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共 20691 条记录，以下是4461-4470 订阅

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Ischemic Heart Disease Prediction Using Machine learning and Deep learning Techniques 3

Ischemic Heart Disease Prediction Using Machine Learning and...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Chowdary, Kadiyala. Likitha Akhil, Boppana. Lasya, Tummala Sai Kalyani, G. Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Kanuru520007 India

ISBN: (纸本)9781665494007

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

关键词： Deep neural networks

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Quality of AI Service Assurance in 6G Native Artificial Intelligence Networks 3rd

Quality of AI Service Assurance in 6G Native Artificial Inte...

引用

3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Hua, Meihui Chen, Tianjiao Liu, Guangyi Deng, Juan Li, Na Wang, Xiaoyun China Mobile Research Institute Beijing China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing China China Mobile Communications Group Corporation Beijing China

ISBN: (纸本)9789819624676

6G native artificial intelligence (AI) networks are expected to support diverse vertical industries and offer countless emerging AI services. To satisfy stringent requirements of diversified services, network slicing is developed, which enables service-oriented resource allocation tailored for different AI scenarios. However, the mismatch between the user’s quality of AI service (QoAIS) requirements and the actual capacity of network slices, as well as the hierarchical network architecture, pose great challenges to the guarantee of QoAIS. This paper introduces AI-assisted slicing management framework to maintain a high performance of network operation by supporting diverse AI services and meeting QoAIS. A novel methodology and resource allocation scheme based on federated learning and transfer learning is proposed, which enables efficient life-cycle management of AI services and selection of network slices. Simulation results illustrate that the proposed scheme has superior performance in ensuring QoAIS compared with benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and automated Deep learning 17th

Olive Phenology Forecasting Using Information Fusion-Based I...

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17th international conference on Hybrid Artificial intelligent Systems (HAIS)

作者： Chacon-Maldonado, Andres Manuel Molina-Cabanillas, Miguel Angel Troncoso, Alicia Martinez-Alvarez, Francisco Asencio-Cortes, Gualberto Pablo Olavide Univ Data Sci & Big Data Lab Seville 41013 Spain Easytosee AgTech S L Diego Martinez Barrio 10 3rd floor Seville 41013 Spain

ISBN: (纸本)9783031154713;9783031154706

A new methodology has been applied to improve the prediction accuracy on the olive phenology forecasting problem, applying deep learning with hyperparameter optimization to handle with imbalanced data. The application of hyperparameter optimization to optimize the architecture of the deep neural network along with both class balancing preprocessing and the introduction of new variables allowed to improve the phenological forecast classification problem in 16 different plots from 4 different areas in Spain is introduced in this work. The results obtained have been shown to be promising and encourage further research in this field, where the potential for improvement is very high. The improvements, in terms of prediction accuracy, achieved are around 4% on average and, in some cases, exceeding 20%.

关键词： Deep learning Classification Phenology forecasting Imbalanced learning

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A Nature-Inspired Concept Drift Adaptation Method for Industrial data Stream Regression 33rd

A Nature-Inspired Concept Drift Adaptation Method for Indust...

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33rd international conference on Flexible Automation and intelligent Manufacturing

作者： Trat, Martin Bergmann, Philipp Ott, Andreas Ovtcharova, Jivka FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9783031744815;9783031744822

The industrial domain is characterized by many regression problems. Machine learning (ML) solutions to such problems that perform well and are robust against concept drift (CD) under real-world conditions are scarce. This paper proposes a novel model-agnostic method for ensembles, inspired by principles from evolutionary biology. It leverages these to preserve fit and significant concepts from the data stream and to constantly consider newly emergent, potentially short-lived concepts to challenge aforementioned ones. Introduced as dynamic member rotation and weight update algorithmics, this aids to robustly and explicitly adapt to various CD types as well as balance statistic modeling stability and plasticity. The method outperforms popularly employed state-of-the-art approaches on two real-world case studies by a considerable margin. With its capability of respecting practically feasible resource constraints, economizing ground truth consumption and enabling the straightforward use of tried-and-tested ML models, it is particularly valuable for productive use in data streams.

关键词： Explicit concept drift adaptation Ensemble algorithms Regression

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Enhancing Sales Forecasting in E-Commerce Through Advanced Predictive Analytics and Deep Neural Networks 3

Enhancing Sales Forecasting in E-Commerce Through Advanced P...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Kotha, Mahesh Rajaram, Gnanajeyaraman Sathishkumar, K. Fallah, Mohammed Hussein Karpagam, J. Anil, P.S. CMR Technical Campus Hyderabad India Simats Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical sciences Department of Cse Chennai India Erode Arts and Science College Dept. of Computer Science TamilNadu Erode India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Gnanam School of Business Department of Management Mary's junction Sengipatti Tamilnadu Thanjavur India

ISBN: (纸本)9798331542375

Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of results as compared to traditional approaches that are based on historical data and simple econometric models. This research evaluates how deep learning approaches, LSTM and Transformer models, improve precisely the forecast by noticing the behaviour dynamics and more complex patterns in the business environment. The actual approach includes sales history data, understanding customers, trends, and related promotions to train as well as test neural networks. The basic hybrid approach integrates deep learning and statistical methods to forecast the maximum potential outcomes. The performance of models in this study is evaluated by Mean Absolute Error (MAE), Root Mean Squared Error(RMSE) and R-Squared to compare the forecasted sales values with the actual ones. It is established that incorporating the deep learning models that have been described enhances the forecast precision concerning the baseline and conventional methods of approach significantly, especially the hybrid model. The precise accuracy, and factors that cause it to fluctuate from one product category to another, and the impact of promotional activities on the forecast's precision, are addressed. Thus, the study contributes to the knowledge that adopting innovative techniques such as advanced predictive analytics and deep learning to improve sales forecasting can potentiate strategic business courses of action and the firm's performance in today's cut-throat e-commerce marketplace. © 2025 IEEE.

关键词： Marketplaces

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Machine learning Protocols for Enhanced Cloud Network Security 3

Machine Learning Protocols for Enhanced Cloud Network Securi...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Khatarkar, Poonam Singh, Devesh Pratap Sharma, Anil Ies College of Technology Department of Electrical & Electronics Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

In fortifying the security of an organization's cloud environment, it is crucial to align security strategies with specific objectives and threats unique to the cloud landscape. This paper proposes an Integrated Augmented Intelligence approach, amalgamating Artificial Intelligence (AI) technologies such as machine learning, natural language processing, and behavioral analytics. The methodology involves thorough evaluation of existing security protocols, identification of AI components based on organizational needs, and integration of cloud-native security tools. data from diverse cloud sources are processed, and features are extracted for machine learning models. The proposed system includes three key algorithms: Threat Detection using Machine learning, Real-time Incident Response, and Explainable AI for Cloud Security. The Threat Detection algorithm employs machine learning for real-time threat identification. The Real-time Incident Response algorithm automates responses based on threat severity, significantly reducing response time compared to traditional manual methods. Lastly, the Explainable AI algorithm enhances interpretability, crucial for ensuring human trust in AI-driven security decisions. A comprehensive comparative analysis is performed against six traditional methods, revealing the proposed system's superiority in threat detection accuracy, rapid incident response, scalability, interpretability, and resource efficiency. The integration of augmented intelligence offers continuous improvement, learning from emerging threats. © 2023 IEEE.

关键词： Machine learning

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Scheme and Construction of a Smart Vacuum Cleaner Robot 3

Scheme and Construction of a Smart Vacuum Cleaner Robot

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Hasan, Md. Ibrahim Zeyad, Mohammad Ahmed, S.M. Masum Tasrif Anubhove, Md. Sadik Hossain, Eftakhar Hasan, Sayeed Mahmud, Dewan Mahnaaz Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Chattogram4226 Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities Heriot-Watt University Edinburgh United Kingdom Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering Mons Belgium

ISBN: (纸本)9781665494007

A smart vacuum robot is a remarkable technological achievement that makes cleaning more efficient, faster, and easier. According to this study, the smart vacuum cleaning robot can be operated manually, automatically, mopped/swept, vacuumed/vacuumed, and observed from 360°. A design of a smart vacuum-cleaning robot is proposed with different characteristics. The Arduino IDE and Proteus Design Suite 8 Professional software were employed for analytical analysis, with a microcontroller serving as the primary control component. The hardware implementation comprises a microcontroller, ultrasonic sensor, and light-dependent resistor (LDR) where the ultrasonic sensor identifies obstacles (trash). Therefore, the LDR emits light and sends the microcontroller's signal to the microprocessor. All the sensors in the proposed system operated flawlessly during the experiment. Moreover, all the sensors were performing perfectly. With additional features of this robot, it can continue its operation during darkness. The proposed robot is equipped with a number of creative and intelligent features that allow it to provide the maximum possible support to employees, housewives, and ordinary people. This work presents a new notion of an advanced cleaning robot employing cutting-edge technology that can work independently in automated and regular situations and dramatically improve the cleaning system for humanity. © 2023 IEEE.

关键词： Microcontrollers

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Electric bicycle battery management system based on NB-IoT 3

Electric bicycle battery management system based on NB-IoT

引用

3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Lin, Qingyao Gao, Qiuhong Beijing City University School of Information Science and Engineering Beijing China

ISBN: (纸本)9781510680449

The birth of electric bicycles greatly facilitated people's lives. However, due to the increasing voltage of electric vehicle batteries, the discharge power and capacity have also increased. The Battery Management System (BMS) developed in the early stage has been unable to accurately estimate the battery capacity and provide timely battery protection. In order to improve the safety, reliability and accuracy of capacity estimation of electric vehicle batteries, this project has designed an intelligent electric vehicle battery management system that can use NB-IoT technology to communicate and measure the battery impedance in real time. There is an NTC temperature sensor array inside the battery pack that can detect the temperature of each battery. When the battery is abnormal, the abnormal data can be sent to the nearby base station through the NB-IoT module. Then, the user and administrator can be informed through the cloud platform to ensure the safety of the battery. The NB-IoT module also supports the location tracking, which can periodically send the location data to the cloud platform. Additionally, the battery pack is also equipped with an Impedance Track Algorithm coulometer that can calculate the remaining capacity of the battery and discharge time at any time. The accuracy can reach 99%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Lithium-ion batteries

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Bird's Nest Detection Algorithm for Transmission Lines Based on Deep learning 3

Bird's Nest Detection Algorithm for Transmission Lines Based...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Ge, Zhao Li, Hongwen Yang, Rui Liu, Haifeng Pei, Shaotong Jia, Zhihui Ma, Ziru State Grid Xiong'an New Area Electric Power Supply Company Baoding China North China Electric Power University Hebei Provincial Key Laboratory of Power Transmission Equipment Security Baoding China

ISBN: (纸本)9781665459112

The bird's nest on the power transmission tower may cause the occurrence of bird flash accidents and threaten the safe and reliable operation of the power grid. The realization of the independent identification and positioning of the bird's nest has always been a research hotspot. With the gradual deepening of the application of UAV inspection, higher requirements are put forward for the accuracy and speed of the bird's nest recognition algorithm. This paper proposes a bird's nest defect recognition method based on YOLOv5, which is composed of backbone network, FPN and YOLO head. After multiple rounds of training on the construction of the bird's nest defect database and model of the transmission line, the independent identification and positioning of the bird's nest has been realized. The results show that the recognition rate of the YOLOv5 model for the bird's nest can reach 83.4%, and the FPS can reach 85.32. The recognition algorithm based on YOLOv5 proposed in this paper can meet the real-time and accuracy requirements of UAV inspection for target detection. © 2022 IEEE.

关键词： Electric lines

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Design and Implementation of Industrial Real-Time Ethernet Redundant Network Based on Parallel Redundancy Protocol 3

Design and Implementation of Industrial Real-Time Ethernet R...

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3rd international conference on Computer Science, Electronic Information engineering and intelligent Control Technology, CEI 2023

作者： Gang, Mingxu Yan, Bingjun Hu, Bo Chinese Academy of Sciences Key Laboratory of Networked Control Systems China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350372052

Industrial real-time Ethernet technology is widely used in modern industrial control systems due to its high communication rate and low application cost. However, in complex network environments, industrial real-time Ethernet may encounter data collisions, data loss, and other issues, which reduce the overall reliability of industrial control systems. To address these issues, a method for designing and implementing industrial real-time Ethernet redundancy networks based on parallel redundancy protocols is proposed. The method first analyzes the redundant data frame structure, dual-connected node (DANP) structure, and redundancy implementation principle of parallel redundancy protocols (PRP). Then, aiming at the standard redundancy discard algorithm principle and its shortcomings, an improved sliding window redundancy discard algorithm is proposed to adapt to complex network environments. Based on the discard algorithm, dual-connected redundancy nodes are designed and implemented. Finally, the dual-connected redundancy nodes are used as the core to construct industrial real-time Ethernet redundancy networks. The test results show that in a PRP-based industrial real-time Ethernet redundant network, when a single redundant network fails, the system can switch without delay, greatly improving the reliability of the industrial control system communication network © 2023 IEEE.

关键词： Redundancy

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