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Can Large Language Models Recognize and Respond to Student Misconceptions? 6th

Can Large Language Models Recognize and Respond to Student M...

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6th international conference on Adaptive Instructional Systems (AIS) Held as Part of the 26th international conference on Human-Computer Interaction (HCII)

作者： Smart, Francis Bos, Nathan D. Bos, Jaelyn T. Michigan State Univ E Lansing MI 48824 USA Johns Hopkins Univ Baltimore MD USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9783031606083;9783031606090

Expert human tutors can observe learner mistakes to understand their misconceptions and procedural errors. Highly capable, but opaque large language models have shown remarkable abilities across numerous domains, and may be useful for adaptive instruction in a variety of ways. Working with publicly available data from the National Assessment of Educational Progress, (388 questions selected from 4th, 8th and 12th grade math and science) we examined these three questions: 1) Do language models find the same problems difficult as students do? We found statistically significant, but small similarities in performance that differ somewhat by model. 2) Do language models have the same pattern of errors as students? Our findings reveal that, under the "minimal " prompts, the models often mirror students in choosing the same incorrect answers. However, this alignment decreases when prompt models used "chain of thoughts". 3) Can language models interpret and explain students' wrong answers? We presented frequently-chosen wrong answers toNAEP items to GPT-4 and an experienced science teacher, and compared their explanations. there was a good correspondence between these explanations, with 81% being fully or partially in agreement. Discussion focuses on how these capabilities can be used for test design and adaptive instruction.

关键词： Large Language Model Difficulty Prediction Distractor Prediction Distractor Analysis Item Analysis Question Difficulty Large Language Models machine learning Natural Language Processing

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A Comprehensive Review on machine learning in Breast Cancer Analysis 6th

A Comprehensive Review on Machine Learning in Breast Cancer ...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Ananthanagu, U. Ebin, P.M. Chinnaiyan, Ramasubramanian Department of Computer Science Engineering Alliance University Bangalore560102 India Pro Vice-Chancellor Lingayas Vidyapeeth Deemed-to-be University Haryana Faridabad India

ISBN: (纸本)9789819728381

One of the most feared illnesses is cancer since it sneaks up on people and you don't know how to fight it. Early and accurate detection are essential for efficient treatment and better patient outcomes for breast cancer, a critical worldwide health concern. For the investigation, Wisconsin Diagnostic Breast Cancer (Diagnostic) (WDBC) dataset was considered from the repository for machine learning at UCI. Medical records pertaining to breast cancer might produce insightful results using data mining techniques, such as trends in behavior and frequent/rare item distribution. To determine the best model, the study has been applied to several machine learning classification algorithms -SVM, SMO, NB, Attribute Selected Classifier, Decision Strump, J48, using Weka 3.8.3. In all these algorithms, Wrapper-Subset-Evaluation is used to apply feature selection. On the breast cancer data set, it shows that the J48 method performs better than every other classifier. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Automatic target recognition module development for fire control system based on machine learning 6

Automatic target recognition module development for fire con...

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6th Modern machine learning Technologies Workshop, MoMLeT 2024

作者： Vysotska, Victoria Romanchuk, Roman Nazarkevych, Mariia Lavrut, Oleksandr Lavrut, Tetiana Pysarchuk, Maksym Lviv Polytechnic National University Stepan Bandera 12 Lviv79013 Ukraine Hetman Petro Sahaidachnyi National Army Academy Heroes of Maidan 32 Lviv79026 Ukraine Organization and Planning Department of the Training Center "Partnership for Peace" of the International Peacekeeping and Security Center Lviv79026 Ukraine

Target recognition is a priority in military affairs. this task is complicated by the fact that it is necessary to recognize moving objects, different topography and landscape create obstacles for recognition. Combat actions can take place at different times of the day, accordingly, the lighting angle and general lighting must be taken into account. It is necessary to detect the object in the video by segmenting the video frames and to recognize and classify it. In the work, the authors propose the development of a target recognition module as a component of the fire control system within the framework of the proposed information technology through artificial intelligence use. the YOLOv8 pattern recognition model family was used to develop the target recognition module. the data was collected from open sources, in particular, from video footage posted in open sources on the YouTube platform. the main task of data pre-processing is the classification of three classes of objects on video or in real-time - APC, BMP, and TANK. the dataset is formed using the Roboflow platform based on marking tools and, subsequently, augmentation tools. the data set consists of 1193 unique images - approximately equally for each class. the training was conducted using Google Colab resources. 100 epochs were taken to train the model. the analysis is carried out according to mAP50 (mean Average Precision as 0.85), mAP50-95 (0.6), precision (0.89) and recall (0.75) metrics. Large losses are present because the background was not taken into account in the study - training the module based on validated data (images) of the background without the technique. this will be the next step. It is also necessary to expand the classification of objects of military equipment. © 2024 Copyright for this paper by its authors.

关键词： machine learning

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6th international conference on Big data Cloud and Internet of things, BDIoT 2022

6th International Conference on Big Data Cloud and Internet ...

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6th international conference on Big data Cloud and Internet of things, BDIoT 2022

ISBN: (纸本)9783031283864

the proceedings contain 49 papers. the special focus in this conference is on Big data Cloud and Internet of things. the topics include: Knowledge Embeddings for Explainable Recommendation;Design of Blended learning Course Based on SPOC for Primary School Teachers: Case of Soft Skills;the Impact of the Online Flipped Classroom on the learning Outcomes and Motivation of Nursing Students in Computer Science;digitizing Writing: An Empirical Exploratory Study Towards Students’ Writing Skill Fluency Improvement in ‘Padletosphere’;AraBERT with GANs for High Performance Fine-Grained Dialect Classification;genetic-Novelty Oversampling Technique for Imbalanced data;neural Network for Arabic Text Diacritization on a New dataset;processing Time Performance Analysis of Scheduling Algorithms for Virtual machines Placement in Cloud Computing Environment;Business - IT Alignment in Cloud Environment Comparative Study;machine learning-Based Intrusion Detection System: Review and Taxonomy;reduce Cheating in e-Exams Using machine learning: State of the Art;comparative Study of Bayesian Optimization Process for the Best machine learning Hyperparameters;the Effect of Normalization and Batch Normalization Layers in CNNs Models: Application to Plant Disease Classifications;the Role of Ground Truth Annotation in Semantic Image Segmentation Performance for Autonomous Driving;fine-Tuning Pre-trained Vision Transformer Model for Anomaly Detection in Video Sequences;An Efficient Real-Time Moroccan Automatic License Plate recognition System Based on the YOLO Object Detector;evaluation of Selected machine learning Models and Features for Electrical Consumption Prediction in Educational Institutions;Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised machine learning Algorithms;SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation;Intelligent Evaluation System Using NLP;retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subj

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Experimental design of emotion recognition based on machine learning 6

Experimental design of emotion recognition based on machine ...

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6th international conference on machine learning and Natural Language Processing, MLNLP 2023

作者： Yu, Jintao Jiang, Mingze Liang, Tingwei School of Computer and Information Engineering Harbin University of Commerce China

ISBN: (纸本)9798400709241

In this paper, a wrist bracelet was used to measure the subjects' heart rate signals and skin electrical signals, and three types of emotions (positive, neutral and negative) were induced by designing an experimental environment. the heart rate and skin electrical signals corresponding to emotions were collected through the bracelet, and the recognition accuracy of the three types of emotions was finally obtained through data preprocessing and the support vector machine model in machine learning. At present, how to build an experimental environment that can fully induce subjects' emotions is a major difficulty in emotion recognition, so this paper provides a specific emotion recognition environment design and detailed steps. Meanwhile, through a large number of experiments, the kernel function in the support vector machine model, namely Gaussian kernel function, is determined, and grid search is introduced to search for hyperparameter C. To get the optimal parameters and results. A total of 20 people were measured in this experiment. the experimental results obtained by SVM model were positive: the recognition accuracy of skin electrical signal was 0.8422, the recognition accuracy of heart rate signal was 0.8345, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.8832. Negative: the recognition accuracy of skin electrical signal is 0.9812, the recognition accuracy of heart rate signal is 0.9385, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion is 0.9902. Neutral: the recognition accuracy of skin electrical signal was 0.6403, the recognition accuracy of heart rate signal was 0.5308, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.5438 © 2023 ACM.

关键词： Emotion recognition

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Augmentation of Human Activity data: Convert, Generate, Transform 27th

Augmentation of Human Activity Data: Convert, Generate, Tra...

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27th international conference on pattern recognition, ICPR 2024

作者： Nair, Nilah Ravi Matei, Arthur Krön, Dennis Moya Rueda, Fernando Reining, Christopher Fink, Gernot A. Chair of Material Handling and Warehousing TU Dortmund University Dortmund Germany Department of Computer Science TU Dortmund University Dortmund Germany MotionMiners GmBH Dortmund Germany

ISBN: (纸本)9783031781919

data-driven neural network models trained on human motion data facilitate human activity recognition and identity verification applications. However, large annotated and processed human motion datasets are scarce, leading to the overfitting of models to training data. thus, it is important to investigate data augmentation techniques to generate additional data to facilitate model generalisation. In addition, the choice of augmentation techniques severely impacts the performance of self-supervised learning. thus, this work experiments and evaluates various augmentation techniques on seven sensor-based human activity datasets. three supervised neural network models and one self-supervised learning model were experimented with. We note that due to the high variability in the performance of algorithmic augmentation techniques on time-series human activity datasets, generative data is highly influential in this domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Software Cost Estimation Using Temporal data mining Techniques: An over View 6

Software Cost Estimation Using Temporal Data Mining Techniqu...

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

作者： Antil, Jyoti Rishi, Rahul Sharma, Sonia Nandal, Ajeta Maharshi Dayanand University Department of UIET CSE Haryana Rohtak India C.R. Polytechnic Computer Engineering Haryana Rohtak India

ISBN: (纸本)9798350304480

data mining is a process that involves extracting new, non-trivial information from huge datasets. this information can be used in a variety of applications. Several fields, including temporal pattern recognition, statistics, optimisation, temporal databases, high-performance computing, visualisation, and parallel computing, all meet in the rapidly developing field of temporal data mining. the growth of digital data and the processing power of computers have severely limited a field called data mining that integrates computer science and statistics. the software cost estimation category is one of data mining's disciplines. this study's primary objective is to comprehend the temporal mining methods currently in use in order to spot the gaps and flaws in the most recent software cost estimation technologies. this research also includes a concise summary of recent developments in the field of temporal data mining that has taken place recently. this study serves as a springboard for a discussion of the reasons why temporal data mining was required in the industry, which played a significant part in the development of the current generation of temporal data mining techniques for software cost assessment. © 2023 IEEE.

关键词： data mining Software cost estimation Temporal data mining Techniques Temporal Rules

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Unveiling Early Autism Spectrum Disorder Detection: A Comprehensive Study of machine learning and Deep learning Approaches 6

Unveiling Early Autism Spectrum Disorder Detection: A Compre...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Tarai, Trilochan Parhi, Manoranjan Mishra, Debahuti Shaw, Kailash University Department of Computer Science and Engineering Odisha Bhubaneswar India University Centre of Data Science Department of Computer Science and Engineering Odisha Bhubaneswar India Department of Aiml Pune India

ISBN: (纸本)9798350324204

According to research by the world health organization (WHO), approximately 0.63% of children are diagnosed with autism spectrum disorder (ASD). ASD commonly emerges during childhood and persists through adolescence and adulthood. Early detection of ASD is crucial as it can lead to effective interventions and improved outcomes. machine learning and deep learning techniques have gained significant recognition for their efficacy in various fields including biomedicine and medicine. these models offer valuable tools for data interpretation in clinical diagnostics and decision-making processes. Consequently, researchers emphasize the utility of both machine learning and deep learning methods for disease screening. this study aims to leverage both machine learning and deep learning techniques to classify ASD cases using a dataset sourced from the UCI repository. the dataset encompasses pediatric cases. the classification task employed k-nearest neighbors' algorithm (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Among these, CNN outperformed SVM and k-NN, achieving a remarkable accuracy of 99.71%. © 2023 IEEE.

关键词： Support vector machines

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Efficient Detection of Fake Twitter Profiles on Social Media through Hybrid Deep learning Model 6th

Efficient Detection of Fake Twitter Profiles on Social Media...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Bibi, Haleema Ashraf, Humaira Jhanjhi, N.Z. Department of Computer Science and Information Technology International Islamic University Islamabad Pakistan School of Computer Science SCS Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

Social media is an interactive and reliable tool used for communication among people around the world. It provides a facility for people to make accounts of it and to share their personal ideas, photos, interests, and other expressions. through social media, people can easily interact with each other. Everyone is in contact with their loved ones. In social media mostly used applications are Facebook, Instagram, WhatsApp, Twitter, LinkedIn, and YouTube. Due to the ever-increasing popularity of social media, it has become an ideal platform for the creation of fake accounts by spammers for undesirable activities. Spammers make fake accounts on social media to create panic among people. In this research, we aimed to detect fake profiles in one of the most used content-sharing applications Twitter. We have used the MIB Twitter dataset. Based on user-based attributes we have implemented deep learning techniques LSTM and GRU, and we proposed a hybrid LSTM-GRU. through experimental results, we gain better accuracies and small losses with deep learning techniques than machine learning algorithms for efficient detection of fake Twitter profiles. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Distributed Self-fault Outlier Detection in Wireless Sensor Network Based on machine learning 6th

Distributed Self-fault Outlier Detection in Wireless Sensor ...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Padhi, Ruchika Mudulli, Debendra Gouda, Bhabani Sankar C. V. Raman Global University Odisha Bhubaneswar India School of Computer Science NIST Institute of Science and Technology Odisha Brahmapur India

ISBN: (纸本)9789819780891

Due to a wide range of essential applications, resource limitations, and intermittent irregular responses from cluster sensor nodes, different cluster sensor node faults and failures of distributed wireless sensor networks have occurred in recent years. Due to limited calculation capabilities, limited storage space, resource constraints, and a poor wireless communication environment, relatively few studies have been done on distributed fault diagnosis and fault tolerance in wireless sensor networks, making these networks vulnerable to impending security issues and abnormal detection. this chapter presents a thorough investigation into data quality, with a focus on the features of sensor node information with respect to time and space similarity, as well as recent advances in the study of outlier analysis. In this study, we employ classification to identify outliers by predicting the values of faulty sensor nodes using information gathered from their fault-free neighbors. the proposed method, DSFOD, uses the KNN algorithm to diagnose the proximity of a distance and achieves promising results of greater than 0.99. To accomplish the goal of data outlier detection in WSN, the data are analyzed and detected using the DSFOD method. the simulation’s findings demonstrate the network’s efficiency and superior performance at that specific time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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