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Mechatronics and Electrical engineering (MEEE), International Conference on

作者： Juheon Kwak Wonkeun Jo Soomin Lee Hyein Kim Jeongin Koo Dongil Kim Division of Artificial Intelligence and Software Ewha Womans University Seoul South Korea Computer Science and Engineering Chungnam National University Daejeon South Korea Manufacturing System R&BD Group Korea Institute of Industrial Technology Cheonan South Korea Department of Data Science Ewha Womans University Seoul South Korea

Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

关键词： Training Electrical engineering Mechatronics Force measurement Force Time series analysis Process control

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing Information retrieval data mining Task analysis

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagnosis of Pancreatic Tumor using Region-based Segmentation Techniques

Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagno...

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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： Nilankar Bhanja Akila A Devulapalli Sudheer Ashok Kumar PramitBrata Chanda Rakesh Dani Department of Electronics and Communication Engineering Techno Engineering College Banipur Habra West Bengal India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science and Engineering VallurupalliNageswara Rao VignanaJyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani West Bengal India Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttarakhand India

Globally, the pancreatic tumor is one of the principal sources of cancer death. This is because of a deficiency in promising tools for prompt identification of this cancer. Nowadays, the automatic discovery of pancreatic cancers with the help of novel computed tomography is extensively used for the analysis and presentation of pancreatic tumors. Conventional approaches are capable of extracting only low-level features. Tumors in pancreatic malignant that extremely impends the life span of infected people. Categorization of tumors without human intervention is a really challenging task. But image segmentation and classification have real-world complications, such as unbalanced categorization accuracy, a heavy workload, and the final outcomes determined by the subjective judgment of the medical expert during the analysis and presentation of pancreatic cancers. In addition, precise prediction of pancreatic cancers could help the clinical experts to provide the best therapeutic schedule for infected people of various stages. In this research work, Region-Based Segmentation (RBS) is used to segment the input images of pancreatic cancers. In case of feature extraction, Particle Swarm Optimization (PSO) _ Convolutional Neural Network (CNN), Cuckoo Algorithm _ Convolutional Neural Network (CNN), Modified Cuckoo Algorithm _ Convolutional Neural Network (CNN) are adopted. Results are evaluated based on Accuracy, Precision, Recall, time period. Results have proven that the proposed Modified Cuckoo Algorithm_ Convolutional Neural Network (CNN) performs better in all aspects.

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Automatic Text Summarization Method Based on Improved Textrank Algorithm and K-Means Clustering

SSRN

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SSRN 2023年

作者： Liu, Wenjun Sun, Yuyan Yu, Bao Wang, Hailan Peng, Qingcheng Hou, Mengshu Guo, Huan Wang, Hai Liu, Cheng Chengdu611731 China School of Computer and Software Engineering XiHua University Chengdu610039 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group Corporation Chengdu610041 China

Automatic text summarization is to obtain a summary by compressing the text while retaining its important information, and then users can obtain the important content of the text by reading the summary. In the research literatures, the extraction method is widely used and is also one of the main research methods of most abstracts. However, this summary method still has some problems. The selection of the initial cluster center has not been carefully determined, and in articles with complex sentences, the sentence redundancy summarized is high. In order to solve the above problems, this paper proposes an automatic text summarization method based on improved TextRank algorithm and K-Means clustering. This method uses the improved BM25 model to calculate the BM25 similarity between sentences, and then uses the TextRank model to obtain the TR score of sentences. Then, the TR score is used to select the initial center of K-Means clustering. Finally, the summary is obtained through the class score and sentence score. The experimental results show that the proposed BM25Rank algorithm has a better effect on DUC2004 dataset. In conclusion, the improved TextRank and K-Means method proposed in this paper improve the accuracy of automatic text summarization and reduce the redundancy. © 2023, The Authors. All rights reserved.

关键词： K-means clustering

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Brain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images

Brain Cancer Tumor Detection by U-Net Deep Learning Algorith...

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Annual Information technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Utpal Chandra Das Watit Benjapolakul Manoj Gupta Chanyanan Somthawinpongsai Timporn Vitoonpong Pannee Suanpang Sujin Butdisuwan Aziz Nanthaamornphong Department of Electrical Engineering Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Chhattisgarh India Shinawatra University Pathum Thani Thailand Dept. of Rehabilitation Medicine King Chulalongkorn Memorial Hospital and Dept. of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Information Technology Faculty of Science and Technology Suan Dusit University Bangkok Thailand College of Computing Prince of Songkla University Phuket Thailand

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas

关键词： Deep learning Image segmentation Correlation Shape Magnetic resonance imaging Genomics DNA Clustering algorithms Bioinformatics Standards

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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Random-coupled Neural Network

arXiv

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arXiv 2024年

作者： Liu, Haoran Liu, Mingzhe Li, Peng Wu, Jiahui Jiang, Xin Zuo, Zhuo Liu, Bingqi The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China The Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China The School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China The Engineering & Technical College of Chengdu University of Technology Leshan614000 China The Southwestern Institute of Physics Chengdu610225 China The Chengdu University Chengdu610106 China

Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between the PCNN and biological neural systems remain: limited neural connection, high computational cost, and lack of stochastic property. In this study, random-coupled neural network (RCNN) is proposed. It overcomes these difficulties in PCNN’s neuromorphic computing via a random inactivation process. This process randomly closes some neural connections in the RCNN model, realized by the random inactivation weight matrix of link input. This releases the computational burden of PCNN, making it affordable to achieve vast neural connections. Furthermore, the image and video processing mechanisms of RCNN are researched. It encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, the same as biological neural information encoding characteristics. Finally, the RCNN is applicated to image segmentation, fusion, and pulse shape discrimination subtasks. It is demonstrated to be robust, efficient, and highly anti-noised, with outstanding performance in all applications mentioned above. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Blockchain Based Voting System for Secure and Transparent Electoral Processes

Blockchain Based Voting System for Secure and Transparent El...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Sumathy S S. Karthick Murugan J. Latha G. Mageshwari Allen Joseph Gilbert Andrews M Hema Kumar PG Department of Computer Science Sir Theagaraya College-Shift II Chennai Tamilnadu India Department of Computer Science and Engineering R. M. D. Engineering College Thiruvallur Tamilnadu India Department of Computer Science and Engineering R. M. K. Engineering College Tiruvallur Tamilnadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tiruvallur Tamilnadu India Specialist leader Deloitte consulting LLP 200 Sterling road Mechanicsburg PA USA Department of ECE Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This study describes an innovative approach to blockchain-based voting systems. The proposed method guarantees the integrity, security, and resistance to manipulation of elections conducted on a decentralized network through the utilization of smart contracts and cryptographic principles. Emphasizing participant confidentiality, vote integrity, and real-time auditability, the experimental outcomes demonstrate the system's scalability, dependability, and security. Within the domain of theoretical democratic discourse, presents a prospective scenario in which voting methods based on blockchain technology. The results indicate that the approach can be easily implemented on a large scale, representing a significant milestone in the struggle for democratic, secure, and transparent voting procedures. With highly efficient, boasting a transaction speed of 500 TPS, scalability to accommodate over a thousand users, and a block confirmation time of less than 10s. Encryption efficiency and digital signature dependability are indicators of the system's high level of security. Equipped with an accuracy rate that is 97.2%.

关键词： Resistance Electric potential Voting Scalability Smart contracts Systems architecture Transforms Real-time systems Blockchains Encryption

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Anomaly Detection in IoT data

Anomaly Detection in IoT Data

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IST-Africa

作者： Jason N. Kabi Ciira Wa Maina Edwell T. Mharakurwa Centre for Data Science and Artificial Intelligence Dedan Kimathi University of Technology. P.O. BOX -PRIVATE BAG – 10143 Dedan Kimathi Nyeri Kenya Department of Electrical and Electronic Engineering Dedan Kimathi University of Technology.P.O. BOX -PRIVATE BAG – 10143 Dedan Kimathi Nyeri Kenya

This work describes the performance-evaluation of various unsupervised classical machine learning algorithms in time series outlier detection. The aim is to test the robustness of known classical models that act as baselines in anomaly detection. IoT offers flexibility for various anomalies detection algorithms to be tested since the data collected is voluminous and the types of anomalies found are diverse. By deploying fine-tuned, long-established models, researchers can improve on the quality of the data they release from or use in various studies. This work also provides an insight into how time series data properties such as non-stationarity can affect anomaly detection and how operations such as windowing can be used to mitigate the effects and achieve desirable results. The experiments done show that, with some fine-tuning and data pre-processing, classical outlier detection methods’ performance can be enhanced and utilized in IoT data quality control.

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