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共 3165 条记录，以下是501-510 订阅

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Identification of Student Programming patterns through Clickstream data 5

Identification of Student Programming Patterns through Click...

引用

5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Anika Jindal, Mani Goyal, Ankush Bennett University Scset Greater Noida India School of Business and Management Bangalore India

ISBN: (纸本)9798350383522

In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. this study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. © 2024 IEEE.

关键词： clustering computer programming educational data mining regression student programming patterns

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Real-Time Innovation in Future Generation Security for data Privacy Intrusion System in machine learning Approach 6

Real-Time Innovation in Future Generation Security for Data ...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Wankhede, Hansaraj Shalikram Venkatesh, R. Pundr, Sumit Madala, Ravikiran Babu, Kamunuri Ganapathi Mallaiah, Badepally G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India University of Cumberlands DurhamNC United States St. Martin's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India CVR College of Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350304480

Programming called an intrusion discovery system watches over a solitary PC or an organization of PCs for pernicious movement that intends to take or edit data or change network conventions. Most of intrusion recognition methods utilized today are unequipped for guarding against the dynamic and perplexing nature of cyber-attacks on PC organizations. Albeit successful versatile procedures, for example, unique machine learning calculations, may prompt more noteworthy discovery rates, diminished deception rates, and suitable calculation and transmission costs. data mining might be utilized to make miniature data streams, classification, and example mining rather frequently. the real-time intrusion identification system and related intrusion counteraction system are introduced in this work utilizing machine learning. To prepare the model, a convolutional brain organization (CNN) is utilized. A 5G data network assessment of the innovation was directed. the improvement of programming characterized networks is thought about by brilliant intrusion location systems (IDS). It utilizes man-made reasoning based demonstrating. Convolutional brain network-based programming parts empower the system to distinguish intrusions that were not recently recognized. Intrusion Prevention System 'IPS'is to stop malicious traffic. Based on the evaluation, this solution provides more buffers than comparable alternatives, facilitating real-time management in 5G settings. © 2023 IEEE.

关键词： machine learning

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A Novel Semi-Supervised learning Method Using Causal Margin Adaptation for Imbalanced Classification

A Novel Semi-Supervised Learning Method Using Causal Margin ...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yilan Hou Yujun Zeng Yixing Lan Junkai Ren Zhaowei Ma College of Intelligence Science National University of Defense Technology Changsha China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

When semi-supervised learning (SSL) comes across imbalanced or long-tailed data, which is common in realistic applications such as autonomous driving, it will usually suffers from a severe deterministic bias which leads to a significant performance degradation of SSL model. In this paper, a novel SSL method using causal margin adaptation for imbalanced classification is proposed. Firstly a new mechanism for propensity score margining is used to eliminate the false association between class labels and input data or the corresponding features, while relieve the adverse effects coming from the violation of the class-balance assumption. Secondly, counterfactual inference estimation is adopted to obtain the margin used for interpolating and estimating unbiased pseudo labels and SSL model output. Experimental results on standard imbalanced dataset Cifar-10-TL verify the effectiveness of the proposed method.

关键词： Degradation Adaptation models Interpolation Heavily-tailed distribution Estimation machine learning Semisupervised learning data models pattern recognition Standards

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Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma 47

Measuring Bias in AI Models: An Statistical Approach Introdu...

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47th IEEE-Computer-Society Annual international conference on Computers, Software, and Applications (COMPSAC)

作者： DeAlcala, Daniel Serna, Ignacio Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain

ISBN: (纸本)9798350326970

the new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. the proposal highlights the need to develop adequate risk assessments for the different uses of AI. this risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

关键词： Artificial Intelligence AI Bias Explainable Risk Assessment Trustworthiness 5-Sigma

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Spatially Augmented Speech Bubble to Character Association via Comic Multi-task learning

Spatially Augmented Speech Bubble to Character Association v...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Soykan, Gurkan Yuret, Deniz Sezgin, Tevfik Metin Koc Univ Comp Engn Dept Istanbul Turkiye KUIS AI Ctr Istanbul Turkiye

ISBN: (纸本)9783031706448;9783031706455

Accurately associating speech bubbles with corresponding characters is a challenging yet crucial task in comic book processing. this problem is gaining increased attention as it enhances the accessibility and analyzability of this rapidly growing medium. Current methods often struggle with the complex spatial relationships within comic panels, which lead to inconsistent associations. To address these short-comings, we developed a robust machine learning framework that leverages novel negative sampling methods, optimized pair-pool processes (the process of selecting speech bubble-character pairs during training) based on intra-panel spatial relationships, and an innovative masking strategy specifically designed for the relation branch of our model. Our approach builds upon and significantly enhances the COMIC MTL framework, improving its efficiency and accuracy in handling the unique challenges of comic book analysis. Finally, we conducted extensive experiments that demonstrate our model achieves state-of-the-art performance in linking characters to their speech bubbles. Moreover, through meticulous optimization of each component-from data preprocessing to neural network architecture-our method shows notable improvements in character face and body detection, as well as speech bubble segmentation.

关键词： Speech Bubble Association Speech Bubble to Character Association Deep learning for Comics Comic Book Analysis Multi-Task learning

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Underwater Acoustic Target Classification with Joint learning Framework and data Augmentation 5

Underwater Acoustic Target Classification with Joint Learnin...

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5th IEEE international conference on Artificial Intelligence and Big data, ICAIBD 2022

作者： Chen, Liang Liu, Feng Li, Daihui Shen, Tongsheng Zhao, Dexin Institute of Ocean Engineering and Technology Zhejiang University Zhoushan China National Innovation Institute of Defense Technology Chinese Academy of Military Science Beijing China

ISBN: (纸本)9781665499132

As speech recognition technologies have garnered increasing attention recently, the possibility of integrating related technologies into the field of underwater acoustic target classification (ATC) has piqued researchers' curiosity. However, the classification of underwater acoustic targets confronts several obstacles due to the time-varying and complicated nature of the underwater environment,, such as the low signal-to-noise ratio and background interference. To address these concerns, this paper proposes a combined feature extraction framework and a deep convolutional neural network learning framework. this combination features include MFCC, CQT, Gammatone, Log-mel which are extracted from the underwater acoustic signal and fed into the joint learning framework. the classifier has a deep learning architecture, which is a deep neural network system that can learn differentiating features from physical feature representations to abstract concepts in a hierarchical pattern. Multiple datasets under diverse settings have been used to verify the performance of our proposed architecture. Using the acoustic data gathered in several underwater experiments conducted in a shallow sea setting, the proposed framework achieves a recognition rate of 89.9% and a competitive classification accuracy in the underwater scenario. © 2022 IEEE.

关键词： Deep neural networks

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2021/ 5th international conference on Emerging data and Industry 4.0, EDI40 2022

13th International Conference on Ambient Systems, Networks a...

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2021/ 5th international conference on Emerging data and Industry 4.0, EDI40 2022

the proceedings contain 107 papers. the topics discussed include: are professional drivers more aggressive than general drivers? a case study from Doha, Qatar;information upwards, recommendation downwards: reinforcement learning with hierarchy for traffic signal control;hand gestures identification for fine-grained human activity recognition in smart homes;predicting truck parking occupancy using machine learning;filtering data bins of UWB radars for activity recognition with random forest;pedestrian movement modelling for a commercial street considering COVID-19 social distancing strategies;and exploring persona characteristics in learning: a review study of pedagogical agents.

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Restaurant Queuing Time Prediction Using Random Forest Regression 12

Restaurant Queuing Time Prediction Using Random Forest Regre...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Xue, Yijia Zhang, Xiang DukeKunshan Univ Suzhou 215316 Peoples R China Southeast Univ Nanjing 210096 Peoples R China

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

Unknown queuing time often brings negative consumer experience in restaurant service. Prediction of queuing time has been a critical task to benefit both consumers and restaurants. However, current research mainly focuses on the queuing theory, which requires collecting prior knowledge and survey. this paper proposes a queuing time prediction model based on Random Forest Regression with features selection method to ease the limitations of previous research. Random Forest Algorithm have achieved enormous success in various fields. Experiments on a real-life restaurant queuing database validate the practicality of the model. the proposed method outperforms the baseline model on the experimental data. We conclude the intrinsic information of the queuing model with the feature selection method, which will help improve other application models in queuing scenarios.

关键词： Queuing time prediction feature selection random forest regression data mining machine learning

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Critical Radical Analysis Network for Chinese Character recognition 26

Critical Radical Analysis Network for Chinese Character Reco...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Luo, Guo-Feng Yin, Hua-Yi Wang, Da-Han Zhang, Xu-Yao Zhu, Shun-Zhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Zero-shot learning is a challenging problem in many tasks due to the lack of training samples of the unseen classes. the radical-based zero-shot Chinese character recognition methods treat Chinese characters as a combination of radicals and structures, and recognize Chinese characters by identifying the radicals and structures contained in them. Current approaches generally treat the contribution of all radicals to Chinese character recognition as the same, and the recognition results rely on the network's ability to recognize radicals and their corresponding position information, ignoring the potential value of radicals themselves in eliminating the uncertainty of Chinese characters. In this paper, we model the problem of radical-based Chinese character recognition as an uncertainty elimination problem and propose a Critical Radical Analysis Network (CRAN) to explore the Ideographic Description Sequence (IDS) information for zero-shot Chinese character recognition. Specifically, we propose a novel method to compute the critical values of radicals based on information theory using the predefined Chinese character IDS dictionary. In recognition, we use an iterative approach to translate the predicted radical sequence to target Chinese characters. that is, the radicals of the predicted sequence are sorted in descending order of the critical value, and then the radicals are continuously selected in this order as the information obtained to eliminate the uncertainty of the Chinese character until the character is recognized. We conduct experiments on the CTW, CASIA-AHCDB, and CASIA-HWDB datasets. the experimental results show that the proposed method improves the ability of recognizing unseen Chinese characters, demonstrating the effectiveness of the proposed method.

关键词： Zero-shot learning Chinese character recognition

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An Efficient Selective Features Approach to Detect Hypothyroid Using machine learning 5th

An Efficient Selective Features Approach to Detect Hypothyro...

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Chandra, N. Subhash Dhanikonda, Srinivasa Rao Jagli, Dhanamma Siddhartha, Nalla Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Information Technology BVRIT HYDERABAD College of Engineering for Women Hyderabad India V.E.S.Institute of Technology University of Mumbai Mumbai India Indian Institute of Technology Tirupati Tirupati India

ISBN: (纸本)9789819997039

the thyroid gland is a hormone-producing gland located in the upper part of the neck. It helps to regulate the metabolism of the body. Its malfunction might lead to either inadequate or excessive thyroid hormone production. thyroid issues are classified as hyperthyroidism or hypothyroidism. Predicting thyroid illness is now more crucial than ever. machine learning algorithms and data mining techniques are critical for dealing with today’s massive amounts of data and information, particularly in the health-care system. A specific dataset is created that contains patient-related medical data. Based on the attributes considered, it is possible to categorize thyroid disease into three categories: primary hypothyroid, compensated hypothyroid, and routine. To identify earlier, various conventional machine learning methods are utilized, including Random Forest, Decision Tree, and Artificial Neural Network (ANN). these algorithms carry out the classification and determine the status but fall short of the desired results, particularly in the medical sector. In order to accomplish the goal, this research developed an effective classification strategy to improve the performance of hypothyroid prediction and it is divided into two stages: data preprocessing and classification methods. During the preparation phase, the dataset was preprocessed using two methods: PCA and an Extra Tree classifier, and the data was enriched with the Synthetic Minority Oversampling Technique (SMOTE). the proposed approach's outcomes were compared to traditional methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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