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LEUSIM: A Lightweight Extendable User SIMulator for Testing Commercial Task-Oriented Dialogue System

LEUSIM: A Lightweight Extendable User SIMulator for Testing ...

引用

electrical Automation and Artificial Intelligence (ICEAAI), international conference on

作者： Xiaoqian Li Wenbo Guan Lingyu Liang Huanming Zhang School of Computing Science and Engineering South China University of Technology Guangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China Artificial Intelligence Technology Co. Ltd China Southern Power Grid Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

We propose LEUSIM (Lightweight Extendable User SIMulator), an automatic testing and assessing framework for commercial Chinese task-oriented dialogue systems. LEUSIM contains four modules, namely, natural language parser, business flow controller, user utterance imitator and system performance recorder, which can be directly obtained from the existing telephone intelligent customer service (TICS) system without or with a little adaptation. Having dependency parsing theory as its theoretical basis, LEUSIM can fully imitate the users' behavior and make it an applicable testing framework in real world scenario.

关键词： System testing Costs System performance Customer services automatic testing Natural languages Telephone sets Artificial intelligence Business

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Saravanan, S.K. Parvathy, Palaniraj Rajidurai Meenakshi, R. Prabu, K. Ganeshbabu, T.R. Meenakshi, B. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Big Data and Network Security Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Redhills Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions. © 2025 IEEE.

关键词： mHealth

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Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture

Flood Prediction and Adaptive Farming Solutions Using IoT, M...

引用

2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Karthiyayini, J. Jain, Alok Prasad, Kashi Sai Abedi, Tamam Ali Abd Ulabbas Prasanna, Yedida Murugesan, Karthikeyan New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Information Technology Bachupally Telangana Hyderabad India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

Flood is one of the most disastrous natural disasters to the people of agriculture, people and infrastructure. Mitigation of these risks and promotion of climate resilient agriculture depends on accurate flood predictions and progressive farming strategies. This paper presents an integrated model consisting of Internet of Things (IoT) sensors, Machine Learning (ML) and Remote Sensing technologies to provide precise, real time flood prediction and adaptive farming options. Using advanced deep learning algorithms and real time sensor data to predict and risk assess floods in real time, the model leverages real time satellite based Synthetic Aperture Radar (SAR). Another function of the model is to offer adaptive farming solutions including optimized irrigation, flood resilient crop selection, and disaster recovery recommendations for regions impacted by the disaster. A hybrid AI technique assimilating the social media data, and blockchain for integrity of data is more effective to make decisions for the local authorities and farmers. This study seeks to fill the gaps in interdisciplinary collaboration, data scarcity and governance and provide a solution for flood management and sustainable agriculture as a whole. © 2025 IEEE.

关键词： Deep learning

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Trajectory Optimization for a Four In-Wheel Motor Vehicle in Double Lane Change Maneuver 16th

Trajectory Optimization for a Four In-Wheel Motor Vehicle ...

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Nguyen, Huu Thien Fernandes, Manuel C. R. M. Prodan, Ionela Fontes, Fernando A. C. C. SYSTEC-ARISE University of Porto Porto Portugal LCIS Valence26000 France

ISBN: (纸本)9783031817236

This paper presents an optimization-based trajectory generation for a four in-wheel motor vehicle in a double lane change maneuver. It features the usage of a three-dimensional dynamical model of the vehicle and the generation of a feasible optimal trajectory satisfying state and input constraints, while still providing smooth lane change movements. Simulation results confirm the viability of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimal control systems

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Advanced Spatiotemporal Anomaly Detection in Photovoltaic Power Plants Using Temporal Graph Networks Integrated with Publicly Available Weather Data

Advanced Spatiotemporal Anomaly Detection in Photovoltaic Po...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Reddy, B.Ravali Awasthy, Neeta Keer, Priyameet Kaur Nijhawan, Ginni Mala, Vajenti Albawi, Ali Srinivas, T. Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331527495

For purposes of performance analysis and minimization of maintenance expenses, it is vital to identify an effective way in which to monitor anomalies in photovoltaic (PV) power plants. This paper proposes a novel framework to address the problem of anomaly detection based on the inherent graph structure of PV plant configurations using Temporal Graph Networks (TGNs). TGNs are built to capture spatial and temporal dependencies of the PV systems which allows detecting discrepancies affecting the power production, which traditional NN might fail not to notice. The model uses external weather data collected from NOAA and tracks how external parameters influence the performance of photovoltaic panels and therefore electricity generation. Our methodology solely employs TGNs to analyse the intricate relationship in the PV plant data to improve the recognition of abnormality associated with the equipment and weather conditions. The integration of NOAA's open source weather data into the extent of analysis further raises sophistication by offering a variety of features and conditions affecting PV results. Our approach achieves high accuracy of anomaly detection and can be considered to be better compared with the baselines that do not take graph dependencies or time aspect into account. This advancement has important implications for enhancing the operation and maintenance recommendations in the PV power plants and hence the reliability of the satellite power systems. Allying our efforts to one highly specialized method, this research presents a new state of the art in leveraging centralized data for anomaly detection in renewable energy. © 2025 IEEE.

关键词： Network theory (graphs)

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A Hybrid Swarm Intelligence Model for Efficient Query Optimization in Distributed Databases Using Genetic and Ant Colony Algorithms

A Hybrid Swarm Intelligence Model for Efficient Query Optimi...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Jayasri, Nemala Santosh Kumar, B. Singh, Navdeep Singh, Ajeet Al-Rubaye, Taqi Mohammed Khattab Rao, Mallikarjuna Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technologies The Islamic University Department of Medical Laboratory Technology Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

Optimisation of queries in distributed databases is more imperative to enhance the processing rate and use of resources with limited computational abilities. This work presents a novel GA-ACO hybrid swarm intelligence model to address the query optimization problem. The optimization effort of the proposed model starts with GA for an initial population of probable paths for query execution which uses the concept of evolution to select diverse solutions. After that, ACO strengthens this set by the application of pheromone-based reinforcement that allows adjusting of a query paths toward the best solution or its approximation with the following number values. In this way the enhancement of the convergence rates and the scalability of the proposed solution when distributed databases are used can be explained. The model was tested with artificial query logs with DEAP being used for the implementation of the genetic algorithm while ant colony operations were implemented using Python built ACO libraries. Performance analysis shows the success of the developed model in decreasing query response time as follows: This essentially complex hybrid mechanism shows potential for future use in other areas with large data sets and efficient query handling. © 2025 IEEE.

关键词： Ant colony optimization

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Analyzing Performance Patterns and user Experience in Online Cybersecurity Challenges: Insights from SKRCTF

Analyzing Performance Patterns and user Experience in Online...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Singh, Vinai K. Sengar, R.S. Pandey, Navneet Gl Bajaj Group of Institutions Department of Applied Science and Humanities Mathura India Gl Bajaj Group of Institutions Department of Electrical Engineering Mathura India Gl Bajaj Group of Institutions Department of Mechanical Engineering Mathura India

ISBN: (纸本)9798331527495

The field of cybersecurity education has undergone a significant transformation, with the emergence of innovative e-learning platforms that aim to bridge the gap between academic institutions and industry. This study investigates the effectiveness of SKRCTF, a customized Capture-the-Flag platform designed to support cybersecurity skill development, through comprehensive analysis of user performance patterns and learning experiences. Drawing from data collected from 120 participants over a 12-week period, this research employs mixed-methods analysis to examine learning progressions, engagement patterns, and skill development pathways in cybersecurity education. The findings reveal three key insights: 1. the emergence of distinct specialist and generalist learning patterns, challenging traditional assumptions about linear skill progression;2. the identification of an optimal engagement zone that maximizes learning effectiveness;and 3. the discovery that analytical challenges serve as effective entry points for cybersecurity skill development. The study also uncovers a complex relationship between perceived difficulty and learning outcomes, where higher perceived difficulty often correlates with increased persistence rather than decreased engagement. These insights contribute to both theoretical understanding of cybersecurity skill development and practical platform design considerations. The research suggests that effective cybersecurity education platforms should support multiple learning pathways, implement structured engagement mechanisms, and carefully sequence challenges to optimize skill development. These findings have significant implications for the design of cybersecurity education programs and the development of future security professionals. © 2025 IEEE.

关键词： engineering education

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Advanced Temporal Forecasting Models for Energy Consumption Prediction Using Recurrent Neural Networks and Additive Decomposition Techniques

Advanced Temporal Forecasting Models for Energy Consumption ...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Gopal, K. Rao, M. Srinivasa Abedi, Tamam Ali Abd Ulabbas Gupta, Manish Reddy, Gurram Vijendar Kumar, Ajay New Horizon College of Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

In this paper, the performance of two popular time series forecasting techniques, LSTM and Prophet, is analyzed to forecast energy consumption. Probably, one of the most important aspects for achieving efficient grid balance and resource management is energy demand forecasting. The data used in research includes energy consumption data which were cleaned from noise level and then normalized for model usage. In both models, outcomes are compared to performance measured on historical dataset. Mean Absolute Error (MAE) Root Mean Square Error (RMSE). LSTM, a Recurrent Neural Network which is extensively used for processing sequential and temporal data is compared with Prophet, a statistical model built by Facebook especially for analyzing seasonality and trends in time series data. The analysis shows that both approaches have their advantages and disadvantages and reveals how more accurate energy consumption forecasting might be achieved by employing a combination of the strategies. Comparing different evaluated models and their performance, possible variations in field application depending on the type of model and characteristics of the data set are described. © 2025 IEEE.

关键词： Recurrent neural networks

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STM32-based intelligent following trolley

STM32-based intelligent following trolley

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2024 international conference on Green Energy and Mechatronics engineering, ICGEME 2024

作者： Chen, Hong Fu Yan, Shu Bin Gao, Yuan Yuan Xu, Ai Wei College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China School of Electrical Engineering Zhejiang University of Water Resources and Electric Power Hangzhou310018 China Joint Laboratory of Intelligent Equipment and System for Water Conservancy Hydropower Safety Monitoring of Zhejiang Province and Belarus Hangzhou310018 China

In this paper, we design a kind of cart that uses ultrasonic module to realize automatic target following, which can realize high-precision target following. Existing following methods have distance and hardware limitations, this paper uses a single transceiver ultrasonic module to form a triangle with the target to follow the target by comparing the length of the distance between the two sides, the hardware cost is low and less interference can be stabilized following. We also designed a host computer to facilitate the control of the cart. Finally, we tested the following ability of the trolley, and the results show that the trolley's steering reaction speed can meet the requirements, and the following is accurate and fast. © Published under licence by IOP Publishing Ltd.

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A TFEM-Transformer Model for Lithium-Batteries Remaining Useful Life Prediction 16th

A TFEM-Transformer Model for Lithium-Batteries Remaining Use...

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16th international conference on Modelling, Identification and control, ICMIC 2024

作者： Nie, Yixin Ren, Yanhui Wang, Anqi Xiang, Gang Qu, Chen Yang, Fan Department of Automation Tsinghua University Beijing100084 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Lab on Aerospace Intelligent Control Beijing100854 China

ISBN: (纸本)9789819617760

With the widespread application of lithium batteries, the focus on battery performance evaluation and safety has increased. Accurate prediction of battery degradation and remaining useful life is now a crucial research topic. Scholars have improved prediction accuracy by integrating multiple parameters related to battery degradation or combining various prediction models. However, environmental noise, sensor limitations, and faults can impact the quantity and quality of available feature sequences, rendering these methods partially ineffective. This paper proposes an optimized Transformer prediction model based on a Temporal Feature Enhancement Module (TFEM). By decomposing long-sequence signals and accurately capturing temporal features across different frequency bands through convolution, the model significantly improves the accuracy of future degradation process assessments and remaining useful life predictions of batteries, even with a single prediction model and a limited number of feature sequences. The proposed TFEM exhibits strong adaptability and can be easily integrated into other temporal prediction models to enhance prediction accuracy. The prediction model demonstrates high generalizability and robustness, achieving favorable prediction results even in scenarios involving short sequence predictions or a limited number of feature sequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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