检索结果-内蒙古大学图书馆

Networking and Communications (ICNWC), International Conference on

作者： Harsh Vardhan Harsavardhan S M Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings.

关键词： Training Performance evaluation Sign language Adaptation models Refining Speech recognition Medical services

来源：评论

学校读者我要写书评

暂无评论

Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel dataset

arXiv

引用

arXiv 2024年

作者： Khanna, Sandeep Chattopadhyay, Chiranjoy Kundu, Suman Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India School of Computing and Data Sciences FLAME University India

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Person Re-identification based on YOLOV5 with Mask Region Convolution Neural Network

Unsupervised Person Re-identification based on YOLOV5 with M...

引用

data Science and Information System (ICDSIS), IEEE International Conference on

作者： Jianbo Liang Mideth Abisado College of Computing and Information Technologies National University Manila Philippines School of Big Data Engineering Kaili University Kaili China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The unsupervised person re-identification is particularly relevant across various fields, including the continued growth of autonomous cars, communication between humans and computer units and human action research. However, the existing research are complex to identify the person accurately and efficiently. This research proposed an You Only Look Once Version 5 (YOLOV5) with mask Region-based Convolution Neural Network (RCNN) for unsupervised person re-identification effectively from the Pascol Voc dataset. It is preprocessed by data augmentation to tackle the overfitting which is preformed based on random rotation, flipping and shearing transformation. Then, these features are extracted through Local Ternary Pattern (LTP). The extracted features are provided into YOLOV5-mask RCNN for the recognition and detection of unsupervised person. The YOLOV5 adopts one neural network to predict bounding box and class possibilities straightly from the whole image in one forward pass. It shortens the detection and enhances the efficiency. The mask RCNN provide accurate pixel-level segmentation for every instance in image. The performance of YOLOV5-mask RCNN is calculated by metrics like Mean Average Precision (mAP), Inference Speed (FPS), precision, accuracy, recall and f1-score. The YOLOV5mask RCNN achieves 85.61% mAP, 38.5 Hz FPS, 95.37% precision, 95.68% accuracy, 94.51% recall and 94.29% f1-score when compared to Channel Attention (CA)-MobileNetv2YOLOv 4 and YOLOv2 based pedestrian detection (YOLOV2PD).

关键词： YOLO Image segmentation Accuracy Pedestrians Convolution Neural networks Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Defending Against Vishing Attacks: A Comprehensive Review for Prevention and Mitigation Techniques

Defending Against Vishing Attacks: A Comprehensive Review fo...

引用

International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Ashfaq, Shaikh Chandre, Pankaj Pathan, Shafi Mande, Uday Nimbalkar, Madhukar Mahalle, Parikshit Information Technology Department M H Saboo Siddik College of Engineering Mumbai India Computer Science and Engineering Department MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Artificial Intelligence and Data Science Department Vishwakarma Institute of Information Technology Kondhwa Pune India

ISBN: (纸本)9789819998104

Vishing attacks, or voice phishing attacks, are a type of social engineering attack in which attackers use voice communication channels, such as phone calls, to trick victims into divulging sensitive information or performing actions that compromise their security. Vishing attacks have become increasingly common in recent years and can have severe consequences for individuals, businesses, and organizations. In this review, we examine various prevention and mitigation techniques that can be used to defend against vishing attacks. We start by discussing the common tactics used by attackers and how they exploit vulnerabilities in human behaviour to achieve their goals. We then present an overview of different prevention and mitigation techniques, including education and awareness campaigns, technical solutions such as authentication mechanisms and policy-based approaches. We also evaluate the effectiveness of different techniques, highlighting their strengths and weaknesses, and provide guidance on how to implement a comprehensive vishing defence strategy. Finally, we discuss emerging trends and challenges in vishing attacks and defence, such as the use of artificial intelligence and deepfake technology, and suggest directions for future research and development. Overall, this review provides a comprehensive and up-to-date overview of the current state of vishing attacks and defence, and should be of interest to researchers, practitioners, and individuals who want to enhance their knowledge and understanding of this important topic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

引用

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

来源：评论

学校读者我要写书评

暂无评论

CurricularVPR: Curricular Contrastive Loss for Visual Place Recognition

CurricularVPR: Curricular Contrastive Loss for Visual Place ...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dongshuo Zhang Nanhua Chen Meiqing Wu Siew-Kei Lam College of Computing and Data Science Nanyang Technological University Singapore School of Aerospace Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual Place Recognition (VPR) techniques commonly utilize Contrastive Losses (CL) to train models that generate compact and discriminative global descriptors for images. These models often result in poor performance due to one of the following reasons during training: 1) loss functions that focus primarily on easier samples, 2) reliance on time-consuming hard sample mining methods to identify informative supervisory samples, which hinders effective learning from large-scale datasets. To enhance both learning efficiency and effectiveness, we propose a Curricular Contrastive Loss (CCL) and use graded similarity labels as a measure of sample difficulty. Inspired by human learning that begin with easier concepts and progressively tackle more challenging ones, our CCL dynamically emphasizes easier samples during the initial training stages to achieve rapid convergence. The learning gradually focuses on harder samples in later training stages to bolster robustness of the models under challenging conditions. Our proposed method has been extensively evaluated on popular datasets, and the results demonstrate its superior performance compared to the CL and Generalized CL functions.

关键词： Training Accuracy Big data Loss measurement Robustness Intelligent robots Visual place recognition Convergence

来源：评论

学校读者我要写书评

暂无评论

Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Wang, Zhiying Sun, Gang Wang, Yuhui Yu, Hongfang Niyato, Dusit University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Nanyang Technological University College of Computing and Data Science Singapore Singapore

The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. The low-altitude economy, leveraging the flexibility and multifunctionality of Unmanned Aerial Vehicles (UAVs) in SAGIN, holds significant potential for development in areas such as communication and sensing. However, effective coordination is needed to streamline information exchange and enable efficient system resource allocation. In this paper, we propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN. The CMADDPG algorithm leverages dynamic UAV clustering to partition UAVs into clusters, each managed by a Cluster Head (CH) UAV, facilitating a distributed-centralized control approach. Within each cluster, UAVs delegate offloading decisions to the CH UAV, reducing intra-cluster communication costs and decision conflicts, thereby enhancing task scheduling efficiency. Additionally, by employing a multi-agent reinforcement learning framework, the algorithm leverages the extensive coverage of satellites to achieve centralized training and distributed execution of multi-agent tasks, while maximizing overall system profit through optimized task offloading decision-making. Simulation results reveal that the CMADDPG algorithm effectively optimizes resource allocation, minimizes queue delays, maintains balanced load distribution, and surpasses existing methods by achieving at least a 25% improvement in system profit, showcasing its robustness and adaptability across diverse scenarios. © 2025 IEEE. All rights reserved.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Attentive Feature Segmentation model for Stroke detection on MRI images

Attentive Feature Segmentation model for Stroke detection on...

引用

BIT Conference (BITCON), International

作者： S.E. Viswapriya D. Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Medical image segmentation plays a crucial role in treatment planning, disease diagnosis, and guiding surgical navigation. Research should focus on medical image segmentation to achieve substantial breakthroughs or fill knowledge gaps that could lead to discoveries and improvements. Deep learning (DL) techniques have recently become necessary in creating advanced models, particularly for medical image segmentation. This paper introduces a novel segmentation algorithm specifically designed for MRI images. Initially, the paper will discuss the introduction and challenges in medical image segmentation. Later, developing a novel attentive feature-based segmentation algorithm is focused and their corresponding evaluation steps are measured. The evaluation metrics are finally compared with other existing segmentation algorithms and concluded that the proposed model surpasses the cutting-edge methods by notable differences in performance, showing the adaptability and overall effectiveness of the proposed model.

关键词： Deep learning Image segmentation Adaptation models Magnetic resonance imaging Semantic segmentation Surgery Computer architecture Feature extraction Medical diagnostic imaging Testing

来源：评论

学校读者我要写书评

暂无评论

Phylogenetic-Informed Generative Adversarial Network for Predicting Mutations in SARS-CoV-2

Phylogenetic-Informed Generative Adversarial Network for Pre...

引用

Networking and Communications (ICNWC), International Conference on

作者： Vijay Chandar Vishnu G Nair Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In the pursuit of developing more effective vaccines against viruses like SARS-CoV-2, understanding their potential mutations is crucial. In order to generate protein sequences, this study presents a unique framework that synergistically blends phylogenetic data with Generative Adversarial Networks (GANs). The proposed method combines the strengths of two models: TEMPO, a transformer-based model proficient in leveraging phylogenetic data for mutation detection in SARS-CoV-2, and MutaGAN, a GAN-based model adept in generating synthetic protein sequences. The phylogenetic data from TEMPO is used in this novel design as the input for the MutaGAN generator, replacing the conventional random noise input used in GANs. The generator may now create synthetic protein sequences that are impacted by the evolutionary past included in the phylogenetic data resulting in the creation of artificial protein sequences that are more biologically significant and lifelike. The discriminator portion of the model, similar to MutaGAN, is still in charge of determining whether or not the input sequences comprise a valid parent-child pair and uses sequences produced by the first GAN as extra negative cases. This prevented the model from going too far off its planned route. Additional investigation into this fascinating confluence of bioinformatics and AI may be possible as a result of this study’s conclusions. This approach has tremendous promise in many applications, such as the production of more powerful vaccinations, protein engineering, medication design, and improving our knowledge of biological systems, by forecasting how SARS-CoV-2 could change. A precision of 0.662 and an accuracy of around 0.671, together with a Matthews Correlation Coefficient (MCC) score of 0.313, show that the framework performs very well. These scores show that the framework performs better than a number of cutting-edge baseline techniques. The synthetic protein sequences generated by the framework not only bea

关键词： Proteins Protein engineering Biological system modeling Generative adversarial networks Transformers data models Generators

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：