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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11233 条记录，以下是251-260 订阅

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Heterogeneous Event Matching Based on Event Structures 3rd

Heterogeneous Event Matching Based on Event Structures

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xuewei Han, Deshuai Gao, Zhiyuan Xu, Di Wang, Ronghao Army Engineering University of PLA Jiangsu Nanjing China Rocket Force Engineering University of PLA Shanxi Xian China Engineering Quality Supervision Centers of Beijing Haidian District Beijing China

ISBN: (纸本)9789819616978

Currently, a large amount of heterogeneous event data with opaque event names (e.g., obscured IDs) are increasingly generated, e.g., the blockchain technology requires the transaction information to be recorded by different stakeholders in various ways from different blocks (i.e., distributed ledgers). Thus, a large amount of heterogeneous event data with opaque event names (e.g., obscured IDs) is generated on blockchains. To identify similar workflows on blockchains for collaborator selection, matching heterogeneous events is an important task. Existing techniques are inappropriate because they do not make full use of workflow features in event logs. We observe that several types of event constraints in event logs may serve as more discriminative features in event matching. Specifically, a general matching approach is proposed for identifying a right mapping that maximizes the similarity of event constraints for a pair of heterogeneous event logs. An advanced A* algorithm with a tight upper bound function is devised to improve the matching efficiency of the proposed approach. We conducted exr5tensive experiments to evaluate the effectiveness of our approach, and the experimental results demonstrate that our approach outperforms state-of-the-art matching approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed ledger

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Tea Bud Identification and Picking Location Using Improved Yolov7 Algorithm 3rd

Tea Bud Identification and Picking Location Using Improved Y...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Liu, Hongwei Zhang, Jingui Yang, Yongjun Gong, Daoxiong Faculty of Information Technology Beijing University of Technology Beijing100124 China Agricultural Comprehensive Service Center Liangshi Tang Sub-District Office Shaodong City Hunan Shaoyang422800 China Hunan Tea Research Institute Hunan Academy of Agricultural Sciences Changsha410125 China

ISBN: (纸本)9789819624676

Tea is an important cash crop. Because the tea shoots in the tea garden are very similar to the old leaves, and the environment of tea buds in tea gardens is complicated, the difficulty of picking tea buds is increased. Aiming at the problems that tea buds are tough to identify and the picking points are challenging to locate in the complex environment in reality, this paper proposed a detection model based on Yolov7 for tea bud recognition and key point location. In this model, some conventional convolutional modules are changed to Diverse Branch Block (DBB), which can enrich the feature space and obtain more feature information of tea buds. The GE attention convolution module is incorporated to the neck network to boost the model's use of contextual features, so as to accurately locate the key point of the tea bud. The ELAN module is replaced by P-ELAN module, which reduces the redundant calculation and the size of the model. The outcomes of the experiment demonstrate that the mean Average Precision (mAP) of tea buds and their key points is increased by 3.81% in contrast to the original Yolov7. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Key point detection P-ELAN module Target detection tea buds picking

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Biometric Authentication with Blockchain: A Secure and User-Friendly Approach 3rd

Biometric Authentication with Blockchain: A Secure and User-...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Subburaj, T. Raju, K. Srujan Suthendran, K. Kumar, Kovuri Praveen Rekha, Ch. Sultana, Saba Deeraj, C. Department of Master of Computer Applications Rajarajeswari College of Engineering Bengaluru India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of Information Technology Kalasalingam Academy of Research and Education Tamilnadu Krishnankoli India

ISBN: (纸本)9789819783540

"Biometric Authentication with Blockchain: A Secure and User-Friendly Approach" proposes a novel system, a Blockchain-based Distributed biometric Authentication System (BDAS), to address vulnerabilities in existing biometric authentication methods. The primary concerns tackled include the leakage of biometric information, irregularity in authentication modules, and non-transparency in information management. BDAS, leveraging blockchain technology, establishes a decentralized and distributed authentication system, ensuring reliable and secure authentication while minimizing performance overhead. The authors present a detailed exploration of biometric authentication, emphasizing the immutability of biometric data and the challenges associated with centralized management. They introduce BDAS as a solution that utilizes blockchain to enhance security, reliability, and transparency in biometric authentication. The paper includes an insightful evaluation of BDAS, comparing its performance with conventional authentication systems. The proposed system exhibits increased reliability in scenarios involving disabled clients, outperforming traditional systems. The authors conclude by highlighting BDAS's contributions to securing biometric information and propose future enhancements, such as optimizing template segmentation processes and considering the number of nodes for improved reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Road Anomaly Detection Method Based on RADNet 3rd

Road Anomaly Detection Method Based on RADNet

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Yide Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

As the demand for road inspection continues to increase, traditional manual inspection methods are insufficient for large-scale road inspections. Researchers continue to propose new detection methods for road anomalies. However, existing detection methods are not effective enough because of the varied types, scales, and complex backgrounds of road anomalies. Therefore, this paper proposes a road anomaly detection method RADNet-FG based on deep learning, aiming to assist inspection personnel in completing routine inspections and key ***, we propose a RADNet algorithm for routine inspections, including Deformable Feature Extraction module (DFE), Coordinate Attention module (CA), and Directly Connected Feature Pyramid Network (DCFPN). Then, we cascade the Fine-grained Classification module (FG) on the RADNet algorithm to form RADNet-FG algorithm, which enhanced the classification ability of RADNet. Finally, experiments on our datasets show that in routine inspections, RADNet algorithm achieves a precision rate of 93.1%, a recall rate of 86.5%, and a mAP of 91.6%, which is better than Faster R-CNN, YOLOv8, YOLOv9, etc.;In key inspections, RADNet-FG algorithm achieves a precision rate of 95.6%, a recall rate of 48.3%, and a mAP of 90.1%, which are 5.0%, 3.5%, and 15.4% higher than RADNet without fine-grained classification, and are 10.6%, 2.2% and 21.3% higher than YOLOv8. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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AI-Driven Symptom Analysis: Enabling Early Disease Prediction Through Chatbot Interaction 3rd

AI-Driven Symptom Analysis: Enabling Early Disease Predictio...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Prasannakumar, P. Paul, Jaydev Vani, V. Karthik, N. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

ISBN: (纸本)9789819783540

Given the current environment, in which the appearance of fresh diseases has become a regular occurrence and human immunity seems to be declining, it is critical that basic healthcare services be made available to everyone on the planet. In order to provide health care to individuals, this work offers a novel model that makes creative use of an AI-driven chatbot. The prediction model that powers the chatbot’s functioning is based on historical data about different diseases, the symptoms that go along with them, and how serious these illnesses are. Through the integration of historical data and user-provided real-time symptom data, the chatbot functions as a potent instrument for very accurate disease prediction. Its unique strength lies in its capacity to merge medical records from the past with current data, providing customers with the most precise and trustworthy illness prediction. This research further investigates the concept of AI-Driven Symptom Analysis, a novel method that enables early disease prediction via conversational interactions with the chatbot. In an era marked by changing health threats and weakened human immune systems, this two-pronged approach, which incorporates both historical data and current health information, represents a major advancement in our quest to improve access to healthcare and proactively tackle emerging health challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Ornithology Species Edge Detection Using Soft Computing Techniques 3rd

Ornithology Species Edge Detection Using Soft Computing Tech...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Himabindu, K. Deepak, Yadagiri Rama Department of CSE Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Department of CSA Parul Institute of Computer Applications Parul University Gujarat Vadodara India

ISBN: (纸本)9789819783540

A common hobby that provides relaxation from daily life is bird watching. In nature, birds are present. They support the environment and ecosystem. Given how visually similar many bird species are, it is a difficult challenge. As a practical aid for birdwatchers to recognize the species in their native habitat, for humans, it might be difficult to identify different bird species. From a human perspective, it is only natural for birds to appear in a variety of situations in a variety of sizes, shapes, colors, and perspectives. Some bird species are now only sometimes discovered, and even then, it can be challenging to classify them. Additionally, photos offer more distinct variants for bird species identification than audio categorization. The ability of humans to identify the birds from the images is also easier to understand. To identify the species from one another, shape feature is important. Edge detection is a key component of shape extraction. Edge is a significant visual feature that uses a limited number of pixels to communicate visual information. Because of climate factors, it can be uncertain to assess a bird's morphology. Therefore, in this study, we used the Bird Image Edge Detection using Fuzzy Algorithm (BIEDF) to implement this model to extract data from bird images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropogenic

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MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm 3rd

MRI-Based Brain Tumour Detection and Classification Using Ra...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Jareena Begum, D. Chokkalingam, S.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783540

A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image segmentation technique and a classifier. One such classifier is the Random Forest Algorithm. The Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT) are also employed. The created method is put to the test using a dataset available on the Kaggle platform. The acquired results were approximately 97.68% accurate. A confusion matrix and a comparison of the developed method to other research in the literature are included in the investigation to facilitate the implementation of the proposed strategy in image testing. This Research shows that the offered system is superior to others in terms of detection rate, false positive rate, and recall. Results from this study showed that the developed method was effective at detecting brain tumours due to its high levels of accuracy, precision, and recall. According to the proposed strategy, it is essential to provide professionals who diagnose brain tumours with access to such technology. © The Author(s) 2025.

关键词： Image segmentation

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A Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System 3rd

A Privacy Preserving Decision Tree Classification Algorithm ...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Raj, Thaluka Mohan Maheswari, K. Raju, K. Srujan Shweta, N. Dharmireddi, Srinivasarao Reddy, Avala Raji Department of Computer Science and Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789819783540

In recent years, health monitoring systems have become popular due to increasing medical costs and advances in wireless technology. Organisations use information systems to develop and distribute information as well as gather, filter, and process data. Patients are responsible for sharing information with particular types of healthcare providers in order to make an accurate diagnosis and decide on the most appropriate model of treatment. In health monitoring systems, hospitals outsource clinical decision-making models to cloud service providers, who then use the models to make clinical decisions based on the biomedical information they receive from remote clients. Biomedical data as well as clinical decision-making models must be protected since privacy issues are extremely serious. An effective categorization technique for health monitoring systems using a privacy-preserving decision tree (PPDT) is given in this analysis. Decision tree classification with privacy preservation is carried out by using the encrypted token to search the encrypted index. According to a performance analysis, PPDT is extremely effective in terms of accuracy, time, storage, and communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Enhancing Grayscale Image Clarity Through Blind Deconvolution and PSF Estimation With Total Variation Regularization 3rd

Enhancing Grayscale Image Clarity Through Blind Deconvolutio...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Narasimharao, Jonnadula Kumar, Voruganti Naresh Kirankumar, Adepu Pooja, Bejjanki Rambabu, D. Joshi, Ganpat Department of CSE CMR Technical Campus Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of Computer Science and Engineering Madhav University Rajasthan India

ISBN: (纸本)9789819783540

Nowadays, the usage of digital cameras in daily life has increased, leading to a growing demand for enhancing the quality of captured images. One of the most challenging problems in image processing is the restoration of blurred or out-of-focus images. This paper presents Enhancing grayscale image clarity through blind deconvolution and PSF estimation with total variation regularization. This blind deconvolution approach is used to handle the problem of removing blur from acquired photos, making it easier to restore defocused grayscale photographs. Determining the ideal picture and the point spread function (PSF) may be challenging when using blind deconvolution, which aims to recreate a clear ideal image from a single fuzzy image. As a result, this work also introduces a unique PSF estimate technique that makes use of an adaptive filter and total variation regularization. Finally, the results obtained after implementation are demonstrated based on the blur type and performance metrics, which include the mean square error and the peak signal-to-noise ratio. The experimental outcomes indicate that our method is the best among various other methods in terms of deblurring performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive filtering

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Deep Ensemble Learning Approach for Multimodal Emotion Recognition 3rd

Deep Ensemble Learning Approach for Multimodal Emotion Recog...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dave, Maheak Mukherjee, Shivesh Krishna Singh, Pawan Kumar Mahmud, Mufti Department of Computer Science and Engineering Techno India University West Bengal Kolkata700091 India Department of Information Technology Jadavpur University Jadavpur University Second Campus Salt Lake City Plot No. 8 Salt Lake Bypass LB Block Sector III West Bengal Kolkata700106 India School of Science and Technology Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University NottinghamNG11 8 NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University NottinghamNG11 8 NS United Kingdom

ISBN: (纸本)9789819739363

In this research initiative, our central objective is the development of an emotion recognition system through the utilization of cutting-edge deep learning methodologies. Our methodology involves adopting a subject-independent approach to classify emotions based on Electroencephalogram (EEG) signals sourced from a standard benchmark DEAP dataset. To ensure data quality and reliability, we initiate our process by meticulously preprocessing the raw signal data, which includes the application of Normalization and Common Average Reference (CAR) techniques. Subsequently, we employ Discrete Wavelet Transform (DWT) technique to extract salient features from the cleaned EEG data. These extracted features serve as the foundation for training three distinct deep learning models: the CNN-LSTM, CNN-GRU, and 2D-CNN models. To consolidate their predictive capabilities, we employ a Majority voting algorithm, effectively combining the strengths of these models. Notably, our proposed deep ensemble learning approach yields an impressive accuracy rate of 88% when evaluated on the challenging DEAP dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete wavelet transforms

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