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Hand Movement Prediction Based on EEG signals by Combining MEMD and CSP 2020

Hand Movement Prediction Based on EEG signals by Combining M...

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2nd international conference on image processing and machine Vision / international conference on pattern recognition and machine learning (IPMV)

作者： Tao, Yi Yan, Nong Wang, Gang Xi An Jiao Tong Univ Sch Life Sci & Technol Key Lab Biomed Informat Engn Minist Educ Xian Peoples R China

ISBN: (纸本)9781450388412

For prosthetic limb control and rehabilitation training of disabilities, it is important to use electroencephalography (EEG) to recognize different hand movements to assist the disabilities. In this paper, we proposed a novel method by combining multivariate empirical mode decomposition (MEMD) and common spatial pattern (CSP) to extract EEG features, and achieved the prediction of hand movement. Thirty-channel EEG signals and four-channel EMG signals were acquired during the experiment, and the EEG signals were captured one second prior to the beginning of detected hand movement based on the surface electromyography (EMG) signals. MEMD was applied to decomposing the pre-processed EEG signals into several multivariate intrinsic mode functions (IMFs) and CSP was used to extract the features of IMFs. Then, the principal component analysis (PCA) was used to reduce the feature dimension. In the end, six one-versus-one support vector machines were applied to classify the EEG signals. Ten subjects participated in this experiment consisting of four types of hand movements. EEG signals were divided into a training set and a test set by five-fold cross-validation. The average classification accuracy was regarded as the final results. The optimal single IMF and combination IMFs for classification were analyzed in this study. The results showed that the proposed method had a good performance in predicting the upcoming hand movements by classifying the signals prior to the detected hand movement. The combination of IMF1, IMF2, and IMF3 revealed the highest average classification accuracy of 82.67%, and the average kappa coefficient was 0.77, which indicated the predicted results were highly consistent with the actual results. It indicates that the proposed method combining MEMD and CSP is suitable for predicting different types of hand movements.

关键词： Multivariable empirical mode decomposition Common spatial pattern Principal component analysis Support vector machine

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Pinning based Energy Aware Computation Offloading for Mobile Cloud Computing

Pinning based Energy Aware Computation Offloading for Mobile...

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international conference on Computational Science and Technology (ICCst)

作者： R.S. Vindan M. Gobi V. Meena Chikkana Arts College Tiruppur India SASTRA Deemed University Thanjavur India

ISBN: (纸本)9781665476560

Nowadays mobile devices are becoming in each other's pocket in part of human life. Most of us preferred the mobile as a good platform for their computation. But Mobile devices are still experienced with frontier resources like CPU performance and battery life when executing computation intensive applications. The exponential growth in this field necessitates the Technology Acceptance model. To overcome these limitations, the computation intensive jobs are migrated to rich resourceful server. This popular approach in mobile cloud computing is called as computational offloading. To find the computationally intensive jobs in an application, static and dynamic energy profiling is performed. Applications are represented as Task integration graph (TIG), the tasks in TIG can be executed on the mobile device or migrated to nearby resourceful server. In this paper we proposed P-ECOM algorithm for cloudlet-based image processing over quad-core machine to measure the energy profile of an image compression step. Towards the goal, each of the three image file sizes 276MB, 546MB, 715.6MB are partitioned into 1 to 4 splits. Compression process are executed in two phases, with pinned with specific core and without pinning. Measure the energy consumption for compressing each partition of image by pinning each process with specific CPU core. And also measure energy for without pinning the process with specific core. This research work gives an important insight into energy consumption pattern which concludes that if all the available cores in the offloaded devices are evenly loaded and pinned with specific core then energy consumption is minimum than without pinning the process with specific core. As per the proposed Technology Acceptance model, these energy profile details are integrated into partitioning engine. This will dynamically partition the applications based on the number of cores available and also pinned with specific core in target offloading devices to minimize the ener

关键词： Performance evaluation Energy consumption Cloud computing image coding Technology acceptance model Energy measurement Mobile handsets

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Efficient Method to Extract QRS Complex and st Segment for Cardiovascular Diseases Prediction 3rd

Efficient Method to Extract QRS Complex and ST Segment for C...

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3rd international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2020

作者： Ghodake, Sanjay Ghumbre, Shashikant Deshmukh, Sachin Pune Maharashtra India Government College of Engineering and Research Avasari Pune Maharashtra India Department of CSIT Dr. Babasaheb Ambedkar Marathwada University Aurangabad India

ISBN: (纸本)9789811604928

For the heart diseases, the early prediction required to save the human being life. There are several ways to perform the early prediction of Cardiovascular Disease (CVD), however the most of the state-of-art approaches are expensive with poor accuracy of prediction. The computerised approach used the Electrocardiogram (ECG) signals to perform the early prediction of CVD. The ECG based approach is simple, effective and inexpensive;hence it gains the significant attention of researchers from last two decades. The Computer Aided Diagnosis (CAD) system introduced the ECG based approach for CVD prediction using the ECG signal of patients on which the algorithms single processing, data mining, and machine learning applied for accurate prediction. ECG based CVD detection framework composed of three main sections that is preprocessing, features extraction, and classification. The steps like preprocessing and features extractions are crucial for the efficiency of CVD detection. In this paper, we proposed the novel framework of CVD detection of Q, R, S, T beats efficiently from the pre-processed ECG signal. From the pre-processed ECG signal, our aim is to extract QRS and st segments using the dynamic and simple thresholding approach. The segments are used then for the statistical features extraction. The classification is performed by using the Artificial Neural Network (ANN) classifier. The proposed method shows the precision rate, recall rate, detection accuracy and detection time are 0.91, 0.92, 0.91 and 1.51 respectively. It shows the balance between the accuracy and prediction time performance as compared to state of art method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary pattern (LBP) 1

Low-Density Polyethylene (LDPE) Food Packaging Defect Classi...

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1st international conference on Material processing and Technology, ICMProTech 2021

作者： Sabri, N. Hamed, H.N. Abdull Isa, M.A. Ghazali, N.S. Ibrahim, Z. School of Computing Universiti Teknologi Malaysia UTM Johor Skudai81310 Malaysia Faculty of Computer and Mathematical Sciences University Teknologi MARA Kampus Jasin Merlimau Melaka77300 Malaysia Faculty of Computer and Mathematical Sciences University Teknologi MARA Selangor Shah Alam40450 Malaysia

The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be exhausted due to repeated activities. This paper provides the primary phase for effective automation of image classification solution implemented using Weka software. An evaluation of the performance of the Support Vector machine (SVM), K-nearest Neighbour (KNN) and Random Forest (RF) classification models for Low-Density Polyethylene (LDPE) food packaging defect image classification using a small sample of dataset and Linear Binary pattern (LBP) as feature extraction algorithm is investigated. Four criteria have been used to evaluate the performance of each classification model which is accuracy, sensitivity, specificity and precision obtained from the confusion matrix table. The results indicate that SVM performs better than RF and KNN with 95% accuracy, 95% sensitivity, 72% specificity and 95% precision in classifying LDPE food packaging defect images. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Support vector machines

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

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

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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...

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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 ...

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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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