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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3588 条记录，以下是171-180 订阅

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An intelligent tester: automated test generation and test execution through machine learning and natural language processing 25

An intelligent tester: Automated test generation and test ex...

引用

25th international conference on Software engineering and data engineering, SEDE 2016

作者： Bischke, Christopher Choy, Yue Fung Urhiafe, Ejiroghene DiBenedetto, John Raytheon TewksburyMA01876 United States

this paper investigates an automated test generation and execution method that leverages a project's backlog of system requirements. Test generation depends on a combination of a Machine Learner and Natural Langua... 详细信息

ISBN: (纸本)9781943436057

关键词： Natural language processing systems

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Enhancing Diabetes Prediction with Advanced Machine learning Techniques

Enhancing Diabetes Prediction with Advanced Machine Learnin...

引用

22nd international conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Tian, Yuan Wang, Chuan Shi, Wen Zhou, Ying Zhou, Yi Vanderbilt University NashvilleTN37232 United States Department of Chemical Engineering Department of Biosystems Engineering Auburn University AuburnAL36849 United States Department of Economics TSYS School of Computer Science Columbus State University ColumbusGA31907 United States

ISBN: (纸本)9783031859014

Diabetes stands as one of the foremost causes of mortality in the United States. the imperative to predict diabetes in the country arises from its widespread prevalence, substantial healthcare expenses, potential severe complications, and the prospect of proactive prevention and early intervention. this study leverages data from the Framingham study to investigate the utilization of machine learning models in the realm of diabetes prediction. Consequently, the implementation of predictive models to combat diabetes holds the promise of yielding substantial positive outcomes for public health, optimizing healthcare resource allocation, and enhancing the overall health and welfare of individuals and communities. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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A Hybrid Tactical Decision-Making Approach in automated Driving Combining Knowledge-Based Systems and Reinforcement learning 25

A Hybrid Tactical Decision-Making Approach in Automated Driv...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Fiedler, Julius Gerwien, Maximilian Knoll, Carsten Tech Univ Dresden Inst Control Theory Dresden Germany IAV GmbH Dept Intelligent Driving Funct Dresden Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Decision-making in automated driving is influenced both by objective traffic rules and subjective perceptions and goals of the driver. thus, a suitable representation of the environment of the autonomous vehicle is required to model complex traffic situations and extract key features. To achieve this objective, this work uses an ontology-based situation interpretation (OBSI) to model traffic situations. the resulting semantic state representation is used to train models of vehiclecontrolling agents using reinforcement learning. Based on our simulations, it can be shown that the semantic preprocessing of traffic situations significantly improves the agent's performance regarding safety and driving style.

关键词： Training Semantics Knowledge based systems Decision making Reinforcement learning data collection Feature extraction

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Scalable data Set Distillation for the Development of automated Driving Functions 25

Scalable Data Set Distillation for the Development of Automa...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Rigoll, Philipp Ries, Lennart Sax, Eric FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Since highly automated driving functions are obligated to work safely without human interference, careful and comprehensive testing is mandatory. Due to the open world context, automated driving functions must be able to cope with a huge variety of driving situations. To achieve this goal, huge data sets are being recorded. these data sets are used in the development and in open loop tests where the data is replayed to examine driving functions under diverse conditions. the driving functions design is based on machine learning to cope with the variant environment. For the generation of meaningful testing evidences, well-tailored data sets are necessary. To avoid costly processing of voluminous sensor data, the focus lies on a method based on metadata. therefore, we present a methodology based only on the position and temporal information for the distillation of automotive data sets. the method is intended to support the driving function experts and can be easily parameterized by them according to their requirements. this enables broad scalability and allows to efficiently find specific and potentially challenging situations. the process is presented exemplarily for sun positions and geographical data.

关键词： Scalability Machine learning Interference Metadata Sun intelligent transportation systems Testing

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Prediction of human driving behavior using deep learning: a recurrent learning structure 25

Prediction of human driving behavior using deep learning: a ...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Pousseur, Hugo Correa Victorino, Alessandro Heurist & Diag Complex Syst CS60319 F-60203 Compiegne France

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Predicting the intentions of the human and the machine on a near future is required to the human-machine shared control of automated intelligent vehicles. the autonomous system is able to inform about its future intentions, however it is not possible for the human to provide this information, it is necessary then to predict it. this paper proposes a deep learning methodology to predict human navigation intentions in a time horizon of a few seconds, using a recurrent neural network (RNN) architecture based on the Long Short-Term Memory (LSTM) architecture. Taking as input various preprocessed and non-preprocessed data, generated by embedded sensors and the intrinsic data of the vehicle, the proposed model predicts the future linear and angular velocities of the vehicle. the model was trained and tested on a dataset created from real data from our cars equipped with sensors (LiDAR, camera), in different scenarios and road types. Furthermore, a data sensitive study is presented evaluating the effects of missing data in the learning process.

关键词： Deep learning Recurrent neural networks Navigation Roads Memory architecture Predictive models data models

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An ontology-based approach for preprocessing in machine learning 25

An ontology-based approach for preprocessing in machine lear...

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25th IEEE international conference on intelligent engineering Systems (INES)

作者： Soto, Patricia Centeno Ramzy, Nour Ocker, Felix Vogel-Heuser, Birgit Infineon Technol AG Munich Germany Tech Univ Munich Inst Automat & Informat Syst Munich Germany

ISBN: (纸本)9781665444996

Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine learning models. However, Machine learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. this paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.

关键词： semantic preprocessing machine learning semantic web technologies

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the Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement

The Vital Role of Small and Marginal Farmer in Future of Our...

引用

25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Vuppalapati, Chandrasekar Ilapakurti, Anitha Vissapragada, Sandhya Mamidi, Vanaja Vuppalapati, Sriya Vuppalapati, Akshay Kedari, Sharat Kedari, Santosh Vuppalapati, Jaya Kallakur, Karthik Rajasri, Koduru Lella, Narendra San Jose State University San Jose United States Hanumayamma Innovations and Technologies Inc. Fremont United States Hanumayamma Innovations and Technologiesa PVT Limited Hyderbad India

ISBN: (纸本)9783031859229

Small and marginal dairy farmers are a crucial part of the global farming community, and there are approximately 570 million farms worldwide, with the majority being small farms, as per the Food and Agriculture Organization (FAO). the international Farm Comparison Network (IFCN) estimates that there are around 133 million dairy farms globally. Despite being a significant source of income for small dairy farmers, they often face challenges regarding cattle productivity and health issues. Interestingly, cattle rumination counts could be the solution to these issues, not only for small farmers but also for the world at large. Cattle rumination count is a measure of how much time cows spend chewing cud, which is a mixture of partially digested food regurgitated from the cow’s stomach to the mouth and further broken down by chewing before being swallowed again. Rumination count is a critical indicator of a cow’s health and productivity since cows that chew more tend to have better digestion, absorb more nutrients from their feed, and produce more milk. Cattle rumination count is also related to methane emissions, as cows release methane gas during the digestive process, especially during rumination. Methane is a potent greenhouse gas that contributes to climate change. By using Dairy IoT Cattle Cow Necklace sensors, small and marginal farmers worldwide can track rumination counts proactively. the Dairy Cow Necklace sensor records the time spent chewing cud, and this data can be used by small farmers and veterinarians to identify cows experiencing digestive issues or other health problems and adjust their feeding and management practices accordingly. Reducing methane emissions from dairy cattle is a vital part of global efforts to mitigate climate change since livestock, including dairy cattle, contributes to around 14.5% of global greenhouse gas emissions. In a recent study, rumination counts were captured from three major dairy producing areas in India (Vishakhapatnam,

关键词： COP21 Cow Necklace Sensor Methan emissions Rumination

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Early Alzheimer’s Detection Using Bidirectional LSTM and Attention Mechanisms in Eye Tracking

Early Alzheimer’s Detection Using Bidirectional LSTM and A...

引用

作者： Ghayoumi, Mehdi Ghazinour, Kambiz Ghayoumi, Mehrad CyberSecurity Department SUNY at Canton CantonNY United States ALLGalaxy Los AngelesCA United States

ISBN: (纸本)9783031859014

this study introduces a deep-learning framework aimed at improving the early detection of Alzheimer’s Disease (AD) through the analysis of eye movement patterns. We developed a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with an attention mechanism, utilizing a dataset consisting of eye movement data from both early-stage AD patients and a control group. the model captures temporal dynamics and ocular characteristics that may indicate early cognitive decline. Our empirical results show that the Bi-LSTM network with attention mechanism performs better than traditional models in metrics such as accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic (ROC) curve. these findings suggest that eye movement data could be a useful, non-invasive tool for early AD detection. the study highlights the potential for more accessible and timely diagnostic methods, which could support earlier intervention and better patient outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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ExAgt: Expert-guided Augmentation for Representation learning of Traffic Scenarios 25

ExAgt: Expert-guided Augmentation for Representation Learnin...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Balasubramanian, Lakshman Wurst, Jonas Egolf, Robin Botsch, Michael Utschick, Wolfgang Deng, Ke TH Ingolstadt CARISSMA Inst Automated Driving D-85049 Ingolstadt Germany Tech Univ Munich D-80333 Munich Germany RMIT Univ Melbourne Vic Australia

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Representation learning in recent years has been addressed with self-supervised learning methods. the input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions cross-view prediction. Augmentation is one of the key components in cross-view self-supervised learning frameworks to learn visual representations. this paper presents ExAgt, a novel method to include expert knowledge for augmenting traffic scenarios, to improve the learnt representations without any human annotation. the expert-guided augmentations are generated in an automated fashion based on the infrastructure, the interactions between the EGO and the traffic participants and an ideal sensor model. the ExAgt method is applied in two state-of-the-art cross-view prediction methods and the representations learnt are tested in downstream tasks like classification and clustering. Results show that the ExAgt method improves representation learning compared to using only standard augmentations and it provides a better representation space stability. the code is available at https: //***/lab176344/ExAgt.

关键词： Representation learning Visualization Codes Self-supervised learning Prediction methods Distortion Stability analysis

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A Smart Air Quality Analysis and Pollutant Diffusion Detection and Prediction System Based on Tree Canopy Shape Research Using Machine learning and Artificial Intelligence

A Smart Air Quality Analysis and Pollutant Diffusion Detecti...

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作者： Liu, Mingyuan Li, Ang Montverde Academy Shanghai Shanghai China EECS Department California State University Long Beach United States

ISBN: (纸本)9783031859014

this project represents a significant foray into the intersection of computational fluid dynamics (CFD), machine learning, and environmental science [1]. By integrating CFD results with the predictive capabilities of computer vision and AI, we have crafted a multifaceted approach that not only serves environmental sciences by forecasting air quality but also advances the field of machine learning with its interdisciplinary applications [2]. the developed model stands as a testament to this synergy, exhibiting high levels of accuracy in its predictions, albeit with occasional outliers. Recognizing the model’s substantial promise, we are committed to its ongoing refinement. Future efforts will be channeled into expanding its data foundation and exploring innovative algorithmic strategies, underscoring our long-term commitment to enhancing the project's contribution to both scientific domains. this sustained investment is poised to solidify and extend the practical and theoretical benefits of our interdisciplinary methodology. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Model predictive control

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