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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4811 条记录，以下是4791-4800 订阅

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Quantum Computational Intelligence with Generative AI image for Human-Machine Interaction

Quantum Computational Intelligence with Generative AI Image ...

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ieee international conference on Fuzzy Systems (FUZZ-ieee)

作者： Chang-Shing Lee Mei-Hui Wang Jun-Kui Chiang Naoyuki Kubota Eri Sato-Shimokawara Yusuke Nojima Giovanni Acampora Pei-Yu Wu Szu-Chi Chiu Sheng-Chi Yang Chyan-Zheng Siow National University of Tainan Tainan Taiwan Tokyo Metropolitan University Tokyo Japan Osaka Metropolitan University Japan University of Naples Federico II Italy

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

This paper introduces a Quantum Computational Intelligence (QCI) agent equipped with a content attention ontology model, specifically designed to enhance human-machine interaction based on a Generative Artificial Intelligence (GAI) image generation agent for Taiwanese/English learning and experience. Its diverse primary applications include social media analysis on Facebook groups and YouTube learning videos related to the 2023 ieee CIS Education Portal (EP) Subcommittee, as well as in the areas of Taiwanese/English language learning and dialogue experience with GAI image generation. To establish the knowledge and inference models for the QCI agent, we initially developed a Taiwanese/English learning and experience ontology, including a content attention ontology, and an image attention ontology. The QCI agent utilizes metrics such as the number of views, posts, and comments to predict the fuzzy number of reactions. In addition, the GAI image agent generates Taiwanese speech-based/English text-based images and evaluates the fuzzy similarity score between Taiwanese/English and the attention ontology together with the Sentence BERT (SBERT) agent. This Taiwanese/English fuzzy similarity score is further validated through human assessments, with these evaluations subsequently serving as an additional metric for comparative analysis of Human-Machine Interaction (HMI). Furthermore, the GAI image agent is designed to create images and Chinese/English texts from text/speech translated by the Meta AI Universal Speech Translator (UST) Taiwanese/English agent. A Particle Swarm Optimization (PSO)-based machine learning mechanism is employed to train the QCI model for assessing learners' performance and predicting the performance of others. The National University of Tainan (NUTN) Taiwan-Large Language Model (***-LLM) agent has been further enhanced to support interactive learning experiences for HMI. An SBERT-based assessment agent is used to calculate fuzzy similarities be

关键词： Human computer interaction Measurement Quantum computing Social networking (online) image synthesis Generative AI Computational modeling

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Topological Dependencies in Deep Learning for Mobile Edge: Distributed and Collaborative High-Speed Inference

Topological Dependencies in Deep Learning for Mobile Edge: D...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Yousef Methkal Abd Algani A Suresh Kumar Md. Abul Ala Walid Balu S Priya Velayutham A. Sasi Kumar Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tamil Nadu India Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamil Nadu India Inurture Education Solutions Pvt Ltd Bangalore Department Of Cloud Technology & Data Science Institute Of Engineering & Technology Srinivas University Karnataka State India

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. Distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Deep learning Training Cloud computing Fault tolerance image edge detection Computational modeling Fault tolerant systems

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Parallel Pattern Matching over Brotli Compressed Network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

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ieee international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

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JiShiShan Mw 6.2 Earthquake Monitoring by Means of Lutan-1 SAR Data and Optical Data

JiShiShan Mw 6.2 Earthquake Monitoring by Means of Lutan-1 S...

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Signal, Information and Data processing (ICSIDP), ieee international conference on

作者： Xiang Zhang Hui Zhao Tao Li Xiaoqing Zhou Sen Lv Xuefei Zhang Land Satellite Remote Sensing Application Center Ministry of Nautral Resources of P.R. China Beijing China National Geomatics Center of China Beijing China Land Satellite Remote Sensing Application Center Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

On December 18, 2023, a 6.2-magnitude earthquake hit Jishishan County, Linxia Prefecture, Gansu Province, followed by several aftershocks. The earthquake caused a large number of house collapses and casualties. Interferometric Synthetic Aperture Radar technique can obtain multiple dimensional information for geo-hazard management. Lutan-1 SAR satellites with accurate orbit control ability are the first group L-band SAR constellation in China, which provide the potential for deformation monitoring with centimeter even millimeter accuracy. Based on the pre-earthquake and post-earthquake Lutan-1 SAR data and Gaofen-7 optical data, the information of coseismic deformation, debris flow and slope collapse were extracted and analyzed. It provides important data support for post-earthquake rescue and post-disaster reconstruction. The coceismic deformation field characterized the typical deformation induced by the earthquake, and the maximum deformation can reach to 8 cm. The debris flow area showed obvious change on the SAR data and optical data, including spectral information and backscattering information. In combination with the application of Lutan-1 and Gaofen-7 data, the earthquake and secondary disaster can be effectively monitored.

关键词： Integrated optics Optical interferometry image motion analysis computer vision Accuracy Deformation Earthquakes Adaptive optics Synthetic aperture radar Monitoring

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2nd international conference on Computing, Communication, and Learning, CoCoLe 2023

2nd International Conference on Computing, Communication, an...

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2nd international conference on Computing, Communication, and Learning, CoCoLe 2023

ISBN: (纸本)9783031569975

The proceedings contain 25 papers. The special focus in this conference is on Computing, Communication, and Learning. The topics include: Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress;stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques;nearest Neighbor and Decision Tree Based Cloud Service QoS Classification;object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment;detection of Malicious Activity on Credit Cards Using Machine Learning;agent-Based Modeling for Optimum Location Selection: Enhancing Day-Ahead Market Analysis for Microgrid Integration;a Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony;a Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer;unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic;sentiment Analysis of Crypto Currency Trading applications in India Using Machine Learning;Graph Similarity Join (GSJ) Approach to Detect Near Duplicate Text Documents;three Factor Authentication Scheme for Telecare Medical Information System;an Effective Threshold Based Technique for Retinal image Blood Vessel Segmentation on Fundus image Using Average and Gaussian Filters;substantiation System Using Facial Recognition and Principal Component Analysis;impact of Stress on Faculties’ Attitudes: A Study of Higher Education Institutes;dynamic Cross-Layer Communication Design for Multi-objective Optimization in Wireless Sensor Networks;Implementation of Weighted Parallel Interference Cancellation Technique for Multi User Detection in UWA Communication;analysis and Comparison of Firefly Algorithm for Measuring Convergence Rate in Distributed Environment;A Comparative Study on Social Behavior of Tribes and Non Tribes in ITDA Paderu by Using Network Science;raster Big Data processing Using Spark with GeoTrellis.

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Novel hybrid classifier synergizing 3D_2D CNN with Support Vector Machine to enhance Hyperspectral image classification

Novel hybrid classifier synergizing 3D_2D CNN with Support V...

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international conference on Computing and Networking Technology (ICCNT)

作者： Sridevi S V V V Bhagya Sree Sudarshan R PrasannaKumar R Shanmugakumar M Balasubramanian E Surendar G Balachandran S Kanimozhi T Jae Sung Choi Rithani M SCOPE Vellore Institute of Technology Chennai Computer Science and Engg. Amrita School of Computing Chennai CSE Amrita School of Computing Chennai MATIC IIITDM Kancheepuram Mechanical Engineering NITTR Taramani Chennai Aeronautical Engg VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Central Library Amrita School of Computing Chennai ECE VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of CSE Sun Moon University Asan-si Korea

Hyperspectral imaging (HSI) is a potent tool that offers accurate identification and analysis of materials and objects in various domains ranging from remote sensing, agriculture, chemo-metric analysis for agricultural products and bio-medical applications, and much more. However, accurately categorizing HSI images remains challenging due to high dimensionality pertaining to spectral resolution in multiple bands, inadequate training samples, and pixel mixing. Deep learning Convolutional Neural Networks (CNN) have undergone development over time to process data of varied dimensions as 1D CNNs were developed to handle time series data, while 2D CNNs operate on spatial image data, and 3D CNNs evolved for processing 3D volumetric data. This paper proposes a synergistic hybrid classifier model that generalizes the 3D_2D CNN deep learning model to extract features from HSI images automatically and a robust Support Vector Machine (SVM) classifier to classify them accurately with less computational efficiency. The prominent intention of this proposed classifier is to be robust in terms of efficiency and less computationally complex. The proposed method involves HSI image dimensionality reduction using principal component analysis (PCA) and 3D_2D convolutional layers to extract significant spectral-spatial features. These feature maps are flattened, which is then used to train the SVM classifier. On the Kennedy Space Centre (KSC) HSI dataset, experimental results demonstrate that the proposed hybrid technique provides excellent accuracy and outperforms the modern techniques regarding classification precision and computational effectiveness. The proposed hybrid 3D 2D CNN synergized with the SVM approach achieved 99.8% accuracy, which is a 1.31% improvement compared to the existing Cube CNN with an SVM similar kind of hybrid classifier.

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Increase Patients’ Survivability During Emergency Care Using Blockchain-Based Digital Twin Technology

Increase Patients’ Survivability During Emergency Care Usin...

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international conference on Software Engineering Research, Management and applications (SERA)

作者： Shirin Hasavari Yeong Tae Song Benjamin Lawner dept. Information Science & Systems School of Business & Management Morgan State University Baltimore United States dept. Computer& Information Science Towson University Towson United States dept. Emergency Medicine School of Medicine University of Maryland Baltimore United States

ISBN: (数字)9798350345889

ISBN: (纸本)9798350345896

Effective communication of patient clinical and care information between pre-hospital services and Emergency Departments (EDs) is crucial for the rapid and effective treatment of patients, potentially saving lives. Recent research indicates a frequent lack of patient data metrics, such as vital signs, which represents a potential limitation in the comprehensiveness of handoffs received by ED physicians from pre-hospital providers. To address this challenge, we propose a solution that utilizes a patient's digital twin and permissioned blockchain technology to ensure complete, real-time, secure, and shareable access to patients' vital signs and other data metrics for Emergency Departments. This solution seeks to answer the following questions: Which data model for the digital twin best represents the patient during transport, meeting the requirements of the receiving facility? How can patient digital twin data, including the history of clinical information and patient care, be shared in real-time with the receiving facility? How can EHR-compliant digital twin data be produced to satisfy the reconciliation between the Patient Digital Twin (PDT) and the Electronic Health Record (EHR)? Method: We conducted a comprehensive literature review and a series of interviews with both pre-hospital and hospital care providers to identify the problem and develop a model based on it. To address the second question, we designed an architecture that includes all parties in the care team, during and after the transport. For the third question, we explored NEMSIS data exchange standards and a Natural Language processing (NLP) Module. Objective: Our goal is to improve the survivability of patients in emergency care through secure, effective, and real-time sharing of patient metric data between Emergency Medical Services (EMS) and Emergency Departments or Trauma Centers. This approach also aims to reduce the waiting time for patients upon arrival at the receiving facility.

关键词： Measurement Hospitals Real-time systems Natural language processing Data models Digital twins History

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Leveraging Vote and Cooperate Mechanism for Brain Tumor Segmentation

Leveraging Vote and Cooperate Mechanism for Brain Tumor Segm...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Cunhan Guo Heyan Huang Changsen Yuan Yang-Hao Zhou Southeast Academy of Information Technology (of Beijing Institute of Technology.) School of Emergency Management Science and Engineering (of University of Chinese Academy of Sciences.) Beijing China School of Computer Science and Technology (of Beijing Institute of Technology.) Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The task of brain tumor segmentation necessitates the processing of long Magnetic Resonance Imaging (MRI) sequences across multiple imaging modalities. Traditional convolutional neural networks often exhibit suboptimal ability of long-term memory, while Transformers demand substantial computational resources. To mitigate computational requirements and optimally utilize the information from various imaging modalities, we propose Mamba-based Vote and Cooperate Segmentation (VCSeg), for brain tumor segmentation. Features from the imaging sequences of each modality are extracted from multiple spatial orientations, and assigned different weights based on both modality and orientation considerations, enabling the model to adaptively learn modality-specific feature distribution for adult and child patient situation. The model employs a multi-modal cooperate module to further enhance the feature encoding capabilities. Additionally, deep supervision is applied to achieve progressive mask generation, thereby improving decoding quality. Experiments conducted on the BraTS2019-MEN and BraTS2023-PED datasets have demonstrated that VCSeg achieves state-of-the-art results, demonstrating the effectiveness of our advanced method.

关键词： Training image segmentation Adaptation models Pediatrics Magnetic resonance imaging Brain tumors Feature extraction Brain modeling Transformers Decoding

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GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

GNet: 3D Object Detection from Point Cloud with Geometry-Awa...

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ieee international conference on Cyborg and Bionic Systems (CBS)

作者： Hao Ruan Bingrong Xu Junbin Gao Lianguang Liu Junting Lv Yin Sheng Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Automation Wuhan University of Technology Wuhan China

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel highperformance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird’s Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.

关键词： Point cloud compression Location awareness Geometry Three-dimensional displays Neural networks Object detection Proposals

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Detection of Brain Tumour using CNN in Federated Machine Learning

Detection of Brain Tumour using CNN in Federated Machine Lea...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Siddharth Bhadauriya Tanishk Merothiya Shiv Chandra Yadav M. ChandraPrabha Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Galgotias College of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Brain tumours are a deadly illness that kills many people. The earlier a brain tumour is detected, the better the treatment, and the longer the patient lives. Artificial intelligence (AI), which has recently emerged as a promising strategy for the early identification of cancers, is a crucial subject of research in the diagnosis of brain tumours. The implementation of brain tumour makes advantage of federated machine learning. It’s a method for building AI models without allowing anybody else to view or access your data, acting as a conduit for making data accessible to new AI applications. Federated learning, which is comparable to a group presentation or report, allows for remote data sharing by several persons in order to cooperatively and iteratively train a single deep learning model. Each participant downloads the model, which is typically a foundation model that has already been trained, from a cloud datacenter. CNN is the algorithm in use. Convolutional neural networks are a specific class of multi-layered neural networks. It evaluates data that is laid out in a grid-like pattern and then draws out important traits. The significant advantage of CNN use is that it requires less photo preprocessing. The suggested technique yields rapid and exact tumour site identification in addition to accurate tumour detection in the brain. This process has allowed us to attain 96% accuracy.

关键词： Deep learning Federated learning Hospitals Magnetic resonance imaging image processing Data models Convolutional neural networks

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