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Recent advances in deep learning based computer vision 3

Recent advances in deep learning based computer vision

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3rd international conference on computers, Information Processing and Advanced Education, CIPAE 2022

作者： Ma, Dongjian Leicester International Institute Dalian University of Technology Panjin China

ISBN: (纸本)9781665468121

Deep learning is a hot topic in current AI research and significant progress has been made in a variety of fields, including computer vision and natural language processing. Traditional architecture of deep learning normally based on convolutional neural networks. In recent years, impressive results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Thus, neural networks with Transformer architecture have been introduced to computer vision tasks and even challenge mainstream of traditional convolutional neural network. In this paper, it will review recent improvements in computer vision based on deep learning method. It'll go over some of the most important research in deep learning models. After that, it focus on advances in four key computer vision tasks: picture classification, object identification, semantic segmentation, and human posture estimation. It'll go through the most recent developments in these fields. Finally, it will provide a conclution of recent computer vision advances and discuss future possibility. © 2022 IEEE.

关键词： Object detection

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Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms 3

Predictive maintenance on sensorized stamping presses by tim...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Coelho, Daniel Costa, Diogo Rocha, Eugenio M. Almeida, Duarte Santos, Jose P. Univ Aveiro Dept Mech Engn P-3810193 Aveiro Portugal Univ Aveiro Dept Math P-3810193 Aveiro Portugal Ctr Res & Dev Math & Applicat CIDMA P-3810193 Aveiro Portugal Bosch Termotecnol SA P-3810193 Lisbon Portugal

Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection;and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch ThermoTechnology, is discussed. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Predictive Maintenance Anomaly Detection machine learning Time Segmentation

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Deep learning-based Temporal Action Localization Method 3

Deep Learning-based Temporal Action Localization Method

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3rd international conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Wu, Weimei Hu, Jianhua He, Yongjun Zeng, Wenying Wang, Maoxing Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China

ISBN: (纸本)9798350359961

Temporal action localization (TAL) plays a crucial role in video understanding, aiming to determine the class of each action instance and pinpoint its starting and ending frames in untrimmed videos. This paper introduces an anchor-free TAL approach using a U-shaped network. Through backbone and feature pyramid networks, the input video is transformed into a 1D feature sequence encapsulating the video's spatial-Temporal information. By leveraging the U-shaped network-based feature pyramid, spatial dimensions are merged across various temporal convolutional layers, and temporal dimensions are aggregated. Preliminary proposal sequences, which include a basic regressor and classifier, are generated. For each of these proposals, predicted temporal regions are extracted and key boundary features are acquired using a boundary pooling network. These boundary features, in conjunction with the feature pyramid, serve to output refined temporal regression and action classification predictions. Our approach, devoid of predefined anchors, reduces the output volume and complexities of hyperparameter tuning. Experiments on the THUMOSl4 dataset showcase the evident advantages and commendable performance of our anchor-free TAL method. © 2023 IEEE.

关键词： component deep learning temporal action localization U-shaped network video understanding

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A Survey on Image Processing and machine learning Techniques for Detection of Pulmonary Diseases Based on CT Images 3rd

A Survey on Image Processing and Machine Learning Techniques...

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3rd international conference on machine Intelligence and Signal Processing, MISP 2021

作者： Sawant, Priya Sreemathy, R. SCTR’s Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789811908392

In spite of advancements in medical technology, lung diseases continue to cause death and substantial morbidity worldwide as these diseases directly affect the respiratory systems and many a times create a layer of lesions on the lungs. A wide gamut of organisms is known to cause various pulmonary infections and newer ones are also emerging. computer-aided diagnosis (CAD) empowered with image processing and machine learning techniques is an important diagnostic tool for early diagnosis of a disease. These techniques have exhibited a high degree of accuracy. The primary objective of this paper is to present a comprehensive survey of the numerous works carried out to detect lung diseases from CT images using machine learning and deep learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Research on the application of Artificial Intelligence Technology in UAV Trajectory Prediction Algorithm 3

Research on the Application of Artificial Intelligence Techn...

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3rd international conference on Networking Systems of AI, INSAI 2023

作者： Liu, Shaogang Zhang, Lingyan Qin, Chaowu West Yunnan University School of Artificial Intelligence & Information Engineering Lincang China

ISBN: (纸本)9798350344660

This study is based on artificial intelligence technologies such as machine learning algorithms and data visualization analysis, and is committed to designing and implementing a UAV trajectory prediction model. This model uses the random forest algorithm to perform UAV trajectory regression and prediction, and uses data visualization technology to extract features and reduce dimensionality of the data. First, the existing relevant literature is reviewed, and the research status and development trends of UAV trajectory prediction are sorted out. Subsequently, a large amount of UAV transaction data from 2023 was collected and cleaned, and analyzed, preprocessed and feature extracted for this data. On this basis, the random forest algorithm was used to perform regression and prediction of UAV trajectorys. This study conducted an in-depth analysis of the accuracy and error of the model, and summarized the advantages, disadvantages and future development directions of the model. © 2023 IEEE.

关键词： Prediction models

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Gene Expression Analysis using Particle Swarm Optimization and machine learning Algorithms for Diagnosing Liver & Breast Cancer 3

Gene Expression Analysis using Particle Swarm Optimization a...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Lohith, R.D. Chetty, Rudra Narayan Shaan, Maheep Singh Naik, Nupur Rajan Bharanidharan, N. Dayananda Sagar University Computer Science and Engineering Bangalore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Cancer is characterized by tremendous increase in the number of cells which can affect the organs of body. Thanks to the rapid technological advances of recent years, it is now possible to analyze the cellular structure of different types of cancer in a short period of time. DNA microarrays printed microscope slides have thousands of tiny dots in the specified location, each containing a sequence of DNA or a gene. The objective of this paper is to use Particle Swarm Optimization as a transform for increasing the machine learning algorithms such as Quadratic Discriminant Analysis, K Nearest Neighbor Classifier, Random Forest Classifier. Gene expression data related to 358 Liver cancer and 290 Breast cancer subjects are considered in this analysis. Principal Component Analysis used for reducing the dimensionality of data. Notably, Quadratic Discriminant Analysis when used with Particle Swarm Optimization provides the highest Balanced Accuracy score of 96 % for liver cancer data while K Nearest Neighbor classifier along with Particle Swarm Optimization provides the highest Balanced Accuracy score of 91 % for breast cancer data. © 2022 IEEE.

关键词： Principal component analysis

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An Efficient Feature Extraction Technique and Novel Normalization Method to Improve EMG Signal Classification 3

An Efficient Feature Extraction Technique and Novel Normaliz...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Jain, Reema Garg, Vijay Kumar Lovely Professional University Department of Computer Application Punjab Phagwara India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Electromyography (EMG) is one of the medical diagnostic techniques that is widely used to investigate muscular health status. Medical and paramedical professional's record EMG signals as myoelectric signals that need some processing to improve their apt analysis and interpretation. In the present paper, the authors have integrated feature extraction and normalization step to improve the overall EMG signal classification. The process involves 12 feature extraction matrices that are used for the normalization of the recorded signals. The processed signals are fed to a binary classifier, Support Vector machine (SVM) for categorization into pain and normal classes. Performance analysis is performed using 100 simulation rounds and the observations are summarized in terms of precision, sensitivity, specificity, and accuracy of classification of EMG signals. The simulation analysis demonstrated that with the integration of feature extraction and normalization step, an improvement of 3% is observed in the EMG signal classification accuracy. © 2022 IEEE.

关键词： Support vector machines

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Comparative Analysis of machine learning and Ensemble learning Classifiers for Parkinson's Disease Detection 3

Comparative Analysis of Machine Learning and Ensemble Learni...

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3rd international conference on Computing, Analytics and Networks, ICAN 2022

作者： Goyal, Palak Rani, Rinkle Singh, Karamjeet Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9781665499446

A neurological illness called Parkinson's disease (PD) commonly appears between the ages of 55 and 65. Moreover, a patient's entire quality of life is significantly impacted by the progressive development of motor as well as non-motor symptoms due to this disease. There is no known cure for PD, although a number of therapies have been created to assist control its symptoms. Therefore, the management of PD is a field that is expanding, and there is a need to develop a comprehensive framework for the timely detection and classification of PD. In this paper, we developed six machine learning-based and five ensemble learning-based classification models to forecast PD. The six base classifiers which are used in the current study are Support Vector machine (SVM), Decision Trees (DTs), Random Forest (RF), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB);and five ensemble classifiers named XGBoost, Gradient Boost, Bagging, CatBoost and Light Gradient Boosted machine (LGBM) respectively, are then carefully compared. To improve the performance of the classifiers and to reduce the problem of overfitting, a feature selection method named Principal Component Analysis (PCA) and various preprocessing techniques are applied. Further, this study uses the voice samples dataset from the UCI repository having 188 PD and 64 normal patients. Overall, our findings revealed that, when compared to the other five base classifiers, the RF model offered the best classification performance with an accuracy of 82.37%, and the ensemble classifier named LGBM shows best results when compared with base as well as ensemble classifiers having an accuracy of 85.90%. © 2022 IEEE.

关键词： Decision trees

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Human Resource Management Using machine learning-Based Solutions 3

Human Resource Management Using Machine Learning-Based Solut...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Kumar, M. Rudra Sharma, Avinash Bhargavi, Y. Krishna Ramesh, G. Gpcet Dept. of Cse Kurnool India Mmdu Maharishi Markandeshwar Engineering College Dept. of Computer Science & Engg Haryana Ambala133207 India Griet Dept. of Cse Hyderabad India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Human resource management (HRM) is more like a strategic alignment with the purpose. AI (Artificial Intelligence) based techniques are becoming increasingly significant in HRM operations. The firms are concentrating on more realistic solutions. ML (machine learning) algorithms are currently making advances in a range of human resource management operations. This report gives an overview of critical HRM operational functions where AI-based solutions in particular ML techniques may be applied to improve accuracy of HRM operational processes. This paper addresses unique possible and probable opportunity of AI techniques, and focuses on features noted as (i) engaging employees, (ii) corporate culture administration, (iii) and the evaluation. The employment of ML techniques decision tree as well as logistic regression to enhance the possibility of solutions being more substantial as well as capable of offering the ideal evaluation process. If solutions are created along these lines, they can be advantageous to firms in terms of managing strategic HRM operational practices. © 2022 IEEE.

关键词： Fuzzy logic

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A Survey of Offline Handwritten Signature Verification Based on Deep learning 3

A Survey of Offline Handwritten Signature Verification Based...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Muhtar, Yusnur Kang, Wenxiong Rexit, Aliya Mahpirat Ubul, Kurban Xinjiang University Information Science and Engineering Institute Xinjiang China South China University of Technology School of Automation Science and Engineering Guangzhou510641 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Handwritten signatures are biometrics and a point of contention in the scientific community, the process used to verify whether a person's signature is genuine. In the past ten years, the application of handwritten signature technology in the fields of administration, finance, handling legal disputes, and security has been greatly developed, and many researchers have focused on applying systems based on handwritten signature analysis and processing to new fields on the possibility. After several years of disorderly development in this field of research, it is time to assess the applicability of its current developments to formulate a structured path forward. In this paper, we provide a systematic review of the literature on offline handwritten signatures over the past 10 years, focusing on the most prominent and promising deep learning-based signature verification methods, and attempt to elicit possible future research directions on this topic. © 2022 IEEE.

关键词： Deep learning

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