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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是491-500 订阅

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Deep learning for Enterprise Sales Prediction: Haressing CNN-LSTM-Attention Model 3

Deep Learning for Enterprise Sales Prediction: Haressing CNN...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Liu, Yang Claro M. Recto Academy of Advanced Studies Lyceum of the Philippines University Manila Manila Philippines

ISBN: (纸本)9798350380989

This study introduces a novel CNN-LSTM-Attention model to address limitations in traditional LSTM architectures, particularly in scenarios impacted by small weights. Leveraging Convolutional Neural Networks and a multi-head attention mechanism with Dropout, the model optimizes the LSTM component for improved predictive capabilities. Experimental results on enterprise revenue prediction demonstrate a substantial decrease in RMSE and MAPE, indicating the model's superior accuracy compared to traditional LSTM and LSTM-Attention models. The proposed approach presents a promising avenue for advancing time series forecasting in scenarios where nuanced dependencies affect predictive outcomes. © 2024 IEEE.

关键词： Attention block Convolutional Neural Networks Deep learning LSTM Time series forecasting

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Enhanced Eye-State Classification Using InceptionV3 Neural Network 3

Enhanced Eye-State Classification Using InceptionV3 Neural N...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Madaan, Vijay Sharma, Neha Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350354171

The complicated CNN (convolutional neural networks) of the InceptionV3 model is proposed in this article as a novel approach for the classification of eye conditions. The work uses transfer learning to provide quick and efficient categorization using the already acquired parameters of the InceptionV3 as a model, therefore needing less training. The method used on the verified dataset showed 97.51% dependability throughout the assessment. Its reliable and longlasting ability to distinguish between different eye states indicates that there is potential for the suggested technology for useful applications, such as human-machine communication and tiredness in vehicle management. An often-used option for deep learning applications, where the model was enhanced using the Adam optimizer, leading to faster closure and better generalization capabilities. The paper also addresses important parameters and data-enriching techniques that improve the model's ability to correctly generalize to unidentified data and its resistance to overfitting. Both the precision and the absence of functions were employed as criteria for assessment to examine the predictive ability of the system;both were decreased to ensure its predictive capability. The thorough review and verification of the presented framework reveal that it is an accurate instrument for eye-state categorization tasks and offers exciting opportunities for future study and applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Employee Innovative Behavior Feedback System Based on Web-based ID3 Algorithm 3

Research on Employee Innovative Behavior Feedback System Bas...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Luo, Hongchi School of Management Shanghai University Shanghai200444 China

ISBN: (纸本)9798331519032

In the Internet era, the rapid development of technology has had a profound impact on the development of enterprises. With the rapid changes in the global economy, enterprises are faced with the serious challenges brought about by the uncertainty of market structure and demand. In this case, the innovative behaviour of employees has become a key factor to promote the development of enterprises, which is an important part of their innovative capacity. The study used the questionnaire star platform and offline distribution of questionnaires for enterprise employees with different gender, age, nature of the enterprise, job level and other attributes, and successfully distributed and returned 206 valid questionnaires, using SPSS 27, AMOS 25 and other analytical software to analyse the data for reliability analysis, validity analysis and correlation test, and using machine learning decision tree algorithm -ID3 algorithm to construct the decision tree model. This study can effectively help enterprises to recruit employees, through the comprehensive analysis of the attributes and innovative behaviour of employees, to achieve the accurate identification of employee potential, and then screen out high-quality talents with innovative ability for enterprises. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning-Driven Analysis and Prediction of Solar Power Generation for Enhanced Energy Management 3

Machine Learning-Driven Analysis and Prediction of Solar Pow...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kolla, Varsha Mandula, Harshitha Aravindhaa, V. Konguvel, E. School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331509675

This research explores and investigates the use of machine learning (ML) to study, analyse, predict and visualize solar power generation. Using real time data from solar power plants, which includes important key metrics like AC Power, DC Power, Daily Yield, Total Yield, combined with weather data and their corresponding time stamps. Irradiance values are also included to deduce the proportionality factor between sunlight and energy production. By employing advanced ML techniques, this research aims to increase the predictive accuracy in solar power generation, optimize energy management, identify trends and patters in solar power generation for effective decision making. The outcome of this research significantly contributes to the operational efficiency and reliability of Solar Power generation, thereby promoting a cleaner and more sustainable future. © 2025 IEEE.

关键词： Solar irradiance

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English Translation System Assisted by machine learning and Cloud Computing data Aggregation Algorithm 3

English Translation System Assisted by Machine Learning and ...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Yang, Xuming Ma, Ying Pei, Pei School Of Foreign Languages Changchun University Of Finance And Economics Jilin Changchun China

ISBN: (纸本)9798350318609

With the rapid development of machine learning, cloud computing and big data technology, the translation accuracy and fluency of computer-aided English translation system can be greatly improved again. Based on the above reasons, this paper discusses the research of an English translation system assisted by machine learning and cloud computing data aggregation algorithm. By placing data storage and computing resources in the cloud, the English translation system can process massive language data more efficiently and realize real-time update and optimization. At the same time, the cloud computing data aggregation algorithm can also help the English translation system to better cope with the diversity and complexity of data, so as to improve the robustness and reliability of the system. Finally, this paper verifies the efficiency of the technology through experimental analysis (The accuracy rate of the computer-aided translation model based on cloud computing and machine learning is between 0.93 and 1, which is significantly higher than that of the computer-aided translation system based on statistics, memory bank and case.). © 2024 IEEE.

关键词： Cloud computing

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Design of the bandgap reference circuit based on deep learning 3

Design of the bandgap reference circuit based on deep learni...

引用

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

作者： Hu, Li Wang, Jue Sun, Xianfeng Yan, Ru Li, Yongyou School of Information and Electrical Engineering Hangzhou City University Hangzhou China

ISBN: (纸本)9781510680449

In this work, a method combining deep learning and genetic algorithm was applied to assist the design of bandgap reference circuit. Using the neural network to fit the mapping relationship between resistance values and temperature coefficient of bandgap reference circuit has significant improvement over the traditional machine learning algorithms. By using neural network as the fitness function for the genetic algorithm, the optimal design parameters of the circuit have been determined and verified. The temperature coefficient of the bandgap voltage reference is 9.7ppm after optimization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Genetic algorithms

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Predicting Anemia with Precision: Employing machine learning Ensemble Modeling and Hyperparameter Tuning 3

Predicting Anemia with Precision: Employing Machine Learning...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Rana, Anmol Sharma, Nivedita Chandigarh University Department of Mathematics Punjab Mohali India Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350354171

Anemia is one of the diseases that can be easily acquired, but should not be underestimated, as it can be cured only after correct diagnosis and correct treatment. For this, in the present work, we propose a solid and robust approach based on ensemble modeling to machine learning that allows the accurate prediction of anemia. Merging Random Forest, Decision Tree, and logistic regression we enhance the model and its generality by providing more reliability and strengthening the methods used in this model by ensembling them. As for the choice of optimal model parameters, hyperparameter tuning is the key step that has to be conducted to maximize the effectiveness of each type of method. That is why our method achieves an accuracy of 96%, and the fine-tuning of all hyperparameters and ensemble structures shows the efficiency of the approach. The study proves that, in the context of medicine, machine learning may be quite effective, for example in early detection of anemia which can lead to better outcomes for the patient and more targeted therapies. This work presents a general approach to diagnosing anemia in several population groups, providing to the increasing literature area of prediction in health sciences. © 2024 IEEE.

关键词： Accurate Diagnose Anemia Prediction Decision Tree Ensemble Structure Hyperparameter Tuning

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Driving into the Future: Exploring machine learning Approaches for Optimal Energy Management in Electric Vehicles: A Review of Challenges and Future Recommendations 3

Driving into the Future: Exploring Machine Learning Approach...

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Raj, Aby N Sakthivel, K. Bharath Institute of Higher Education and Research Department of EEE Chennai India

ISBN: (纸本)9798350390728

Electric vehicles (EVs) represent an essential and pivotal stride in the pursuit of sustainable transportation. Nonetheless, effective and efficient operation of EVs is critical for achieving optimal energy management. This research paper presents an extensive examination of machine learning (ML) methodologies utilized in EVs to achieve optimal energy management. It also tackles the obstacles encountered in this field and delineates potential future suggestions. In this study, we investigate a range of ML methodologies, such as reinforcement learning, deep learning, supervised and unsupervised learning, and their potential uses in improving vehicle performance, energy efficiency, and battery management. Critical obstacles such as data scarcity, computational complexity, and real-time implementation are identified in the review. Additionally, the integration of machine learning with autonomous driving and vehicle-to-grid technologies is underscored as an emerging trend. In spite of the fact that ML provides encouraging solutions for energy management in EVs, additional research is required to overcome the identified obstacles and maximize its potential, according to the study's findings. © 2024 IEEE.

关键词： Self-supervised learning

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Real-Time Object Detection using Mobile Robot Captured Images: A Deep learning Approach 3

Real-Time Object Detection using Mobile Robot Captured Image...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Hema Srivarshini, Ch. Preeti, N. Marri, Sreekar Praneeth Palaniswamy, Suja Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

Robots are becoming more and more prevalent in a variety of industries, such as transportation, agriculture, healthcare, and so forth. Deep learning is an area of study that is rapidly expanding and is applied in many fields including robots. For a robot to understand its environment and carry out tasks, object recognition is essential. This paper proposes a deep learning method that leverages both pre-existing data and real-time data for robotic object recognition which will enable robots to operate in challenging conditions. Using real-time data, CNN with decision tree is tested after being trained on both pre-existing and real-time data which yields 96% accuracy. A model has been created for reliable and accurate object recognition for robots. © 2024 IEEE.

关键词： Object detection

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Automated Detection of Lyme Disease using Transfer learning Techniques 3

Automated Detection of Lyme Disease using Transfer Learning ...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Dipakkumar, Shah Vaidehi Shah, Vandita Dipak Shah, Dhir Deshmukh, Simran Swaminarayan University Kalol Gujarat India Shreyarth University Dept. of Comp. Appl. Gujarat Ahmedabad India Parul University Gujarat Vadodara India

ISBN: (纸本)9798350375190

This research study explores the use of transfer learning techniques for automated detection of Lyme illness. Lyme disease, a vector-borne infection transmitted by tick bites, may be challenging to diagnose because of its diverse clinical signs and potential similarity to other diseases. Transfer learning, a subtype of machine learning, involves using pre-trained models on large datasets to enhance a model's performance in a specific domain with less labeled data. This study utilizes transfer learning on medical imaging datasets to develop a dependable and precise automated method for diagnosing Lyme disease. The proposed approach demonstrates how advanced technology might improve the precision and reliability of diagnosing Lyme disease, perhaps resulting in quicker treatment and improved patient results. © 2024 IEEE.

关键词： Diagnosis

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