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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24666 条记录，以下是271-280 订阅

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Towards Accurate Diabetes Prediction: A Synergistic Approach Using Adaptive Deep Learning Techniques

Towards Accurate Diabetes Prediction: A Synergistic Approach...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： B.Shuriya V. Balajishanmugam P.Sivaprakash Umamaheswari Department of CSE PPG Institute of Technology Coimbatore India Department of Computer Science and Business Systems Dr. NGP Institute of Technology Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Diabetes is a burgeoning international health issue, requiring effective and precise predictive models. Here, we compare Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for predicting diabetes using the Pima Indians Diabetes Dataset (containing 768 samples with 8 clinical features). For improving model performance, we utilized data normalization, removal of outliers, and feature scaling in preprocessing. A 5-fold cross-validation approach was used to maintain robustness. The best CNN model had an accuracy of 85.20%, which was better than the RNN model (83.50%) and current methods like CNN-RNN hybrids, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. Hyper parameters like learning rate, batch size, and neurons were optimized to prevent over fitting and gain maximum performance. Comparative analysis depicts that deep learning models, i.e., CNNs, can effectively distinguish diabetic and non-diabetic cases. In future work, emphasis will also be given to blending other datasets and optimization techniques for greater generalizability.

关键词： Deep learning Adaptation models Accuracy Predictive models Data models Diabetes Telecommunication computing Convolutional neural networks Optimization Long short term memory

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Predicting Solar Power Based on Machine Learning Approaches and Their Efficacy

Predicting Solar Power Based on Machine Learning Approaches ...

引用

automation and Computation (AUTOCOM), international conference on

作者： Muthuvel Raj Suyambu Pawan Kumar Vishwakarma Sandeep Gupta BESS Projects Energy Vault California USA Independent Researcher SATI Vidisha M.P. India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The planning, administration, and operation of power systems depend on enhancing the forecasting accuracy of solar energy as it grows in popularity within the worldwide electric grid. However, the erratic and disorganised nature of solar energy data makes this a difficult process. To increase a forecasting accuracy of renewable energy, a number of approaches have been developed to date, including statistical methodologies, physical models, and AI techniques. Using weather data collected in Errachidia, Morocco, this research explores the use of several ML techniques for predicting solar power production. Different models, namely ANN, Ridge Regression, SVM, and KNN were employed to forecast solar energy output. Performance evaluation index; therefore, the R 2 _ score and the root mean standard error were used to assess the prowess of the different models. The findings showed that when it came to solar energy predictions, the ANN model was the most accurate, with a R 2 of 99.93 and an RMSE of 0.08. In order to enhance energy management tactics, the results show that ML methods, especially ANN, may increase an accuracy of solar power projections.

关键词： Support vector machines Renewable energy sources Accuracy Statistical analysis Weather forecasting Solar energy Machine learning Predictive models Forecasting Standards

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Leveraging Artificial Intelligence for Colon Cancer: Trends, Challenges and Innovations

Leveraging Artificial Intelligence for Colon Cancer: Trends,...

引用

automation and Computation (AUTOCOM), international conference on

作者： Sakshi Takkar Manik Rakhra Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research.

关键词： Deep learning Ethics Technological innovation Computational modeling Buildings Transfer learning Surgery Colorectal cancer Data models Artificial intelligence

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LLM-Based Video Analytics Test Scenario Generation in Smart Cities

LLM-Based Video Analytics Test Scenario Generation in Smart ...

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international Scientific-Professional conference on Information Technology (IT)

作者： Merve Yilmazer Mehmet Karakose Computer Engineering Munzur University Tunceli Turkiye Computer Engineering Firat University Elazig Turkiye

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field.

关键词： Software testing Measurement automation Smart cities Large language models Visual analytics Software Prompt engineering Scenario generation Software development management

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Research on Mechanical automation Control based on Bidirectional Long Short-Term Memory

Research on Mechanical Automation Control based on Bidirecti...

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Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Xiaolu Song College of Mechanical and Electrical Engineering Zaozhuang Vocational College of Science & Technology China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability.

关键词： Adaptation models automation Accuracy Uncertainty Decision making Bidirectional long short term memory Process control Real-time systems Time factors Intelligent control

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Deep Learning models for Predictive Stability in Renewable Energy

Deep Learning Models for Predictive Stability in Renewable E...

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automation and Computation (AUTOCOM), international conference on

作者： Sujatha Banka Chava Sunil Kumar Theegala Anupriya Jakkula Vineela Arakala Harshitha Bhimpavu Poojitha Department of Electrical and Electronics Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The usage of renewable energy sources is changing the distribution of energy. This makes customers both energy users and producers, which means that energy flows both ways in smart grids. In this complicated system, keeping the balance between supply and demand in check is necessary for keeping the grid safe. Researchers are currently focusing on decentralized smart grid control (DSGC) systems that measure grid frequency, which is a key indicator of power balance, to get a real-time idea of how stable the system is. Using math equations, the DSGC model predicts grid stability in a simple 4node star structure. That being said, this model is too simple and needs more complex approaches. To make a better analysis, this study uses deep learning methods, such as Keras' sequential model. For prediction purposes, the model includes 60,000 examples and 12 predictive features. These include reaction time, nominal power, and price elasticity. Cross-validation can be used to create an artificial neural network (ANN) with three hidden layers after scaling. When it came to predicting grid safety, this model had an accuracy of 97.73% compared to simpler models.

关键词： Deep learning Renewable energy sources Adaptation models Accuracy Power system stability Predictive models Mathematical models Stability analysis Real-time systems Smart grids

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A Current Zero-Crossing Distortion Compensation Method Based on BP Neural Network In Fast-Scanning Power Supply

A Current Zero-Crossing Distortion Compensation Method Based...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Shuxian Zhang Jiang Zhao Zhongzu Zhou Yuan He Huan Jia Yuanshuai Qin Shuai Zhang Ye Liu Anhui Feng Institute of Modern Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Lanzhou China Electrical and Electromagnetic Compatibility Group Institute of Modern Physics Chinese Academy of Sciences Lanzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

fast scanning power supply is utilized in many accelerator fields. Current waveform distortion caused by various reasons often occurs at the zero-crossing point, which reduce accuracy of output current. In order to optimize the distortion of current waveform, a waveform compensation method based on BP neural network of Bayesian regularization is introduced. Firstly, the reasons for the current zero-crossing distortion of fast scanning power supply are analyzed theoretically. Then a principle of optimization method is proposed and implemented. Finally, verification of compensation method is carried out on a sine-wave scanning power supply. The result shows that the compensation method can effectively suppress the current distortion. And it is also conducive to achieving the automation of power supplies.

关键词： Accuracy automation Power supplies Neural networks Optimization methods Manuals Predictive models Distortion Testing Intelligent control

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Deep Learning-Based Implementation of Telerobotic Control with Gesture Recognition from EMG Signals

Deep Learning-Based Implementation of Telerobotic Control wi...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Péter Zsoldos Balázs Nagy Feki Pangestu Wijaya Department of Artificial Intelligence Eö tvö s Loránd University Budapest Hungary

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This research explores the field of telerobotics, focusing on the deep learning based remote control of a robot using Electromyography (EMG) and an Inertial Measurement Unit (IMU). The aim is to enable control of a robot arm by capturing muscle responses via electrical activity using the Myo armband. The captured signals representing hand gestures are classified using a four layer wide fully connected neural network, allowing for seamless translation of gestures into robot commands. Training the model with online and self-collected datasets demonstrates the effectiveness of the proposed method. The model has an accuracy of 82.92% on previously unseen data. Successful implementation of telerobotics relies on the ability of the Myo armband to capture EMG signals and IMU data, facilitating control over the end effector of the robot and movement through gesture recognition and orientation conversion.

关键词： Training Accuracy Translation Neural networks Gesture recognition Robot sensing systems Electromyography Robustness Data models Telerobotics

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Multi-Task Model Fusion via Adaptive Merging

Multi-Task Model Fusion via Adaptive Merging

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Luming Chen Ziwei Xiang Kai Lei Xu-Yao Zhang School of Artifcial Intelligence University of Chinese Academy of Sciences MAIS Institute of Automation of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-task model fusion (MTMF) aims to integrate the capabilities of individual models into a unified model. Past approaches either require extensive training and fine-tuning or necessitate that models share the same pre-training and initialization. Recently, several fusion methods have been proposed that do not require extensive training or fine-tuning. These methods can merge multiple independently trained models with different task capabilities into a single multi-task model without increasing the number of parameters. In this work, we identify a common flaw in these fusion methods: they tend to focus on how well the modules of the individual models match before merging while neglecting the representation bias of the merged model. To address this problem, we propose a simple yet effective mitigation method called adaptive merging by representation alignment (AdMbRA). Specifically, we improve the method of weight matching by using representation bias as a constraint and optimize the merging process. Experiments demonstrate that our method can effectively mitigate the representation bias of the merged model, thus improving the performance of each task.

关键词： Training Degradation Fault diagnosis Adaptation models Analytical models Prevention and mitigation Merging Signal processing Multitasking Speech processing

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Feature-Based Transfer Learning for High-Value Component Recovery in Electric Vehicles: An InceptionV3 Model Evaluation 2nd

Feature-Based Transfer Learning for High-Value Component Rec...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Luo, Yang Xu, Yifeng Zhang, Fan Lo, Ying Tuan Lyu, Geng Tan, Andrew Majeed, Anwar P. P. Abdul Zhu, Yuyi School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China School of Computing and Creative Technologies University of the West England BristolBS16 1QY United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819639489

The worldwide transition to electric vehicles (EVs) has resulted in a substantial rise in the quantity of end-of-life (EoL) EVs that need effective recycling methods. This study examined a method of transfer learning that uses features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed using pre-trained InceptionV3 convolutional neural network (CNN) models to identify distinctive features. The performance of four classifiers, namely the Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Naive Bayes (NB), was tested using the collected features. The dataset was partitioned into training, validation, and test sets using a 70:15:15 stratified split to guarantee an equitable distribution of all classes. The InceptionV3-SVM pipeline achieved the highest performance, with training, validation, and test accuracies of 100%, 97%, and 97%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization capabilities of the InceptionV3-SVM pipeline indicate its potential for practical deployment in sustainable manufacturing processes. This study provides a foundation for further research in the automated sorting and recovery of high-value electronic components from EVs, potentially extending to a broader range of electronic components and applications. The findings highlight the effectiveness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automotive industry

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