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Automatically mining relevant variable interactions via sparse Bayesian learning 25

Automatically mining relevant variable interactions via spar...

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25th international conference on pattern recognition (ICPR)

作者： Yafune, Ryoichiro Sakuma, Daisuke Takayanagi, Mirai Tabei, Yasuo Saito, Noritaka Saigo, Hiroto Kyushu Univ Fukuoka Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9781728188089

With the rapid increase in the availability of large amount of data, prediction is becoming increasingly popular, and has widespread through our daily life. However, powerful nonlinear prediction methods such as deep learning and SVM suffer from interpretability problem, making it hard to use in domains where the reason for decision making is required. In this paper, we develop an interpretable non-linear model called itemset Sparse Bayes (iSB), which builds a Bayesian probabilistic model, while simultaneously considering variable interactions. In order to suppress the resulting large number of variables, sparsity is imposed on regression weights by a sparsity inducing prior. As a subroutine to search for variable interactions, itemset enumeration algorithm is employed with a novel bounding condition. In computational experiments using real-world dataset, the proposed method performed better than decision tree by 10% in terms of r(2). We also demonstrated the advantage of our method in Bayesian optimization setting, in which the proposed approach could successfully find the maximum of an unknown function while maintaining transparency. Apart from Bayesian optimization with Gaussian process, iSB gives us a clue to understand which variables interactions are important in optimizing an unknown function.

关键词： Decision trees

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A Practical Approach to detect Indoor and Outdoor Scene recognition 4

A Practical Approach to detect Indoor and Outdoor Scene Reco...

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4th international conference on Information Management and machine Intelligence, ICIMMI 2022

作者： Sharma, Vaishali Nagpal, Nitesh Shandilya, Ankit Dureja, Aman Dureja, Ajay Fet Pdm University India Department of Information Technology Bhagwan Parshuram Institute of Technology Rohini India Department of Information Technology Bharati Vidyapeeth's College of Engineering A-4 Paschim Vihar New Delhi India

ISBN: (纸本)9781450399937

In computer vision recognition of scenes is a long-time research problem. Scene can be defined as the real-time environment view which consists of a lot of views (like road, tree, building, parks, etc.) in a meaningful manner. the problem of scene recognition can be explained as assessment of labels such as "road", "building", "hall", "bedroom"or in an extra simplified way "Indoor scene"and "Outdoor scenes is classified on an input image based on the object or environment of the image. A huge amount of data is created and available every second in this growing era of digital data. Scene recognition is still a rising area that did not attain much success as compared to image recognition due to the vast variability of features in the scenic environment. Because of this reason, there is not a lot of work being completed these days in this area. this project focuses on the evaluation of problem statements using all the literature surveys completed lately and offering solutions for that problem statement. For resolving the proposed problem declaration ResNet and VGG variants are used ResNet18, 50 and 152 & VGG16, 19, and VGG19 with batch normalization are implemented. the entire scene recognition procedure is discussed in this report and the main motive is to form a foundation that can be further continued in proposing a new algorithm. Before deep learning the design and implementation of the scene recognition model depended on the low dimensional portrayal of the scene. the utilization of deep learning especially CNN for scene recognition has gotten extraordinary attention from the computer vision community. © 2022 ACM.

关键词： Image recognition

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CNN-BiGRU-Attention: A Time Series-Based Traffic Flow Prediction Model

CNN-BiGRU-Attention: A Time Series-Based Traffic Flow Predic...

引用

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

作者： Shaorong Li Yao Chen Jianhong Hu Yaokun Li Yunnan Transportation Science Research Institute Co. Ltd Kunming China School of Software Yunnan University Kunming China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

At present, the task of traffic flow forecasting poses significant challenges. Existing studies predominantly utilize spatial characteristics for comprehensive forecasting and do not extensively consider the issue of extracting features from individual time series data. In the absence of known road topological structures, extracting effective temporal features of a single node becomes especially important. this paper proposes a new time series-based traffic prediction model-CNN-BiGRU-Attention(CBGA), which combines Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and an attention mechanism. the model captures local features through CNN, captures long-term temporal dependencies through BiGRU, and introduces an attention mechanism to focus on key time steps, thereby effectively improving the forecasting performance. We conducted experiments using this model and compared it with traditional and modern models to demonstrate that the CBGA model has good performance in time series forecasting tasks and can effectively perform time series-based traffic flow forecasting tasks.

关键词： Attention mechanisms Roads Time series analysis Predictive models Feature extraction Convolutional neural networks data mining Forecasting Periodic structures Meteorology

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Dhruvi Srishwal Shibu PS Harsha Vardan Sajjad Ibrahim. A Gouthaman. P Department Of Networking And Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department Of Networking And Communications Faculty of Engineering and Technology and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. this paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. the proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions.

关键词： Heart Accuracy databases Arrhythmia Electrocardiography Ubiquitous computing Vectors Real-time systems pattern recognition Monitoring

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Motivating students by visualizing the learning process in college english classes 4

Motivating students by visualizing the learning process in c...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Li, Ming Zhengzhou Sias University Zhengzhou Kansas International School China

ISBN: (纸本)9781665413220

this study examined how the AI platform influenced students' motivation in college English classes. the participants were junior English majors in a university in central China. the students belonged to two groups, one for the control and the other for the treatment (N=210). the key strategies mainly included the visualization of the class activities, such as class attendance, group summary of the given passage, peer-instruction and peer assessment. Independent samples t-tests were used for data analysis. the results showed that classes incorporating visualizing strategies on the AI platform demonstrated more interest, sense of success and sense of power in their learning process. these results were significant for the instructional design in college English if the professors want to motivate their students in their learning. Implications and limitations were discussed. © 2021 IEEE.

关键词： Students

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Research on Model of Seismic Anomaly data mining Based on Neural Network 4

Research on Model of Seismic Anomaly Data Mining Based on Ne...

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4th IEEE international conference on Information Systems and Computer Aided Education, ICISCAE 2021

作者： Long, Yancheng Rong, Jian South West Forestry University Kunming China

ISBN: (纸本)9781665441247

data mining in the seismic anomaly database will be affected by the instability of the seismic monitoring system signal and the environment, so in the development of practice should be based on the existing technology to comprehensively explore, pay attention to gradually break through the limitations of traditional mining methods, in order to effectively solve the problems existing in the previous data mining. Under the background of new era, the neural network as a machine learning algorithm is the most common way of mining, need according to the related theory had a clear standard equation of the minimum mean square error values, thus to build optimized mining model, and then using the calculation data of database, the feature vector to construct the corresponding to the monitoring data are accurate judgment. On the basis of understanding the current development of seismic monitoring technology, this paper proposes a new optimization model based on the constructed seismic anomaly database, and verifies its application effect in practice. © 2021 IEEE.

关键词： data mining

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An Optimized Transfer Attack Framework Towards Multi-Modal machine learning 4

An Optimized Transfer Attack Framework Towards Multi-Modal M...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Zhang, Yinjie Xiao, Geyang Bai, Bin Wang, Zhiyu Sun, Caijun Tu, Yonggang Intelligent Network Research Institute Zhejiang Laboratory Hangzhou China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep neural networks (DNNs) have excelled at a wide range of tasks, including computer vision (CV), natural language processing (NLP), and speech recognition. However, past research has demonstrated that DNNs are vulnerable to adversarial examples, which are deliberately meant to trick models into making incorrect predictions by adding subtle perturbations into inputs. Adversarial examples create an exponential threat to multi-modal models that can accept a variety of inputs. By attacking substitute models, we provide a transferable attack framework. the suggested framework optimizes the attack process by modifying the prompt templates and simultaneously raising the attack on multiple inputs. Our experiments demonstrate that the proposed attack framework can significantly improve the success rate of transferable attacks, and adversarial examples are rarely noticed by humans. Meanwhile, experiments show that in transferable attacks, coarse-grained adversarial examples can achieve higher attack success rates than fine-grained ones, and the multi-modal models has some robustness against uni-modal attacks. © 2022 IEEE.

关键词： Deep neural networks

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Speaker Identification Using Deep learning Models for Enhanced Voice Biometrics

Speaker Identification Using Deep Learning Models for Enhanc...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Akhilesh Mamatha Balipa Anmol S Shetty Department of MCA Nitte Deemed to be University NMAM Institute of Technology Nitte

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the goal of this project is to create a sophisticated voice recognition system that accurately recognizes and categorizes spoken data by applying machine learning and audio processing techniques. It uses a deep learning model (model.h5) for high-accuracy audio input categorization and incorporates tools like Librosa for comprehensive audio feature extraction (such as Mel-frequency cepstral coefficients, MFCCs, and spectral contrast). data preprocessing, which includes gathering unprocessed audio data and using methods like feature extraction, noise reduction, and conversion to machine-readable forms, is the first step in the implementation process. through feature extraction, important aspects of the voice are recorded, allowing the system to recognize distinct voice signatures. A Python-based framework (***) that provides both batch and real-time processing options is used to deploy the trained model. the complete method, including model training, validation, and assessment metrics, is documented in detail in a companion Jupyter Notebook. Because predictions are produced in an organized JSON format, integrating them into bigger systems is simple. Speaker verification, tailored user experiences, and biometric authentication are some of the system's primary uses. It may be scaled to accommodate both enterprise and consumer-level service requirements. the study demonstrates how artificial intelligence may revolutionize voice-based identification and presents opportunities for additional enhancements, including support for other languages, environmental adaptability, and integration with IoT devices.

关键词： Deep learning Training Accuracy Biological system modeling Speech recognition Predictive models Feature extraction data models Speaker recognition Security

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Performance of Domain Adaptation Schemes in Video Action recognition using Synthetic data 2022

Performance of Domain Adaptation Schemes in Video Action Rec...

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4th international conference on Image, Video and Signal Processing, IVSP 2022

作者： Isoi, Hana Takefusa, Atsuko Nakada, Hidemoto Oguchi, Masato Ochanomizu University Japan National Institute of Informatics Japan National Institute of Advanced Industrial Science and Technology Japan

ISBN: (纸本)9781450387415

the videos obtained from surveillance cameras are expected to be processed in neural networks and utilized for indoor monitoring and surveillance services. However, collecting training data is generally an issue in machine learning, obtaining sufficient training data is very difficult from the viewpoint of privacy protection, especially for the abovementioned services. To address this issue, synthetic data instead of real data might be employed for training. However, the domain shift of video has not been sufficiently investigated, and therefore high-accuracy, unsupervised learning of real video using synthetic video has not been achieved. In this study, we create a synthetic video dataset for action classification and show that action classification can be performed with high accuracy without labeling real video data by learning with this synthetic dataset. First, we create an OchaHouse dataset for action classification for indoor monitoring and surveillance. this dataset consists of a real video dataset OchaHouse-Real that records the behavior of one person in a room and a synthetic video dataset OchaHouse-Syn that resembles it. Second, video action recognition of real videos is performed using synthetic data and various unsupervised learning methods with domain adaptation schemes. the results show that learning with synthetic data enables highly accurate action classification of real data without a real data label. the dataset will be published. © 2022 Association for Computing machinery.

关键词： Classification (of information)

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A Novel Model for Recognising Handwritten Devanagari Numerals using machine learning

A Novel Model for Recognising Handwritten Devanagari Numeral...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Ch. Prathima Ramprakash Reddy Arava K Sevitha G Manikanth D Vinay C Surya School of Computing Mohan Babu University (formerly Sree Vidyanikethan Engineering College) Tirupati India Department of CSE KSRM College of Engineering Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Support vector machines Training Handwriting recognition Analytical models machine learning algorithms Image recognition Nearest neighbor methods

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