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共 11565 条记录，以下是501-510 订阅

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Enhancing Cardiovascular Diagnostics with YOLOv8: A Deep learning Approach to Focal Anomaly Detection in Angiogram Imaging 4

Enhancing Cardiovascular Diagnostics with YOLOv8: A Deep Lea...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Begum, Farhana Uddin, Y.Md Riyaz Department of Cse Hyderabad India Vardhaman College of Engineering India

ISBN: (纸本)9798331529635

Fast and precise diagnosis techniques are required since cardiovascular illnesses are one of the main causes of death globally. For the purpose of improve the rate of detection and diagnosis even further, this paper applies an advanced deep learning technique by employing the YOLOv8 algorithm, with a particular focus on the 'focal' class of the 'Cardiovascular-Angiogram-dataset.' Using the most recent object detection model, YOLOv8, the Angiogram images were examined and achieved precision is 0.99, recall is 0.99, and F1 score is 0.98. With a score of 0.99, the Precision-Recall Curve likewise showed excellent performance. these findings demonstrate how well the system detects focused cardiovascular problems, greatly improving diagnostic precisison. the results of this study imply that deep learning models, such as YOLOv8, can play a critical role in clinical settings by providing accurate, real-time diagnosis. Subsequent research endeavours will investigate the assimilation of this model into more comprehensive diagnostic frameworks and its possible utilisation in a range of cardiovascular ailments to enhance patient outcomes. © 2024 IEEE.

关键词： Angiocardiography

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CSOFS: Feature Selection Using Cuckoo Search optimization Algorithm for Software Fault Detection 4

CSOFS: Feature Selection Using Cuckoo Search Optimization Al...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Rath, Pradeep Kumar Mahato, Susmita Bhowmik, Rwiddhi Gourisaria, Mahendra Kumar Das, Himansu Nist Institute of Science & Technology School of Computer Science & Engineering Berhampur India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

the method of identifying and predicting software flaws or problems in software systems involves using historical data. Software fault prediction is a crucial topic of research in the field of software engineering because it enables developers to find probable flaws early on, which can save money and enhance the quality of the programme produced. the study's objective is to determine how well the Cuckoo Search optimization Algorithm can extract the features that are most important for correctly predicting software flaws. the prediction models were tested and trained using a dataset of software metrics, and the dataset was subjected to a variety of feature selection algorithms. We have performed an analysis by comparing the efficiency of various optimization algorithms namely evolutionary based Genetic Algorithm Feature Selection (GAFS), Differential Evolution Feature Selection (DEFS), and swarm based algorithms like Ant Colony optimization Feature Selection (ACOFS) and Particle Swarm optimization Feature Selection (PSOFS) with respect to Cuckoo Search optimization Algorithm (CSOFS). the results from the output tables for various metrics prove that feature selection using CSO can significantly increase the accuracy of the predictive model, as well as reduce the complexity of the models. this study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient. © 2024 IEEE.

关键词： Feature Selection

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Energy Efficiency optimization in the Electricity Market: A Game-theoretic Approach to Sustainable Power Systems 4

Energy Efficiency Optimization in the Electricity Market: A ...

引用

4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Jiao, Dongxiang Zhang, Ru Zhang, Bozhi State Grid Jibei Electric Power Co. Ltd. Metering Center Beijing100055 China

ISBN: (纸本)9798331523923

Power system optimization is an important research topic in the era of energy shortage, and improving the energy efficiency of the power market can directly address this challenge. Inspired by this, this study proposes a model for improving energy efficiency in the electricity market based on game theory, and comprehensively optimizes the model based on an in-depth analysis of the game model. First, as a basic chapter, a master-slave game model involving energy operators, service providers and demand-side users in the power market is studied and constructed. this game model can systematically analyze the energy efficiency coordination algorithm in the multi-level power market. Subsequently, an improved cooperative game method is introduced to analyze the cooperative transaction mechanism between micro-grids. the profit distribution strategy is optimized from the system level, and the game algorithm is improved. Finally, an innovative design algorithm for the reward and punishment coefficients based on energy efficiency indicators was proposed, thereby promoting energy efficiency improvements of market players through electricity price incentives. Systematic experiments confirmed that compared with traditional optimization methods, the proposed algorithm improved load balancing, power dispatching cost and system energy efficiency by 15.6%, 12.3% and 18.7% respectively, reflecting the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： Electricity Market Energy Efficiency Game theory optimization Model

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Curriculum-enhanced GroupDRO: Challenging the Norm of Avoiding Curriculum learning in Subpopulation Shift Setups 4

Curriculum-enhanced GroupDRO: Challenging the Norm of Avoidi...

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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Barbalau, Antonio Bitdefender Deep Learning Research Unit Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Bucharest Romania

ISBN: (纸本)9798331531225

In subpopulation shift scenarios, a Curriculum learning (CL) approach would only serve to imprint the model weights, early on, with the easily learnable spurious correlations featured. To the best of our knowledge, none of the current state-of-the-art subpopulation shift approaches employ any kind of curriculum. To overcome this, we design a CL approach aimed at initializing the model weights in an unbiased vantage point in the hypothesis space which sabotages easy convergence towards biased hypotheses during the final optimization based on the entirety of the available data. We hereby propose a Curriculum-enhanced Group Distributionally Robust optimization (CeGDRO) approach, which prioritizes the hardest bias-confirming samples and the easiest bias-conflicting samples, leveraging GroupDRO to balance the initial discrepancy in terms of difficulty. We benchmark our proposed method against the most popular subpopulation shift datasets, showing an increase over the state-of-the-art results across all scenarios, up to 6.2% on Waterbirds and 2.9% on CivilComments. © 2024 IEEE.

关键词： Contrastive learning

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intelligent inspection system for river basin based on yolov5 24

Intelligent inspection system for river basin based on yolov...

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4th International conference on Internet of things and Machine learning

作者： Gao, Man Li, Yingmeng Qian, Yun Zhu, Gege Hu, Xiaohong Beihua Univ Sch Elect & Informat Engn Jilin Jilin Peoples R China

ISBN: (纸本)9798400710353

With the rapid development of industrialization and urbanization, facing the inefficiency and high cost of traditional manual inspection, this design proposes a smart river inspection system based on YOLOv5, which aims to automatically monitor the river environment and identify abnormalities in real time. this design collects images of floating plastic bottles, bags, ship docking and traveling dataset, and illegal sand mining dataset from public dataset websites in the river, which are manually cleaned to a total of 5047 images. the dataset was classified into five categories of labels: normal, abnormal, package, bottle, and sand mining, and labeled into YOLO format. After training with the YOLOv5 model, the model shows a continuous decrease in the loss of bounding box, object, and classification, as well as an increase in the MAP of each category. the final recognition MAP values of the model for the categories of normal ship movement, abnormal ship movement, bags, plastic bottles, and illegal sand mining are 0.85, 0.50, 0.87, 0.81, and 0.77, respectively;however, the leakage rate of small targets is high, and the "abnormal" and "sand mining" categories are not as well recognized as the "normal" category. there is still room for improvement in the recognition accuracy of the "sand mining" category. the study suggests that it can provide decision support for the water resource management department to carry out intelligent inspection of the river.

关键词： River channel floating objects Smart patrol inspection YOLOv5 CNN

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Proceedings of 2024 4th International conference on Bioinformatics and intelligent Computing, BIC 2024

Proceedings of 2024 4th International Conference on Bioinfor...

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4th International conference on Bioinformatics and intelligent Computing, BIC 2024

ISBN: (纸本)9798400716645

the proceedings contain 80 papers. the topics discussed include: a distributed independent sample T-Test protocol for privacy protection in smart healthcare;deep learning neural network-based Weibo sentiment analysis;a method for extracting physiological parameters of anesthetic patients based on video;modelling the impact of medical student stress after a public health emergency based on logistic regression algorithm: a model of factors influencing medical student stress;temporal shift vision transformer adapter for efficient video action recognition;a machine learning approach for medical imaging evaluation;SplitMorph: a dynamically enhanced liver image registration network based on feature separation fusion;research on recommendation algorithm based on knowledge graph;and premature beats detection using a convolution neural network based on the frequency channel attention module.

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EIMPNet: End-to-End Iterative Feature Matching and Pose Estimation Joint optimization Network 4

EIMPNet: End-to-End Iterative Feature Matching and Pose Esti...

引用

4th International conference on intelligent Technology and Embedded Systems, ICITES 2024

作者： Chen, Junlong Wen, Gongjian Jian, Haojun Li, Dongdong College of Electronic Science and Technology National University of Defense Technology Changsha410000 China

ISBN: (纸本)9798331506292

Traditional pose estimation methods typically involve two separate steps: first using feature matching algorithms to identify matches and then estimating the pose. these conventional approaches often concentrate on enhancing match quality or eliminating potential outlier correspondences, overlooking the inherent connection between feature matching and pose estimation. therefore, we propose EIMPNet, an end-to-end optimization network for joint feature matching and pose estimation, generating sparse features and pose simultaneously. Specifically, we use an iterative graph neural network to enhance pre-extracted descriptors, facilitating the network in the extraction of robust matches. We embed differentiable pose estimation into the matching network, along with the match loss for training. this strategy enables the network to iteratively predict matches that are more favourable to pose estimation and naturally reduces the impact of outliers. Experimental results on HPatches and MegaDepth datasets show that our end-to-end training enhances pose estimation metrics and efficiency compared to baseline methods, eliminating the need for RANSAC iterations. © 2024 IEEE.

关键词： Graph neural networks

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An FTN Sequence Detection Algorithm Based on Soft Information Iteration and ADMM optimization 4

An FTN Sequence Detection Algorithm Based on Soft Informatio...

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4th International conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Li, Zhiwen Chen, Qiang Xie, Hui School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing Wanzhou China

ISBN: (纸本)9798350378542

To address the severe inter-symbol interference (ISI) problem in faster-than-Nyquist (FTN) communication systems, this paper proposes an Iterative Soft ADMM Detection (ISAD) sequence detection algorithm. the ISAD algorithm ingeniously integrates soft information iterative updating with ADMM optimization by alternately estimating residual ISI and updating symbol soft values while utilizing constellation prior knowledge and observation information to guide symbol estimation, ultimately achieving efficient detection in FTN systems. Compared with traditional methods, the ISAD algorithm effectively suppresses ISI while significantly reducing computational complexity, particularly in medium to high-order modulation schemes, where it exhibits outstanding performance. Simulation results demonstrate that the proposed algorithm can effectively suppress ISI in medium to high-order modulation and achieve bit error rate performance close to that of Nyquist orthogonal transmission. the proposed ADMM soft detection algorithm provides an effective solution for FTN communication systems. © 2024 IEEE.

关键词： Intersymbol interference

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Design optimization and Improvement of Water Cooling jacket Based on ANSYS Workbench 4

Design Optimization and Improvement of Water Cooling jacket ...

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4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

作者： Zhang, Xuewei Ren, Gaolin Zou, Ye Lei, Chenhui Song, Anda Lv, Pengfei China Academy of Aerospace Aerodynamics Beijing100074 China

ISBN: (纸本)9798350314434

Water cooling jacket has been widely used in petroleum, chemical, food, energy and other industries. In the exhaust gas treatment device of the chemical experimental platform, bulge phenomenon was found in the inner cylinder. this paper completed the analysis and calculation by ANSYS Workbench and completed the upgrade and optimization of the water cooling jacket structure. At present, the optimization mechanism has been upgraded and reformed, and passed the test and inspection, and provided a number of engineering experiment data, for the reference of relevant technical designers. © 2023 IEEE.

关键词： Cooling

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Accelerating Drug Discovery: Ai Powered Approaches in Pharmaceutical Research 4

Accelerating Drug Discovery: Ai Powered Approaches in Pharma...

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4th International conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Chintala, Satishkumar Fisher Investments TX United States

ISBN: (纸本)9798350307757

the use of machine learning (ML) and deep learning (DL) in the process of drug development marks the beginning of a new chapter in the creation of pharmaceuticals. Researchers now have the ability to manage massive amounts of information, discover complex biomolecular interactions, and accelerate the development of novel therapies thanks to the use of cutting-edge computational approaches. the purpose of this study is to investigate the applications of deep learning (DL) and machine learning (ML) in the field of drug development. Specifically, the research investigates the impacts of these two types of learning on target selection, chemical screening, and lead candidate optimization. there are still problems with the quality of the data, the interpretability of the data, and the processing requirements, despite the fact that these approaches offer a lot of potential. By conducting a thorough analysis of the relevant literature and case studies, this research addresses ethical concerns, provides evidence of successful implementations, and elucidates the many methodologies that are now in use. In the conclusion, the need of continuous innovation and collaboration across disciplinary lines is stressed. this is necessary in order to fully exploit the transformative potential of deep learning and machine learning in the field of drug development. © 2024 IEEE.

关键词： Deep learning

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