials & Continua 2021年第6期67卷 2747-2764页

作者： Ayesha Riaz Naveed Riaz Awais Mahmood Sajid Ali Khan imran Mahmood Omar Almutiry Habib Dhahri SEECS National University of Science and TechnologyIslamabadPakistan College of Applied Computer Science King Saud University(Almuzahmiyah Campus)RiyadhSaudi Arabia Department of Software Engineering Foundation University IslamabadPakistan

The COViD-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical information *** systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and *** leakage or unauthorized access to users’medical data can have serious consequences for any medical information *** majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored *** this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical information *** idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for *** expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each *** templates are stored on the server-side and the processing is also performed on the *** proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric *** the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash *** well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach.

关键词： Biometrics TMiS biohashing multifactor authentication medical information system

来源：评论

学校读者我要写书评

暂无评论

Color-Based Auto-Rickshaw Detection Using Convolutional Neural Networks for intelligent Transportation Systems

Color-Based Auto-Rickshaw Detection Using Convolutional Neur...

引用

information and Communication Technology (iCiCT), international Conference on

作者： Md. Mehedi Hasan Nakib Aman Md Wadud Jahan Software Engineer I Developer Experience Hub Rajshahi Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Computer Science and Engineering Varendra University Rajshahi Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

The investigation in this paper is centered on a new methodology for improving the effectiveness of intelligent transportation systems by identifying and distinguishing auto-rickshaws using Convolutional Neural Networks (CNN). The strategy proposed involves training a CNN model to automatically recognize and differentiate the colors of auto-rickshaws, which in turn significantly enhances the detection capabilities. Using hierarchical convolution feature models, the CNN algorithm independently extracts relevant features from images, thereby aiding in precise object detection. An assessment of the model's performance is conducted using a carefully selected dataset containing 1000 auto-rickshaw images, illustrating a high level of accuracy with minimal margin of error. This research makes a valuable contribution to the advancement of automated transport detection within the realm of traffic management. it presents a more streamlined and impactful methodology aimed at improving the overall efficiency and reliability of the system.

关键词： Training Accuracy image color analysis Semantic segmentation Urban areas Transportation Streaming media Feature extraction Convolutional neural networks Reliability

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

引用

18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 iEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

引用

18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 iEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

引用

18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional Ai methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, Ai's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. in this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 iEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

EfficientNet B7 based Deep Learning Technique for Adaptive Gait Recognition under Challenging Covariate Conditions

EfficientNet B7 based Deep Learning Technique for Adaptive G...

引用

Recent Progresses in science, engineering and Technology (iCRPSET), international Conference on

ISBN: (数字)9798331509477

ISBN: (纸本)9798331509484

Gait recognition is a crucial biometric tool for surveillance and security, but its effectiveness is hindered by factors like clothing, carried obj ects, and environmental conditions. This study introduces an adaptive framework that assigns weights to gait components to counter these issues. Using EfficientNet_B7, a deep learning technique, the framework reduces intra-class variations and improves recognition accuracy. The experimental results show a 98.7% accuracy rate, proving the method's robustness in various conditions. This advancement significantly enhances the reliability of gait recognition systems in real-world applications.

关键词： Deep learning YOLO Heart Adaptation models Accuracy Surveillance Clothing Robustness Security Gait recognition

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：