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Enhanced Manufacturing Execution System "MES" Through a Smart Vision System 10th

Enhanced Manufacturing Execution System "MES" Through a Smar...

引用

10th international Joint conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2020

作者： El Zant, Chawki Charrier, Quentin Benfriha, Khaled Le Men, Patrick Arts et Metiers Institute of Technology LCPI HESAM Universite Paris75013 France CFA Ingénieurs 2000 Montévrain77144 France

ISBN: (纸本)9783030705657

The level of industrial performance is a vital issue for any company wishing to develop and acquire more market share. This article presents a novel approach to integrate intelligent visual inspection into "MES" control systems in order to gain performance. The idea is to adapt an intelligent image processing system via in-situ cameras to monitor the production system. The images are thus analyzed in real time via machine learning interpreting the visualized scene and interacting with some features of the MES system, such as maintenance, quality control, security, operations, etc. This novel technological brick, combined with the flexibility of production, contributes to optimizing the system in terms of autonomy and responsiveness to detect anomalies, already encountered, or even new ones. This smart visual inspection system is considered as a Cyber Physical System CPS brick integrated to the manufacturing system which will be considered an edge computing node in the final architecture of the platform. This smart CPS represents the 1st level of calculation and analysis in real time due to embedded intelligence. Cloud computing will be a perspective for us, which will represent the 2nd level of computation, in deferred time, in order to analyze the new anomalies encountered and identify potential solutions to integrate into MES. Ultimately, this approach strengthens the robustness of the control systems and increases the overall performance of industrial production. © 2021, The Author(s).

关键词： Artificial intelligence Automated Visual Inspection Deep learning DL Industry 4.0 machine learning ML Smart Manufacturing

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Application of bioinspired algorithms for solving transcomputational tasks 23

Application of bioinspired algorithms for solving transcompu...

引用

23rd international conference on Soft Computing and Measurement, SCM 2020

作者： Kureichik, V.V. Kursitys, I.O. Kuliev, E.V. Gerasimenko, E.M. Southern Federal University Rostov-on-Don344006 Russia

The paper investigates the pattern recognition task as one of the most important transcomputational problems. pattern recognition is applied for statistical data analysis, signal processing, image analysis, bioinformatics, machine learning, and many other fields. The area of application is related to automated pattern detection in the data using computer algorithms and data classification in terms of different categories. The development of new ideas in the processing of large volumes of information creates a new trend in the pattern recognition area. A specific field is formed by the bioinspired algorithms as mathematical reorganization, that transform the initial information into the results based on simulating the evolution methods, natural analogies, and statistic approach. In terms of computer modelling based on the bioinspired algorithms, we can create and develop difficult concepts, for which we have no analytical description. The authors present the optimal bioinspired algorithms based on the ant, monkey, and bat behavior in nature and develop software based on the principle of pattern recognition using the uploaded photo and downloading the results into the file. The experiments demonstrate the effectiveness of the proposed approach. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： pattern recognition

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Leukemia disease detection and classification using machine learning approaches: A review 1

Leukemia disease detection and classification using machine ...

引用

1st international conference on Power, Control and Computing Technologies, ICPC2T 2020

作者： Ratley, Astha Minj, Jasmine Patre, Pooja VEC Department of CS Engg. Lakhanpur C.G. India

ISBN: (纸本)9781728120683

Leukemia is a type of blood cancer which occurs due to abnormal increase in WBCs (white blood cells) in bone marrow of human body. Leukemia can be classified as acute leukemia and chronic leukemia, in which acute leukemia grows very fast whereas chronic leukemia grows slowly. Further both the types have two sub categories lymphocytic and myeloid. In this paper, we are going to analyze different image processing and machine learning techniques used for classification of leukemia detection and try to focus on merits and limitations of different similar researches to summarize a result which will be helpful for other researchers. © 2020 IEEE.

关键词： Diseases

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image-Based Facial Expression recognition Using Local Neighborhood Difference Binary pattern 3rd

Image-Based Facial Expression Recognition Using Local Neighb...

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3rd international conference on Computer Vision and image processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Yadav, Madhulika Saini, Ravi Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER;among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.

关键词： Facial expression recognition (FER) Local neighborhood difference pattern (LNDP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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Facial Expression recognition Using Improved Adaptive Local Ternary pattern 3rd

Facial Expression Recognition Using Improved Adaptive Local ...

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3rd international conference on Computer Vision and image processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Saini, Ravi Yadav, Madhulika Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vidyapith Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.

关键词： Facial expression recognition (FER) Adaptive local ternary pattern (ALTP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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Customer Churn Reasoning in Telecommunication Domain 1

Customer Churn Reasoning in Telecommunication Domain

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1st international conference on image processing and Robotics (ICIP)

作者： stehani, S. Karunya, N. Ranjan, D. R. J. B. Sumathipala, Sagara Sandanayake, T. C. Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

ISBN: (纸本)9781728165417

Today every industry tries their best to satisfy and maintain their existing customers or clients and attract new customers to their network. Similarly, telecommunication companies also working hard to increase their customer retention within the organization. However due to increasing demand customers try to switch the network connections frequently. Therefore, it is important to recognize whether the customer satisfies with the existing mobile connection or the possibility to change in future. This research analyses the churn customer details and predict the probability to churn in future using modern machine learning techniques. It is also important to identify the reasons to the churn in order to take actions to retention of the customer. Reasons for the churn were analyzed using the decision path of the Random Forest machine learning algorithm.

关键词： attribute selection churn prediction machine learning

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Identification of Breast Cancer Through Digital processing of Mammograms 1st

Identification of Breast Cancer Through Digital Processing o...

引用

1st international conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021

作者： Carrera, Enrique V. Sandoval, Bernarda Carrasco, Christian Departamento de Eléctrica Electrónica y Telecomunicaciones Universidad de las Fuerzas Armadas – ESPE Sangolquí171103 Ecuador Facultad de Ingeniería y Ciencias Aplicadas Ingeniería de Software Universidad de las Américas – UDLA Quito171204 Ecuador

ISBN: (纸本)9783030902407

Breast cancer is one of the main types of cancerous diseases in terms of its mortality rate, although it can normally be cured if the disease is detected early enough. For this reason, it is very important to have computer-assisted diagnosis systems that allow the early detection of breast cancer, especially in underdeveloped countries where women do not have the opportunity to access specialist physicians. Thus, this work proposes a system for the identification of breast cancer on mammograms, whose main objective is to keep the implementation of the system simple, efficient and effective, reducing the need for complex operations or expensive hardware. For this, the current proposal uses digital image processing techniques for the enhancement and segmentation of the regions of interest. machine learning algorithms (e.g., naive Bayes, artificial neural networks, decision trees, and nearest neighbors) are subsequently used to classify a reduced set of features extracted from the processed mammograms, according to labels that have been validated by specialist clinicians. The evaluation results show that the current proposal is straightforward and, at the same time, precise enough to identify breast cancer on mammograms with accuracy and sensitivity values greater than 99%. © 2021, Springer Nature Switzerland AG.

关键词： Diseases

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Technological Analysis of ECG Classification based on machine learning and Deep learning Techniques 1

Technological Analysis of ECG Classification based on Machin...

引用

1st international conference on image processing and Robotics (ICIP)

作者： Sudila, B. H. Nisal Poravi, Guhanathan Informat Inst Technol Colombo 6 Sri Lanka

ISBN: (纸本)9781728165417

The analysis and processing of the Electrocardiogram (ECG) reveals key information about the condition of the heart. The detection of the QRS wave segment in the ECG signal is crucial as it is the start of extracting relevantfeaturesfor the classification of heart arrhythmia. This paper presents algorithms for each block in the typical structure of an ECG classification system with the recent researches, advantages and limitations of the presented algorithms. Some including adaptive filter, wavelet Transform ANN, GWO (Grey Wolf Optimization), deep residual Convolutional Neural Networks (DRCNN), SVM.

关键词： Arrhythmia classification ECG preprocessing feature extraction optimization SVM wavelet transform CNN ANN adaptive filter

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image Watermarking by Q learning and Matrix Factorization 1

Image Watermarking by Q Learning and Matrix Factorization

引用

1st international conference on machine Vision and image processing, MVIP 2020

作者： Alizadeh, Mina Sajedi, Hedieh Babaali, Bagher University of Tehran School of Mathematics Statistics and Computer Science College of Science Tehran Iran

ISBN: (纸本)9781728168326

Today, with the advancement of technology and the widespread use of the internet, watermarking techniques are being developed to protect copyright and data security. The methods proposed for watermarking can be divided into two main categories: spatial domain watermarking, and frequency domain watermarking. Often matrix transformation methods are merged with another method to select the right place to hide. In this paper, a non-blind watermarking id presented. In order to embed watermark Least Significant Bit (LSB) replacement and QR matrix factorization are exploited. Q learning is used to select the appropriate host blocks. The Peak Signal-to-Noise Ratio(PSNR) of the watermarked image and the extracted watermark image is considered as the reward function. The proposed method has been improved over the algorithms mentioned above with no learning methods and achieved a mean PSNR values of 56.61 dB and 55.77 dB for QR matrix factorization and LSB replacemnet embedding method respectively. © 2020 IEEE.

关键词： Reinforcement learning

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Classification and Validation of MRI Brain Tumor Using Optimised machine learning Approach 1

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1st international conference on Data Science, machine learning and Applications, 2019

作者： Kaur, Prabhpreet Singh, Gurvinder Kaur, Parminder Guru Nanak Dev University Amritsar India

ISBN: (数字)9789811514203

ISBN: (纸本)9789811514197

The influence and impact of digital images on modern society, science, technology and art are incredible and image processing is now a critical part of science and technology. A brain tumor is a distinctive and abandoned enlargement of brain cells, which are the source of death associated with cancer. Early detection of the brain tumors will cut the unconditional deaths of young people. Detection of brain tumor is complex because of the complex size of the brain. MRI (Magnetic Resonance images) can give detail information with respect to the tissue life systems, which is for the recognition of brain tumors. Distinctive phases are included for the recognition of Brain Tumor i.e. pre-processing, segmentation, feature extraction and classification. Diagnostic MRI system corresponds to automated system involving enhancement of segmentation and classification process is discussed in this paper. The segmentation is the initial step that segments the benign and malignant tumor by utilizing filtering techniques available in image processing and then the classification approach to be executed. Modified median filter and multi-vector segmentation machine is used to form the segmented tumor region in the images. At the last stage, the implementation of the suggested techniques evaluated with multi support vector algorithm which distinguishes the tumor and MRI images. The proposed method efficiency increased in terms of RBF accuracy and linear accuracy. The performance analysis shows 10% betterment as compared to system exclusive of the application of modified median filtering with intensity adjustment feature. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Tumors

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