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Enhancing Model Interpretability Through Interactive Visual Analysis and Counterfactual Explanation Methods 21st

Enhancing Model Interpretability Through Interactive Visual ...

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21st international conference on Cooperative Design, Visualization, and engineering (CDVE)

作者： Lan, Hanlin Pu, Jiansu Xia, Yulu He, Yilei Zhang, Yu Huang, Jinyue Rao, Yunbo Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Big Data Visual Anal Lab Chengdu Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

ISBN: (纸本)9783031713149;9783031713156

Counterfactual explanation is a post-modeling technique that helps users manipulate input features to achieve desired model decisions. This study employs counterfactual explanations to help users understand predictive outcomes and their underlying reasons. By reversing model decisions, user issues can be addressed. We developed a visual analysis framework combining machine learning algorithms and visual analytics. And our primary efforts are outlined as follows: Establish a visual analysis framework combining machine learning algorithms and visual analysis. Use counterfactual interpretation, we improved model interpretability and helped users understand prediction results. Design visualization views according to the visual analysis tasks derived from user needs based on machine learning models, and designed counterfactual explanation operation views for model decision instances. Integrate the visual analysis view, realize the interactive visual analysis system CFEVis based on the credit approval data prediction model.

关键词： Machine learning Counterfactual Explanation Model Prediction Visual Analysis

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Leveraging Semi-Supervised learning to Enhance data Mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector Machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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Sparse Context Transformer for Few-Shot Object Detection 21st

Sparse Context Transformer for Few-Shot Object Detection

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Jiu, Mingyuan Mei, Jie Sahbi, Hichem Jiang, Xiaoheng Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou China National SuperComputing Center in Zhengzhou Zhengzhou China Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9789819601240

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for the fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluated the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity 21st

Enhanced Knowledge Tracing via Frequency Integration and Ord...

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21st Pacific Rim international conference on Artificial Intelligence

作者： Cai, Songtao Li, Li Southwest Univ Sch Software Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved considerable success, yet they have neglected the frequency of interactions between students and knowledge points. Frequency information can aid in more stable modeling of students' knowledge states. In this study, we incorporate frequency data into our model's question embeddings, enabling it to consider the frequency of student-knowledge concept interactions. Moreover, the order within students' learning sequences is crucial, but knowledge tracing models based on the transformer architecture, with their attention mechanisms, are insensitive to the sequence order. We propose the N-transformer structure that combines transformer with order-sensitive RNNs, effectively enhancing the model's sensitivity to the order in students' learning sequences. Subsequently, we employ a simpler and more effective method for prior knowledge modeling, directly extracting prior knowledge related to the current exercise from the model's predicted knowledge state at the previous time step. Finally, we have designed a decay function for the attention mechanism based on the Ebbinghaus forgetting curve to simulate the students' forgetting behavior. Ultimately, we conducted experiments on four datasets, Our model has achieved up to a 3% increase in AUC compared to the baseline on some datasets.

关键词： Knowledge tracing Deep learning intelligent tutoring systems

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Autonomous cruise control of intelligent high-speed train with online energy-saving optimization 21

Autonomous cruise control of intelligent high-speed train wi...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhou, Feng Liang, Hongkun Tao, Kewu Li, Shuo Changsha University of Science and Technology College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350329223

The rapid development of intelligent transportation technology has promoted the progress of multiple trains cooperative technology. This paper proposes an online cooperative cruise control method based on improved particle swarm optimization (IPSO) and deep feedforward neural network (DFNN), which enables each train to adaptively adjust the controller according to its own state information, ensuring speed synchronization and maintaining safe distance intervals between trains. Through the collection of information from the train's big data, the optimal time-varying cooperative control gain is fitted to achieve efficient, safe, and real-time control of multiple high-speed trains. © 2023 IEEE.

关键词： Real time control

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An Improved learning Experience for English Language Using Big data Analytics and AAC 2

An Improved Learning Experience for English Language Using B...

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2nd international conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Kumar, P.Vinoth Balamurugan, D. Suriya, K. Reshma, R. Faculty of Engineering Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665491990

Teachers and students in the 21st century are shifting their attention from the classroom to digital education, aiming for meaningful, high-quality, and dynamic learning. With the introduction of digital learning, the educational system has undergone significant transformations. Some teachers/instructors, on the other hand, are still experiencing difficulties. For this reason, this research finds a solution named AAC integrated Big data Analytics Framework (AACBDAF) for people with neural disorders in the English language. AAC-BDAF gathers all the linguistic data and categorizes it based on how frequently it is used. To input data, the AAC system uses eyeball tracking to display symbols and characters on a screen. To help the listener follow along, the selected option/word is converted into a text-to-speech paradigm. The detection algorithms of machine learning and deep learning are evaluated for word detection and suggest the convolutional neural network (CNN) support AACBDAF. According to simulation findings, the proposed AAC-BDAF recognizes phrases and predicts the word from their eye-gazing with higher accuracy of 98.63%. © 2023 IEEE.

关键词： students

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Comparison of data Visualization Platforms Through European Traffic data 11

Comparison of Data Visualization Platforms Through European ...

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11th IEEE international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Nagy, Eniko Tamas Ferenc, Gombos Molnar, Gyorgy Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary Óbuda University John von Neumann Faculty Budapest Hungary Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University Apáczai Csere János Faculty of Humanities Education and Social Sciences Győr Hungary

ISBN: (纸本)9798350350142

The most important and valuable resource in the 21st century is data. The age of the internet brought us an era in which humans, within a foreseeable time, are unable to process the vast amount of data that we produce. It's important to understand the data and statistics we have because it can help a company make a well thought out decision on a given dilemma, which is based on reality and data. When choosing a data visualisation platform, the purpose of the application, the type of data and the user needs are also key factors. For the visualisation of traffic data in Europe, a number of established platforms can be considered and are referred to in this paper. This article compares data visualisation platforms currently available and used in industrial environments with open source free platforms, using European traffic data. © 2024 IEEE.

关键词： data visualization

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Dynamic Bipedal Turning through Sim-to-Real Reinforcement learning 21

Dynamic Bipedal Turning through Sim-to-Real Reinforcement Le...

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IEEE-RAS 21st international conference on Humanoid Robots (Humanoids)

作者： Yu, Fangzhou Batke, Ryan Dao, Jeremy Hurst, Jonathan Green, Kevin Fern, Alan Oregon State Univ Collaborat Robot & Intelligent Syst Inst Corvallis OR 97331 USA

ISBN: (纸本)9798350309799

For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nominal locomotion gaits and more specialized transient maneuvers. Despite recent advancements in controls of bipedal robots, there has been little focus on producing highly dynamic behaviors. Recent work utilizing reinforcement learning to produce policies for control of legged robots have demonstrated success in producing robust walking behaviors. However, these learned policies have difficulty expressing a multitude of different behaviors on a single network. Inspired by conventional optimization-based control techniques for legged robots, this work applies a recurrent policy to execute four-step, 90 degrees turns trained using reference data generated from optimized single rigid body model trajectories. We present a training framework using epilogue terminal rewards for learning specific behaviors from pre-computed trajectory data and demonstrate a successful transfer to hardware on the bipedal robot Cassie.

关键词： Reinforcement learning

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An Effective Automatic Recognition Approach of Chinese Herbal Medicine by Micro Image Processing 21

An Effective Automatic Recognition Approach of Chinese Herba...

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21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Pang, Guangyao Yu, Zhenming Pan, Honglin Xie, Chengwen Gong, Ping Zhu, Xiaoying Peng, Zizhen Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Ind Software Technol Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： Image recognition identification of traditional Chinese medicine transfer learning data enhancement microscopic image

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AI-Enabled learning for Imbalanced Class data using Spark streaming Concepts 21

AI-Enabled Learning for Imbalanced Class Data using Spark St...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Tiwari, Sadhana Zohair, Mohammad Chandra, Ritesh Agarwal, Sonali Agarwal, Parikshit Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Jamia Millia Islamia Department of Computer Engineering New Delhi India SIET Department of Computer Application Prayagraj India

ISBN: (纸本)9798350358230

Chronic Diseases are a very significant problem around the world. Manhandling medical cases often leads to significant repercussions in the treatment process and needs to be detected and dealt with early on. Unusual clinic actions are often related to medical errors, enabling detection and alerting. Huge leaps in machine learning, data science, and big data analytics techniques have led to innovative ways to deal with these issues. As a result, mortality rates can be greatly reduced in all sorts of medical cases. In this work, a new ensemble method is proposed for multiclass classification applied on the US-Chronic Disease Indicator (US-CDI) dataset and validated using UCI-Diabetes data by applying K-means clustering to it to detect chronic disease. The proposed hybrid model, KD-Boost Ensemble, is based on the concept of ensemble voting mechanisms, which combine the predictions of the K-neighbors classifier, Decision Tree classifier, and Gradient Boosting classifier to obtain improved accuracy over the dataset when compared to the individual conventional classifiers. This chronic disease classification achieved 96% accuracy using the proposed model. The devised model is validated through the diabetes dataset in a real-time environment and attains 98% accuracy. © 2023 IEEE.

关键词： Decision trees

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