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Automatic Recognition of Road Cracks Using Gray-Level Co-occurrence Matrix and Machine Learning 3rd

Automatic Recognition of Road Cracks Using Gray-Level Co-occ...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Arya, Deeksha Ghosh, Sanjay Kumar Toshniwal, Durga CTRANS Indian Institute of Technology Roorkee Roorkee247667 India Civil Engineering Indian Institute of Technology Roorkee Roorkee247667 India Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811908392

Road cracks have a stimulating effect on the short-term and long-term life of pavements. For efficient maintenance of pavements, early detection of these cracks is imperative. The conventional methods for road condition assessments involve manual surveys that fail to meet the present-day requirements. As a result, there arises a need to use image-based approaches that can automatically detect the pavement conditions from the images of the roads. The current manuscript presents one such approach utilizing Gray-Level Co-occurrence Matrix (GLCM) and the associated features. In the proposed approach, the GLCM features are used to develop a road crack detection system based on machine learning techniques, capable of detecting the cracks, localizing them in the image, and providing valuable inputs related to the severity of the cracks present on a road section. The applicability of the proposed approach to separate cracks and non-cracks is assessed using images containing different types of cracks, captured using disparate mechanisms and from various locations (India, Japan, and China). The outcomes show promising results, proving the efficacy of the presented approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence 1

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Changkai, Lu Yiwen, Liu Yanxia, Gao Yuanquan, Shi Xiaoning, Peng School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Gesture recognition

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Feature Selection for High-Dimensional Data Based on a Multi-objective Particle Swarm Optimization with Self-adjusting Strategy Pool 3rd

Feature Selection for High-Dimensional Data Based on a Mu...

引用

3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Peng, Yingyu Wang, Ruiqi Yu, Dandan Zhou, Yu College of Mathematics and Statistics Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Information Center The First Affiliated Hospital of Dalian Medical University Dalian China

ISBN: (纸本)9789811961410

The development of data collection increases the dimensionality of the data in many fields, which might even cause the curse of dimensionality, arising a challenge to many existing feature selection. This paper proposes a method based on a feature prioritization approach and a multi-objective particle swarm optimization with self-adjusting strategy pool (MOPSO-SaSP). The proposed algorithm adopts a backward filter approach based on Approximate Markov blankets as a pre-processing step which prioritizes features into predominant features and subordinate features to reduce the search space and lessen the randomness of the initialization step. Afterwards, an adaptive mechanism is applied that enables each particle to adaptively choose a strategy from the strategy pool according to the latest information collected. The strategy pool is constructed through a series of experiments on training data. Different strategies specialized in different searching methods. Experimental results on eight benchmark micro-array gene datasets reveal that the feature prioritization approach can deal features in an efficient way and the adaptive mechanism is a powerful searching method to achieve a competitive classification accuracy and reduce the number of features. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System 3rd

Hybrid Combination of Machine Learning Techniques for Diagno...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Ganu, Likha Arun, Biri Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Yupia791110 India

ISBN: (纸本)9789811908392

Nowadays, liver impairment is one of the most complicated diseases in the world which is increasing very rapidly. Around 50 million adult population in the world are highly affected by cirrhosis liver impairment which is the 12th leading cause of death in 2020. In this paper, we survey and compare various machine learning techniques that help the clinical specialist to determine the risk of liver impairment and will aid the physician in the process of making decision. This paper will conduct a hybrid combination of machine learning techniques and was compared to other machine learning algorithms to determine the best performance. To this end, the study is carried out on the Indian liver patient dataset (ILPD). Firstly, data preprocessing is done to eliminate the irreverent feature and to identify the relevant feature using K-medoids clustering-based attribute weighting (k-MCAW), and for feature selection, PCA is integrated, and for classification, SVM is being carried out. The proposed hybrid method (k-MCAW + PCA + SVM) classifies the dataset into healthy or unhealthy classes. Then the performance is predicted by using the SVM classification technique in terms of accuracy, sensitivity analysis, F-measure, specificity, kappa statics value. The result shows that our purposed hybrid model is a prominent tool for valuable tool for the clinical decision support system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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A Graph Optimization Enhanced Indoor Localization Method 3

A Graph Optimization Enhanced Indoor Localization Method

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

作者： Li, Chao Chai, Wennan Wu, Qingkao Li, Jie Lin, Fanyu Li, Zheng Li, Qingdang Qingdao University of Science and Technology Qingdao China Hisense Home Appliances Group R&D Center Qingdao China Qingdao Agricultural University Haidu College Yantai China Weifang University of Science and Technology Weifang China

ISBN: (纸本)9781665468121

In GNSS-denied indoor environments, improving the localization accuracy and robustness in a scene map is critical. This paper proposes a graph optimization enhanced indoor localization method. It calculates the location of a pedestrian relative to a scene map by averaging the weighted localization results from the PDR localization, sliding window-based localization, and graph optimization localization. These weighting factors are dynamically adjustable. Sliding window-based localization uses the matched landmarks to estimate the transformation matrix between the window and scene maps, which is performed when a new landmark is added to the sliding window. Due to the time-consuming Wi-Fi landmark matching, this paper uses the semantic landmark matching firstly to improve the matching efficiency. The graph is constructed by using semantic landmarks, Wi-Fi landmarks, and waypoints as nodes. The association relationship between waypoints and landmarks (e.g., waypoint-waypoint, waypoint-semantic, and waypoint-Wi-Fi) and the matching relationship between landmarks (e.g., semantic-semantic and Wi-Fi-Wi-Fi) is employed as constraint edges. This paper performs graph optimization in an optimization window to reduce the computing power consumption. PDR localization has the highest weight when there are no matched landmarks in a window. Experiments are conducted in the office building and mall scenes. Results show that the proposed localization method can achieve less than 0.5 m mean localization error. Moreover, compared to the unoptimized sliding window-based localization, the localization accuracy is improved by at least 17.86 % after optimization window-based graph optimization. © 2022 IEEE.

关键词： Semantics

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Automatic Detection of Lung Cancer from Lung CT Images Using 3D Convolution Neural Network 3rd

Automatic Detection of Lung Cancer from Lung CT Images Using...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Gogoi, Lakshipriya Hussain, Md. Anwar Department of Electronics and Communication Engineering North Eastern Regional Institute of Science and Technology Nirjuli791109 India

ISBN: (纸本)9789811908392

There are enough evidences that lung cancer is the most fatal among all the cancer types predicting 22% of total cancer-related deaths in the year 2021 in USA itself for both male and female. Only 21% of lung cancer patients survive for 5 or more years which is the third-worst among all the cancers (only pancreas cancer (10%) and liver cancer (20%) have lower 5-year survival rate than lung cancer). It is highly expected that detecting lung cancer in its initial stages can increase the survival rate. An efficient computer-aided diagnosis (CADe) tool for automatic identification of lung nodules from lung CI images may be very helpful, and many research groups across the world have consistently been working on it. We too have developed a 3D CNN model for auto-detection of lung nodules from CT images which have been described herein. The model consists of 25 layers which include 5 convolution layers, 8 ReLU layers, 3 max-pooling layers, 4 fully connected layers, and 1 softmax layer and is capable of delivering 100 and 60% accuracy at epoch 30 and epoch 1, respectively. ROC area of the developed model is found to be 1, which indicates that the model is a good one and quite satisfactory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Comparative Investigation on Acoustic Attributes of Healthy Young Adults 3rd

Comparative Investigation on Acoustic Attributes of Healthy ...

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Karunaimathi, V. Prarthana Gladis, D. Balakrishnan, D. PG and Research Department of Computer Science Presidency College University of Madras Tamil Nadu Chennai India Bharathi Womens’ College University of Madras Tamil Nadu Chennai India Department of Audiology and Speech Pathology SRM Medical College SRM University Tamil Nadu Kancheepuram India

ISBN: (纸本)9789811664472

Human voice is so unique and used for a wide range of communication and entertainment. Any abnormality in voice quality, pitch, and loudness indicates voice disorder. The objective information of voice can be obtained through voice analysis applications using signal processing techniques. The results are visualized, so that the abnormal voice can be easily diagnosed and consecutively equips the clinicians to track the progression of the voice therapy and treatment. This paper aims at extraction of voice parameters from voice signals such as fundamental frequency (F0 mean, standard deviation, maximum and minimum), percentage of pitch perturbation (jitter%), amplitude perturbation (shimmer%) and harmonics-to-noise ratio (HNR) using a novel algorithm leading to an application named Ephphatha and the same were compared with the parameters of existing software applications Praat and Dr. Speech using t-test analysis and the correlation plot. The result shows that the attributes of the proposed system are strongly correlated to Praat than Dr. Speech. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Jitter

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Extracting Key Information from Shopping Receipts by Using Bayesian Deep Learning via Multi-modal Features 3rd

Extracting Key Information from Shopping Receipts by Using...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Chen, Jiaqi Shao, Lujiao Zhou, Haibin Ma, Jianghong Meng, Weizhi Wang, Zenghui Zhang, Haijun Departmant of Computer Science Harbin Institudte of Technology Shenzhen China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Electrical and Mining Engineering University of South Africa Florida1710 South Africa

ISBN: (纸本)9789811961410

This research presents a new key information extraction algorithm from shopping receipts. Specifically, we train semantic, visual and structural features through three deep learning methods, respectively, and formulate rule features according to the characteristics of shopping receipts. Then we propose a multi-class text classification algorithm based on multi-modal features using Bayesian deep learning. After post-processing the output of the classification algorithm, the key information we seek for can be obtained. Our algorithm was trained on a self-labeled Chinese shopping receipt dataset and compared with several baseline methods. Extensive experimental results demonstrate that the proposed method achieves optimal results on our Chinese receipt dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Capture and analysis of dynamic characteristics of students' eyes in assisted online teaching 3

Capture and analysis of dynamic characteristics of students'...

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Jiale, Chai Zhaohui, Zhang Xiaoyan, Zhao School of Automation and Electrical Engineering University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China Shunde Graduate School University of Science and Technology Fo Shan Beijing528399 China Beijing Engineering Research Center of Industrial Spectrum Imaging University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9781510657281

Aiming at the problem of shelter of identification and concentration analysis such as wearing masks in offline learning under the background of epidemic situation, the identification method of action energy map based on change of eye and the concentration analysis method based on eye movement offset were proposed. The eye contour maps in a complete blink cycle were extracted from the self-built blink database videos to synthesize the action energy map. Wavelet packet decomposition was used to extract features, and kernel principal component analysis (KPCA) was used to reduce the data dimension, then support vector machine (SVM) was selected for classification training. The accuracy can reach 95%. The gradient center algorithm was used to get the eyeball center and collect the videos in the states of focus and non focus in the online class, then the Manhattan distance between the eyeball center and the eye contour center was used to measure the eye movement offset, finally the threshold to divide focus and non focus was *** optimize the interactive effect of online education. © 2022 SPIE.

关键词： Image processing

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A Tensor Solution for Health Indicator Construction of Metro Wheelset Degradation with Irregular Noise 3

A Tensor Solution for Health Indicator Construction of Metro...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Wang, Yu Mao, Wentao Kou, Linlin Liu, Keying School of Computer and Information Engineering Henan Normal University Xinxiang China Beijing Jiaotong University State Key Lab of Rail Traffic Control & Safety Beijing China

ISBN: (纸本)9781665492812

During the operation of metro vehicles, wheel flange and wheel diameter continue to be abrased, so wheel degradation is inevitable. Influenced by the states of welded rail, subgrade and turnout, applied load, speed of running and other components, the degradation process of wheelset is vulnerable to irregular noise interference, which leads to abnormal fluctuation of vibration signal. As a result, the degradation process is hard to be accurately described. To address this problem, this paper proposes a new health indicator construction method for metro wheelset based on tensor reconstruction. First, tensor Tucker decomposition is utilized to obtain the core tensor of original signal, and then tensor reconstruction is applied to transform the signal into a new degradation sequence with noise reduction. Second, the Savitzky-Golay filter is employed to remove the irregular trend from the obtained degradation sequence. Finally, deep autoencoder network is used to extract deep degradation features, and after dimension reduction, a health indicator of wheelset degradation process can be obtained. The effectiveness of the proposed method is verified with Beijing subway wheelset degradation dataset in 2020. The results show that the constructed health indicator can accurately describe the whole degradation process with good trendability and monotonicity. More importantly, the proposed method possess good practicability since the key changing parts in the health indicator can correspond well to the actual maintenance records. © 2022 IEEE.

关键词： Deep autoencoder network Health indicator Irregular noise Tucker decomposition

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