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Predicting Coronary Heart Disease Through machine Learning Algorithms 3rd

Predicting Coronary Heart Disease Through Machine Learning A...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafillou, Vassilios Electrical and Computer Engineering Department University of Peloponnese Patras Greece Health Physics and Computational Intelligence Lab Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation Lab Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9783031655210

machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the functioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tailored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early prediction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical database with 78001 records. Our ultimate goal is to develop an expert Artificial intelligence system and an Artificial Neural Network that can predict the development of coronary heart disease by employing intelligent knowledge-mining algorithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random Forest, Decision Trees and Neural Networks. In our experimental analysis, the Decision Tree and Neural Network achieved an accuracy of 90.08% and 84.56% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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Transcription of Mandarin singing based on human-computer interaction 3

Transcription of Mandarin singing based on human-computer in...

引用

3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Ren, Wanglong Huang, Zebin Zeng, Xiangjian Liu, Zhen School of Informatics Xiamen University Xiamen China School of Journalism and Communication Xiamen University Xiamen China Institute of Artificial Intelligence Xiamen University Xiamen China

ISBN: (纸本)9781510661301

Lyric transcription is similar to speech recognition, both identify content from sound clips. Speech recognition technology is maturing and related application systems have been widely used in the software industry, but the research on singing content is far from getting enough attention, there is still little research on identifying words and sentences from singing voice. What's more serious is that compared with the lyrics transcription in the English field, there are almost no related academic papers in the Mandarin field. On the one hand, speech recognition has high-quality datasets in multiple languages that are large enough to train large-scale models. However, the field of singing lacks data resources. On the other hand, compared with speech recognition, singing recognition has obvious skills in pronunciation, which is embodied in musical characteristics such as pitch and rhythm. Based on these problems, this paper aims to provide a dataset that can be used for Mandarin lyrics transcription, and build a transcription model on this dataset. Our model can address some deficiencies of the existing models, and achieves promising results on our dataset. © 2023 SPIE.

关键词： Music

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Knowledge distillation-based abnormal power consumption pattern detection for edge environment 3

Knowledge distillation-based abnormal power consumption patt...

引用

2024 3rd international conference on the Energy Internet and Energy Interactive Technology, EIEIT 2024

作者： Fan, Yuanliang Wu, Han Li, Zewen Lin, Jianli Li, Lingfei Huang, Xinghua Chen, Weiming Chen, Beibei Electric Power Research Institute of Fujian Electric Power Co. Ltd Fuzhou China

Abnormal power consumption pattern detection can effectively reduce the nontechnical loss of electric energy, which is of great significance in maintaining the safe operation of the power grid and protecting the rights and interests of normal users. However, for edge environments with limited communication conditions, uploading electricity consumption data to the cloud for centralized computation is prone to problems such as network congestion and transmission delays. For this reason, this paper proposes a knowledge distillation (KD)-based anomalous power consumption pattern detection model that can be deployed at edge computing terminals. First, the ResNet50 network is selected as the teacher model, and the self-attention mechanism (SAM) is introduced to improve the feature extraction capability of long-term dependency. Then, ResNet18 is used as the student model, and the difference between the output of the student model and the output of the teacher model is minimized by optimizing the distillation loss function. Finally, the effectiveness of the proposed method is verified in practice. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Health Information Broadcast Distributed pattern Association based on Estimated Volume 3

Health Information Broadcast Distributed Pattern Association...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Dhanalakshmi, R. Kalpana, A.V. Umamageswaran, J. Kumar, B. Praveen Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India K L University Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665462167

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery. © 2023 IEEE.

关键词： Data mining

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Studies on Artificial intelligence Technology's Use in Computer Network Upkeep and Operation 3

Studies on Artificial Intelligence Technology's Use in Compu...

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3rd international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Zhu, Hao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350343922

The study of Artificial intelligence Techniques in Computer Network Operations and Maintenance aims to explore how advanced AI methods and techniques can be utilised to improve network performance, efficiency of management and maintenance, and reliability. This area of research is of increasing interest as the complexity and size of networks continues to grow, requiring smarter, automated solutions to meet changing demands. Artificial intelligence techniques can be used to monitor network performance metrics in real time and automatically detect and alert on abnormal events. machine learning and time series analysis methods are widely used to detect network performance anomalies, thus helping operations staff identify and resolve problems more quickly. Artificial intelligence techniques can be used to automate network configuration, troubleshooting and resource optimisation. Automated network management systems can dynamically adjust network parameters according to real-time demand, improving network adaptability and flexibility. Using machine learning and data mining technologies, network equipment failures or performance degradation can be predicted so that maintenance measures can be taken in advance to reduce unnecessary downtime. Artificial intelligence technology also plays an important role in the field of network security. It can be used to detect network intrusion, threat analysis and malware detection to improve network security and protect user data. In conclusion, the use of AI technology in computer network operations and maintenance provides more intelligent and automated tools for network management and maintenance, helping to improve network performance, reduce failures and improve security. Ongoing developments in this area will continue to drive innovation in network operations and maintenance, providing more reliable network services to a variety of industries and organisations. © 2023 IEEE.

关键词： Network intrusion

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Application of 3d models in intelligent visual inspection of product anomalies 3

Application of 3d models in intelligent visual inspection of...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Kong, Feifei Lu, Yunxia Du, Fuzhou School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Satellite Manufacturing Factory Co. Ltd. Beijing China

ISBN: (纸本)9781510681804

Due to its advantages of non-contact, high efficiency, and digitization, machine vision has been extensively utilized in product appearance anomaly inspection. The advancement of deep learning has significantly propelled the digitization and intelligence process of visual inspection. Nonetheless, the current learning paradigm still heavily relies on a large quantity of high-quality training samples, and exhibits limited capability in detecting anomaly categories beyond the training set (also known as open-set detection). Aiming at these challenges and inspired by the model-driven digital inspection idea, this article advocates for the thorough exploration of the auxiliary potential of 3D models in visual inspection. Diverging from the traditional paradigm, which mainly leverages digital models as data management centers, this article explicitly highlights two additional critical functions of 3D models: knowledge carriers and detection references. The efficacy of the proposed approach is demonstrated through case studies. © 2024 SPIE.

关键词： Smart manufacturing

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Privacy-Preserving Face recognition with Adaptive Generative Perturbations 27th

Privacy-Preserving Face Recognition with Adaptive Generative...

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27th international conference on pattern recognition, ICPR 2024

作者： Zhang, Delong Peng, Yixing Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031783401

For online face recognition services, the potential leakage of facial features and reconstruction techniques gives malicious attackers the opportunity to reconstruct face images, raising public concern about privacy. Previous privacy-preserving face recognition methods either require retraining the face recognition model or iterative perturbation that increases inference time. To overcome these limitations, we propose an efficient plug-and-play method that crafts Adaptive Generative perturbations for frozen Face recognition model (AGFace) to defend the reconstruction attacks. To generate perturbation with a single forward for shallow features extracted by frozen recognition model, we learn a Perturbation Generator to efficiently mine the adversarial perturbations by simultaneously minimizing the perturbation and maximizing the reconstruction error of a proxy reconstructor. To achieve privacy-utility trade-off, we propose the Adaptive Channel Selector to identify top-k reconstruction-sensitive channels for the features. By selecting these channels for perturbation, the dominant visual privacy information is protected with reconstruction-insensitive discriminative information preserved. Extensive experiments demonstrate that AGFace achieves state-of-the-art performance in terms of both privacy and utility among retraining-free methods and is comparable to retraining-dependent methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Design of a Visible and LWIR Dual Band Imaging Detector 3

Design of a Visible and LWIR Dual Band Imaging Detector

引用

3rd international Computing Imaging conference, CITA 2023

作者： Wang, Weiping Zhao, Shuxia Cao, Jun Cao, Jing Hu, Xiaoyan Artificial Intelligence Institute of China Electronics Technology Group Corporation Beijing100086 China

ISBN: (纸本)9781510671409

In order to obtain a high-resolution image and realize the all-day imaging simultaneously, a visible and long-wave infrared dual band imaging detector is proposed and designed, by a meta surface structure through which the infrared light has been absorbed and the visible light passed. With a monolithic chip, the visible light of 400nm-900nm and the long-wave infrared light of 8-14μm have been detected, which have the apparent size and weight advantages. This validates the improved integration of structure and results in considerable reduction of the complexity of the imaging system, which is advantageous to target recognition in complex environment. © 2023 SPIE.

关键词： Surface structure

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3rd international conference on Advanced Intelligent Technologies, ICAIT 2022

3rd International Conference on Advanced Intelligent Technol...

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3rd international conference on Advanced Intelligent Technologies, ICAIT 2022

ISBN: (纸本)9789819952021

The proceedings contain 38 papers. The special focus in this conference is on Advanced Intelligent Technologies. The topics include: Research on CBRN Practical Assessment Technology Based on Artificial intelligence Technology;research on the Application of Voice Acoustics for Speech intelligence in Folk Songs;acoustic Parameters Analysis of Special Singing Method of Folk Songs for Singing Speech Synthesis;Simulation of Crop Planting Decision System Based on "U + CSA" Public Welfare Agriculture and machine Learning Algorithm;Evaluation Method of Armored Soldier Simulated Training Based on BP Neural Network;research on the Shape of Hair Dryer Handle Using Fuzzy Theory;a Scheme for Determining Maintenance Task Priority;an Improved Channel Assignment and Topology Control Algorithm in Cognitive Radio Networks;LFPS-HSDN: Link Failure Protection Scheme in Hybrid SDNs;passive Detection Beamforming Based on Arc Array;fractional Gradient Descent Algorithm for Nonlinear Additive Systems Using Weierstrass Approximation Method;design and Implementation of a Low-Code and No-Code Platform Applied in Meteorological Monitoring Service;Model-Data Driven Fusion Method Considering Charging Rate and Temperature to Predict RUL of Lithium-Ion Battery;Design of Indoor Air Purification Control System Based on STM32;decentralized Oracle with Mechanism for Reputation and Incentive;super-Resolution Reconstruction Based on Kernel Regression Method;on the Impact of Artificial intelligence Application on Public Administration;research on Civil Aircraft Longitudinal Stability and Maneuverability Flight Test Technology Based on Virtual Flight;application of Computer Simulation Technology in Hydraulic Resistance Test of Fire Hose;Research on Construction of Higher Vocational Teaching Quality Evaluation System Based on BP Neural Network.

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Application of Artificial intelligence in Libraries 3

Application of Artificial Intelligence in Libraries

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3rd international conference on Artificial intelligence and Advanced Manufacture, AIAM 2021

作者： Li, Jingjing Wang, Hao Heilongjiang Bayi Agricultural University Library Daqing China Heilongjiang Bayi Agricultural University College of Engineering Daqing China

ISBN: (纸本)9781665417327

Artificial intelligence (AI) has shown great promise for applications with large breakthroughs in pattern recognition, knowledge mapping, computer vision, and human-machine interaction. In this paper, firstly, the key technical features of AI in library application are studied, and its application in library stack building, stack management and information retrieval service is analyzed;then the existing problems of artificial intelligence in library application are analyzed;and finally the corresponding application service countermeasures are put forward, which can provide reference for related researchers and staff. © 2021 IEEE.

关键词： Libraries

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