检索结果-内蒙古大学图书馆

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks artificial intelligence Research and development

来源：评论

学校读者我要写书评

暂无评论

A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

引用

Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International Conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional neural networks (CNNs), Deep neural networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Exploring Parallelism in ZynqNet AI Engine to Accelerate image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

引用

IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks artificial intelligence Hardware acceleration Engines Field programmable gate arrays image classification

来源：评论

学校读者我要写书评

暂无评论

Improvement of image Classification Technique Using Transfer Learning

Improvement of Image Classification Technique Using Transfer...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Devanand Bhonsle Ruhi Uzma Priyanka Prasad Rushikesh Bankar Manjushree Nayak Sampada Vishwas Massey Tanu Rizvi Eiectrical Engineering Shri Shanakaracharya Technical Campus Bhilai India Electrical Engineering Anjuman College of Engineering & Technology Nagpur India Artificial Intelligence and Machine Learning Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India Electronics & Telecommunication Engineering G. H. Raisoni Institute of Engineering and Technology Nagpur India Computer Science Engineering NIST University Berhampur Odisha India Computer Science Engineering Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper; the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. This paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications.

关键词： Training Surveys Adaptation models Accuracy Computational modeling Transfer learning Lighting License plate recognition Tuning image classification

来源：评论

学校读者我要写书评

暂无评论

Exploring Retinal Vessel Segmentation: Deep Learning Models and Their applications

Exploring Retinal Vessel Segmentation: Deep Learning Models ...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Tanishq Soni Sheifali Gupta Komuravelly Sudheer Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Retinal vascular segmentation is essential for the early identification and monitoring of numerous ocular and systemic conditions, including diabetic retinopathy, glaucoma, and hypertension. The emergence of deep learning methodologies, particularly convolutional neural networks (CNNs) and U-Net architectures, has markedly enhanced the precision and efficacy of retinal vascular segmentation. This review paper extensively examines contemporary deep learning methodologies, concentrating on model structures, problems, and assessment measures. This paper discusses the strengths of deep learning techniques in improving upon the limitations of traditional image processing methods, such as thresholding and edge detection, while handling complex vascular structures and varied image quality. It also emphasizes the availability of publicly accessible datasets, namely DRIVE, STARE, and CHASE_DB1, as research tools in this discipline. Despite this significant progress, a lot remains to be desired, including large-scale annotated datasets, computational demand, and problems in generalization across diverse populations. Finally, the paper concluded with some future directions by integrating explainable deep learning, multi-modal imaging, and the development of lightweight models that are more appropriate for real-time clinical use.

关键词： Deep learning image segmentation Thresholding (Imaging) Reviews Computational modeling Computer architecture Real-time systems Data models Retinal vessels Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review

引用

Computers in Biology and Medicine 2025年 190卷 110072-110072页

作者： Debsarkar, Shyam Sundar Aronow, Bruce Prasath, V.B. Surya Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center OH45229 United States Department of Pediatrics College of Medicine University of Cincinnati OH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Computer Science University of Cincinnati OH45221 United States

Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research. © 2025 The Authors

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Intelligent Resource Allocation for Edge-IoT: Enhancing QoE with Deep Reinforcement Learning

Intelligent Resource Allocation for Edge-IoT: Enhancing QoE ...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Sreedhar C Sasikala V T Ram Pavan Kumar M Ruhisulthana Shaik Syed Muqthadar Ali Yadaiah Balagoni Department of CSE (AI&ML) G Pulla Reddy Engineering College Kurnool India Department of CSE Srinivasa Ramanujan Institute of Technology Ananthapuramu India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of ECE Sir C Ramalinga Reddy Polytechnic College Eluru India Department of CSE CVR College Of Engineering Hyderabad India Department of Emerging Technologies Mahatma Gandhi Institute of Technology Gandipet India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

A resource allocation framework zeroed in on Quality of Experience (QoE) that is explicitly intended for the heterogeneous edge Internet of things (IoT) environment's dynamic and fluctuated situation. applications for the Internet of Things (IoT) are confronting edge figures. Be that as it may, because of the heterogeneity of usages, the edge cloud finds it trying to relegate complex compelled resources (handling, memory, limit, information transmission, etc) with limitations in light of the Quality of Service (QoS) necessities of clients. In this exploration, we tackle the issue of resource allocation in Edge-IoT structures by supporting a canny framework called Deep Edge that assigns resources to the different IoT applications with the sole motivation behind working on the Quality of Experience (QoE) of the clients. To accomplish this objective, we create a complex QoE model that considers adjusting the different prerequisites of Internet of Things applications to the accessible edge resources. The arrangement is accomplished by choosing a QoS necessity arrive at that the accessible resources can meet. Moreover, we propose a brilliant two-stage deep reinforcement learning (DRL) move toward that successfully allots edge resources to help IoT applications and further develop QoE for people. Not at all like the famous DRL, our arrangement utilizes deep neural networks (DNN) to design the Edge-IoT state to joint resource allocation movement, which incorporates resource allocation and QoS class. Along these lines, DNN can be used to deal with exercises' exploration. The cooperative exertion not just builds the quality of experience (QoE) of clients and fulfills the prerequisites of assorted applications, yet it likewise adjusts the QoS necessities to the accessible resources.

关键词： image edge detection Quality of service artificial neural networks Information processing Deep reinforcement learning Dynamic scheduling Quality of experience Internet of Things Resource management Edge computing

来源：评论

学校读者我要写书评

暂无评论

GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

引用

International Conference on Computer, Electrical & Communication Engineering (ICCECE)

作者： Vijay Anand Kandaswamy S Joshua Kumaresan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences. Saveetha University Chennai India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent neural networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN’s ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model’s potential improvements and its broader applications in video summarisation tasks.

关键词： Deep learning image segmentation Computer vision Analytical models Computational modeling Visual analytics System performance Feature extraction Vectors artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Brain Tumor Classification Using RNN and CNN Algorithm

A Novel Approach to Brain Tumor Classification Using RNN and...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Hannah Jessie Rani R Trapty Agarwal J Jesupriya Tarang Bhatnagar Swagat Kumar Jena Mithhil Arora School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

They are aiming to establish a solution for brain tumors that are prevailing neurological diseases and subsequently leading to cancer-related deaths. Correct classification of brain tumors is important for proper diagnosis and planning treatment. Existing methods for brain tumor classification are performed manually and tend to be subjective, not to mention time-consuming. Hence, we require an automated and effective mechanism for brain tumor classification. The present study introduces a novel ensemble procedure utilizing a combination of the Recurrent neural Network (RNN) and Convolutional neural Network (CNN) for brain cancer categories. The LSTM RNN is used to analyze the time-varying features of the magnetic resonance imaging (MRI) scans, whereas 3D CNNs are exploited for spatial appearance descriptions. These two network types collaborate in the brain tumor classification process to determine each class according to its properties. This method enhances the accuracy and swiftness of brain tumor classification. They trained their RNN-CNN model using thousands of MRI scans and found that they could be used to distinguish between these different types of bratypesumors accurately. In addition, our method also decreased processing time compared with standard methods used for manual analysis. Finally, the overall results suggest that our proposed method could be an efficient automated solution for brain tumor classification with the potential to improve early diagnosis and treatment planning of patients suffering from such malignant disease. It is also readily integrated into current health systems, providing it a worthwhile position for neurologists.

关键词： Recurrent neural networks image analysis Accuracy Magnetic resonance imaging Brain tumors Brain modeling Planning Convolutional neural networks artificial intelligence Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

applications of artificial neural networks in image processing V : 25-26 January, 2001, San Jose, California / Nasser M. Nasrabadi, Aggelos K. Katsaggelos, chairs/editors;sponsored by IS&T--the Soci

Applications of artificial neural networks in image processi...

引用

2001年

作者： Nasser M. Nasrabadi Aggelos K. Katsaggelos chairs/editors sponsored by IS&T--the Society for Imaging Science and Technology [and] SPIE--the International Society for Optical Engineering publishe

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：