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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3327 条记录，以下是1081-1090 订阅

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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

Multiple Manifold Regularization Based on Non-Negative Matri...

引用

Disruptive Technologies (ICDT), international conference on

作者： Mutyala Sirisha Ghufran Ahmad Khan Jalaluddin Khan Hemlata Pant Kaneez Zainab Zulfikar Ali Ansari Shamshul Haq Ansari Department of Computer and Applications Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow Uttar Pradesh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.

关键词： Manifolds Encyclopedias machine learning Linear programming Internet Performance analysis Online services Iterative methods Matrix decomposition Optimization

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Simsiam Network Based Self-supervised Model for Sign Language recognition 3rd

Simsiam Network Based Self-supervised Model for Sign Langua...

引用

3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Kothadiya, Deep R. Bhatt, Chintan M. Rida, Imad Changa India Department of Computer Science and Engineering School of Engineering and Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Laboratoire Biomécanique et Bioingénierie UMR 7338 Centre de Recherches de Royallieu Université de Technologie de Compiègne Compiègne France

ISBN: (纸本)9783031463372

To make a more accurate and robust deep learning model, more labeled data is required. Unfortunately, in many areas, it’s very difficult to manage properly labeled data. Sign language recognition is one of the challenging areas of computer vision, to make a successful deep learning model to recognize sign gestures in real-time, a huge amount of labeled data is needed. Authors have proposed a self-supervised learning approach to address this problem. The proposed architecture used Resnet50 v1 backbone-based simsiam encoder network to learn the similarity between two different images of the same class. Calculated cosine similarity passes to MLP head for further classification. The proposed study uses Indian and American Sign Language detests for simulation. The proposed methodology successfully achieve 74.59% of accuracy. Authors have also demonstrated the impact of other self-supervised deep learning models for sign language recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Research on Objectives recognition of Robotics based on Convolutional Neural Network

Research on Objectives Recognition of Robotics based on Conv...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Guanzhu Ren Yichen Zhao Zhengfeng Wu Pengwei Li Kaiyu Mi College of Wollongong Engineering Taiyuan University of Technology Taiyuan China College of Computer Science and Technology (Big Data College) Taiyuan University of Technology Jinzhong China College of Mechanical and Transportation Engineering Taiyuan University of Technology Taiyuan China

Recently, robotics has widely utilized in tremendous applications including traffic transportation, medical treatments, power system and virtual environment. These applications are almost based on the vision system, which is consisted by detection sensors and cameras. However, existing recognition methods are concentrated on the precision of machine learnings under the ideal setups and ignored the complex environment for robotics. In this paper, we simulate the realistic robotics sensors with non-complete input images and propose a novel convolutional network without redundancy neural layers. Indeed, an attention mechanism is established to enhance the recognition accuracy of proposed framework. Finally, we evaluate our model with existing objectives identification learning methods in the identical testing configurations. From our extensive experiment results and analysis, we can conclude that the proposed model can achieve the objectives recognition in complex robotics scenario with acceptable recognition accuracy and reasonable computation costs.

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Deep learning-based Functional Prediction Model for Genome Sequence

Deep Learning-based Functional Prediction Model for Genome S...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Bo Lan United World College of South East Asia East Singapore Singapore

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This study proposes a deep learning-based genome sequence function prediction model and systematically evaluates its performance. The study begins with an overview of the importance of genome sequence function prediction and its applications in biomedicine, followed by a detailed description of the main current research methods and advances. We use Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Transformer model for capturing local features, sequence dependencies and long-range dependencies, respectively. Through the combined application of multiple feature extraction methods, the model demonstrates excellent prediction performance on different genomic datasets. The experimental results show that the deep learning model can effectively improve the accuracy of genomic sequence function prediction, especially in complex feature extraction and pattern recognition. Convolutional neural networks have obvious advantages in local feature extraction, recurrent neural networks excel in dealing with the time dependence of sequence data, and the Transformer model demonstrates its superiority in dealing with long-distance dependencies and parallel computation.

关键词： Deep learning Recurrent neural networks Computational modeling Biological system modeling Genomics Predictive models Feature extraction Transformers Convolutional neural networks Bioinformatics

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Employee Attrition Prediction using machine learning

Employee Attrition Prediction using Machine Learning

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Sanidhya Barara Umang Soni Modern School New Delhi India Netaji Subhash University of Technology New Delhi India

data mining plays an important role in the internal Human Resource management processes of any firm [1]. These processes include prevention and prediction of employee attrition. This paper presents a machine learning based approach to attrition prediction for individual employees, by training different machine learning models on attrition data. In this paper, machine learning models like the logistic regression model, the linear regression model, the decision tree model, the random forest model, K nearest neighbors model, radius nearest neighbors model, the naïve bayes classifier model and Bayesian ridge model are trained on data obtained from Kaggle, an online community platform for data scientists and machine learning enthusiasts. The resulting models then predict whether an employee with particular attributes will attrit, and if so, within how many years they can be expected to do so. Prior to training the models, the data is cleaned (outlier removal, feature removal), scaled, and categorical variables are converted to numerical ones. Then, predictions are carried out and feature importances are found using random forest and decision tree models. The utilities of these models are then compared against each other based on accuracy, precision, recall, f-beta score, kappa score and three self made metrics. The results show that, surprisingly, the nearest neighbor models outperformed all others by a large margin (possibly due to data scaling being a part of preprocessing), and the logistic regression model was unable to predict attrition very satisfactorily. The results showed that satisfaction_level, average_monthly_hours and last_evaluation_rating are the most important features when predicting attrition or years. The research also shows that it is viable to use traditional ML models to predict the time in which an employee will attrit, and using the methodology defined in this research on a real dataset will provide useful information to the corporation applying i

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A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition Software engineering

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An IoT and data mining-Based Tool for Early Identification of Speech Disorders in Children Using Advanced Algorithms 1

引用

3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Usha, M. Department of Information Technology KG College of Arts and Science College Tamil Nadu Coimbatore India

ISBN: (数字)9783031862908

ISBN: (纸本)9783031862892

Early detection of speech disorders in children is critical for effective intervention and treatment. This paper presents an innovative tool leveraging the Internet of Things (IoT) and advanced data mining techniques to identify speech disorders in children at an early stage. This paper explores the integration of IoT and data mining technologies for the early detection of speech disorders in children by applying advanced algorithms. The aim is to develop an efficient tool for early diagnosis and intervention, enhancing speech therapy outcomes through real-time monitoring and analysis. The system integrates IoT-enabled devices for real-time data collection, capturing various speech parameters such as pitch, tone, and articulation. These data are processed using advanced algorithms, including machine learning and deep learning techniques, to analyze patterns and anomalies indicative of speech disorders. The proposed paper offers a user-friendly interface for parents, educators, and healthcare professionals, providing insights into a child′s speech development and highlighting potential areas of concern. The system′s effectiveness is evaluated through extensive testing with a diverse dataset, demonstrating its accuracy and reliability in identifying various speech disorders, such as stuttering, dysarthria, and apraxia. By facilitating early diagnosis, this tool aims to improve the quality of life for children with speech disorders through timely and targeted interventions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Research on Sales Forecasting Model Based on GRU Neural Network and machine learning Model

Research on Sales Forecasting Model Based on GRU Neural Netw...

引用

data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Chunchang Zhang Ruoxuan Shi Harbin Institute of Technology Weihai Weihai China Lanzhou University Lanzhou China

In the age of big data, the retail industry generates a vast amount of sales data daily. Analyzing and mining these data can facilitate management decision-making and provide great benefits to enterprises. With competition intensifying in the retail industry, precise sales forecasts are essential to assist companies in effectively managing their inventory of specific commodities, developing marketing plans, and formulating timely and effective strategic policies to reduce costs and increase efficiency. In this paper, the STL method is combined with SVR and GRU to build a daily sales forecasting model for retail enterprises. The trend, seasonal, and residual sub-series decomposed from past sales are used to directly predict future sales to achieve a better forecasting effect, and are compared with some traditional models and STL-ML combined models based on the decomposition integration idea. The results show that the method used in this paper has higher forecasting accuracy, and the forecasting ideas proposed in this paper have important reference significance for the sales forecasting practice of retail enterprises.

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Combination of Local Features and Deep learning to Historical Manuscripts Dating 3rd

Combination of Local Features and Deep Learning to Historica...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Boudraa, Merouane Bennour, Akram LAMIS Laboratory Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (纸本)9783031463341

While paleographers face various challenges in the dating process of historical manuscripts, computer scientists encounter multiple obstacles in automating them. To address this problem, machine learning and deep learning techniques which have proven effective in other domains have been used. This study introduces a system that integrates two primary methods of feature extraction - deep learning and hand-crafted features. The Harris-detector was utilized to extract key-points from the manuscripts, and the K-means algorithm was applied to cluster them. From these clusters, patches of size nxn were extracted. Then, a densnet model was trained on these patches using transfer learning, finally we used majority voting on document patches to determine the date based on document level. The effectiveness of this approach was evaluated on two datasets, MPS and CLaMM, and achieved a CS of 96% and 65% and an MAE of 4.01 years and 20.1 years respectively for both datasets. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Effective heart disease prediction using hybridmachine learning 3

Effective heart disease prediction using hybridmachine learn...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pandiaraj, A. Prakash, S. Lakshmana Kanna, P. Rajesh Bannari Amman Institute of Technology Department of ISE Sathyamangalam Erode India Bannari Amman Institute of Technology Department of IT Sathyamangalam Erode India

ISBN: (纸本)9780738111834

machine learning based heart disease prediction is presented in this research work. Heart related diseases increases everyday throughout the world due to stress, high blood pressure, family backgrounds, etc., Early prediction helps to prevent serious issues due to heart diseases. Classification approaches are widely used to analyse medical data to prevent major issues. Real time large medical data are evaluated through data mining applications to control, contradict and manage large data and make use of it to predict the risk factors. This research work is also aims to diagnose heart disease through support vector machine (SVM) and genetic algorithm (GA) as a data analytic approach. Experimental results demonstrate the superior performance of proposed approach in predicting heart disease. © 2021 IEEE.

关键词： Genetic algorithms

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