检索结果-内蒙古大学图书馆

Research 2024年第4期2025卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

来源：评论

学校读者我要写书评

暂无评论

Efficient semantic segmentation of radar sounder data 30

Efficient semantic segmentation of radar sounder data

引用

Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024

作者： Yebasse, Milkisa T. Bruzzone, Lorenzo Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TrentoI-38123 Italy

ISBN: (纸本)9781510681002

Radar sounders are active sensors that operate by transmitting electromagnetic waves toward the subsurface of a target area and capturing the reflected signals. The reflected signals are then processed to create images or maps of the target subsurface. Supervised deep learning techniques have emerged as powerful tools for radar sounder data analysis. However, they require a significant amount of labeled data, which is challenging given both the difficulty of acquiring such data in specific subsurface environments and the cost of labeling them with complex photointerpretation procedures. Recently, some methods have been proposed to segment the radargrams by employing deep complex models or pretrained networks. However, these methods may lead to models that are too complex for the problem and computationally inefficient. Thus, we present a computationally efficient u2net model that combines u2net and octave convolution. This combination offers the advantage of a deep efficient architecture with rich multi-scale features while keeps computational and memory requirements relatively low. The results show the efficiency and adaptability of our model to the availability of limited labeled data and its generalization capabilities when augmented with additional data. Furthermore, our proposed model significantly reduces the number of parameters used compared to existing methods for radar sounder segmentation. © 2024 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Position paper: On the role of abductive reasoning in semantic image segmentation 22

Position paper: On the role of abductive reasoning in semant...

引用

22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA DP 2022

作者： Rafanelli, Andrea Costantini, Stefania Omicini, Andrea Department of Computer Science University of Pisa Italy Department of Information Engineering Computer Science and Mathematics University of l'Aquila Italy Department of Computer Science and Engineering Alma Mater Studiorum Università di Bologna Italy

This position paper provides insights aiming at resolving the most pressing needs and issues of computer vision algorithms. Specifically, these problems relate to the scarcity of data, the inability of such algorithms to adapt to never-seen-before conditions, and the challenge of developing explainable and trustworthy algorithms. This work proposes the incorporation of reasoning systems, and in particular of abductive reasoning, into image segmentation algorithms as a potential solution to the aforementioned issues. © 2021 Copyright for this paper by its authors.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Ontology-Based Reinforcement Learning for Semantic Offloading in MEC-Enabled 6G IoT Networks

Ontology-Based Reinforcement Learning for Semantic Offloadin...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gyamfi, Eric Ansere, James Adu Golpayegani, Fatemeh School of Computer Science University College Dublin Ireland Sunyani Technical University Department of Electrical Engineering Ghana

ISBN: (纸本)9798350364910

The proliferation of ultra-low-latency and faster convergence applications in sixth-generation (6G) networks necessitates mobile edge computing (MEC) for offloading computationally intensive tasks from user devices to the network edge. However, traditional offloading approaches ignore the semantic aspects of tasks and resources. This paper proposes a novel ontology-based reinforcement learning (ORL) approach for optimal semantic offloading decisions in evolving dynamic MEC-enabled 6G networks, considering network uncertainty and time variation conditions. The proposed ORL approach integrates a dynamic learning mechanism that adjusts and captures semantics task resources, enabling adaptive real-time offloading decisions to satisfy task processing requirements and user demands. The numerical results demonstrate significant improvements in latency reduction, responsiveness, and adaptability compared to traditional offloading approaches. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Dependable Mobile Federated Learning Classifications Focused for Mobile Intelligent Source Distribution System in Big Data Analytics using Artificial Internet of Things 8

Dependable Mobile Federated Learning Classifications Focused...

引用

8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Anitha, G. Jegatheesan, A. Baburaj, E. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India BuleHora University College of Engineering and Technology Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350347791

Latest Mobile Federated learning (MFL) is an enormous procedure for confidentiality and limitations using Artificial Internet of Things (AIOT) tenders in Future-generation mobile networks (FGMN), the latest generation of 6G and 5G is created on the Latest radio (LR) knowledge, the Many-In(data) and Many-Out(data) (MIMO) amenities assorted for AIOT strategies have been accomplished. The independent reserve distribution then the smart excellence of facility group uniqueness in mobile networks founded on MFL schemes are grateful to encounter the rations of secrecy limitations of AIOT applications. In enormous MFL infrastructures, the heterogeneous local strategies broadcast their representations in locally and the parameters accomplished 5G and 6G networks to the aggregation servers in edge devices. Therefore, the quality of network consistency is required to simplify the NodeEnd-to-NodeEnd (NE2NE) consistency of MFL transportations deliver that gratification of various choices. This paper proposed a Mobile smart trivial system built the orientation Domain-defined networking (DDN) architecture to grip more huge MFL transportations upon customers as well as collectors for encounter stated viewpoints. The management technique regulates the typical restrictions and groups extent of the discrete consumer to reproduce the deceptive network circumstances secret by the Node K-nearest neighbours (NKNN) algorithm. The proposed scheme presented famous verified metrics, counting the NE2NE MFL message latency, quantity, system dependability, and model correctness. © 2023 IEEE.

关键词： Data Analytics

来源：评论

学校读者我要写书评

暂无评论

Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images 24

Dual Cross-Stage Partial Learning for Detecting Objects in D...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

来源：评论

学校读者我要写书评

暂无评论

BARRIERS TO SUSTAINABLE SYSTEM EVOLUTION: A SIMULATION STUDY EXPLORING THE TRANSITION FROM PRIVATE TO PUBLIC TRANSPORTATION

BARRIERS TO SUSTAINABLE SYSTEM EVOLUTION: A SIMULATION STUDY...

引用

ASME 2024 International Design engineering Technical Conferences and computers and information in engineering Conference, IDETC-CIE 2024

作者： Otalvaro, Rogelio Gracia Watson, Bryan C. Electrical Engineering and Computer Science Department Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9780791888391

Road transport networks are complex distributed systems that present the most challenges in the management and use phase of their lifecycles. As traffic congestion and air pollution within urban city centers rise, authorities are implementing more restrictions and regulations to address these challenges. Additionally, public transportation, car-sharing, sustainable vehicles, and Mobility-as-a-Service have all increased in popularity as solutions to these issues. However, there is evidence that these new trends might not help as expected. The main objective of this study is to understand the dynamics of transportation network evolution from a systems perspective. Focusing on system capacity, and population characteristics, we seek to evaluate how different ratios of users commuting with either public transport or private car affect network congestion. The hypothesis tested is: as the ratio between public buses and private cars is modified to include more buses, the dynamics of the transportation system change, impacting system evolution towards more sustainable modes of transportation. Contributions are conceptual models documented through the overview, design concepts, and details protocol, as well as an analysis of the results obtained. The results indicate the evolution path taken toward more sustainable transportation can severely mitigate system-level objectives. Significant recommendations to city planners are included to help manage the complexity of transport systems, leading to the design and operation of smarter and sustainable cities. Future research will focus on customizing the model to represent real cities and determine if the same system evolution guidance applies. Copyright © 2024 by ASME.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

引用

Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

引用

SN computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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：