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11th International Conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Wang, Shengjie Su, Guoying Sun, Yishu Jia, Wenfeng Wang, Weicheng Wang, Qingshan The First Monitoring and Application Center China Earthquake Administration Tianjin300180 China College of Computer and Electrical Engineering Hunan University of Arts and Science Changde415000 China Qingdao Zhongdian Zhongyi Intelligent Technology Development Co. Ltd Qingdao266108 China Key Laboratory of Geophysics Exploration Equipment Ministry of Education of PRC Jilin University Changchun130061 China

The multi-turn small-loop transient electromagnetic (TEM) receiving coils can be used for urban underground space survey and mine detection because of their versatility and adaptability to complex environments. However, reducing the size of the receiving coil increases the parasitic parameters while narrowing the frequency band, resulting in the loss of high-frequency signals that contains shallow geological information. We propose a hive-receiving coil, and use finite element simulation to compare multi-turn hive coils with circular receiving coils in the same size. It is proved that the hive coil has lower parasitic parameters and wider passband, which can meet the detection requirements of small-loop TEM. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Electromagnetic transients

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Data Mining Techniques for Content-Based Image Retrieval 3

Data Mining Techniques for Content-Based Image Retrieval

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Thakur, Jyotsana Vansdadiya, Rajesh Premjibhai Joshi, Kapil Singh, Devesh Pratap Trivedi, Guptnath Manickam, R. Chandigarh University Media Studies India Children's University Department of Toy Innovation Gujarat Gandhinagar India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Dr. Rajendra Prasad Central Agricultural University Bihar Pusa India Rathinam College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350399264

Depending on how the query is formatted, content and material approaches are utilized to discover photos. Words such "keywords"and "captions"are employed by content search engines totag and locate photographs. Even if it seems like a nice concept, there are number of issues with it. First off, it is quite challenging to sum up picture content in a few words since imaged at a is so rich in information. Second, manually a notating photograph requires a lot of effort. When a collection contains a large number of photos, this operation becomes extremely thing, challenging, and costly. Third, characterizing the appearance of a picture is an extremely subjective task. Finding a picture may be more challenging since an authoring tool and a user could use distinct terms todescribe the same material in the same picture. Thus, content-based image retrieval (CBIR), a technique foridentifying related pictures in a database by examiningtheir visual contents, has become essential. This article explains how to use the Haar transform, K-means clustering, and the Euclidean distance to retrieve photos. © 2023 IEEE.

关键词： Data mining

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Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab *** false information is harmful to society and *** and detecting the spread of fake news in Arabic becomes *** artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake ***,fake news in Arabic is identified by utilizing AI *** article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic *** HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful ***,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and ***,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN *** performance validation of the HPOHDL-FND technique is tested using two Arabic *** outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

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Detection method of nickel foam foreign matter based on double-branch lightweight network

Detection method of nickel foam foreign matter based on doub...

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Jianqi Liang, Yincong Liu, Huiting Cao, Binfang Yan, Wei Du, Rui Hunan University of Arts and Sciences Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft Changde China Jishou University College of Information Science and Engineering Changde China Hunan University of Arts and Sciences College of Computer and Electrical Engineering Changde China Xiangtan University College of Automation and Electronic Information Xiangtan China

ISBN: (纸本)9781665465335

Real-time, accurate and rapid detection of defects in nickel foam is a challenging task and plays a crucial role in the quality control of nickel foam production. Currently, research on deep learning-based industrial defect detection usually uses large neural network models to pursue high accuracy, ignoring model complexity and hardware requirements. In order to reduce the application cost, the Detection method of nickel foam foreign matter based on double-branch lightweight network is proposed. First, a lightweight backbone convolutional network is designed to extract features, which reduces computational complexity while effectively extracting features. Then, atrous convolution is used to further optimize the model accuracy, increasing the receptive field of the convolution without losing information, and ensuring the quality of the extracted features. Finally, we use a dual-branch structure for segmentation and classification to dynamically query the detection results of defects. The experimental results on the nickel foam defect dataset show that our model is able to achieve high detection accuracy and single-sheet detection rate, and has strong competitive accuracy and lower detection rate compared with state-of-the-art detectors amount of calculation. © 2022 IEEE.

关键词： Convolution

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Deep Learning-Based Automated Segmentation of Fiber Breaks in Advanced Composites 38

Deep Learning-Based Automated Segmentation of Fiber Breaks i...

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38th Technical Conference of the American Society for Composites, ASC 2023

作者： Jain, Sudhir Li, Carina Xiaochen Vuong, Daniel McGllick, Matthew Saifaee, Saeed Furtado, Carolina Kopp, Reed Joseph, Joshua Adjodah, Dhaval Rosini, Sebastian Sinclair, Ian Spearing, S. Mark Wardle, Brian L. Integrated Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Southampton SouthamptonSO17 1BJ United Kingdom Departments of Aeronautics and Astronautics and Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781605956916

The heterogeneity of the microstructure and the anisotropy of the mechanical properties of carbon fiber reinforced polymer composites (CFRP) are associated with complex damage mechanisms that make failure prediction highly challenging. Typical damage mechanisms for CFRP include: (i) interlaminar cracking (delamination), (ii) matrix cracking, and (iii) fiber breakage. X-ray computed tomography (CT) has been utilized recently to identify the complex damage in progressive failure of CFRP in 3D and 4D (3D spatial and 1D temporal). However, these scans contain a large amount of data (≈10 GB/mm3), which makes it challenging to extract mechanistic insights in a reasonable time frame. In previous work, we presented the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale matrix damage in CFRP. The trained CNN segments complex and sparse composite matrix damage mechanism to ≈ 99.99% agreement, with nearly 100% of human time eliminated, providing higher accuracy compared to human segmentation. In this work, we developed CNN-based method for the automated segmentation of fiber breaks using synchrotron radiation-based CT (SRCT) scans that have been human-labeled. The model employed a U-Net full CNN as the high-level encoder (feature extraction)-decoder (feature localization) structure, and VGG16 as the deep CNN backbone via the publicly available Segmentation Models GitHub repository. An unbiased evaluation was performed comparing the machine vs. the human-labeled ground truth. Finally, active learning was presented for the machine-assisted relabeling of datasets containing fiber breaks. The deep learning-based automated segmentation method demonstrated accurate identification (> 90%) of fiber breakage for CFRP. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Complex networks

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An Efficient Data Collection using Whale Optimization Algorithm (WOA) in Wireless Sensor Network

An Efficient Data Collection using Whale Optimization Algori...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Udhayakumar Pasupathi A. Gopi Saminathan A. Manikandan M. Subhashini C. Gowrishankar T. S. Poornappriya Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) (Affiliated to Bharathidasan University Tiruchirappalli) Tamilnadu India NPR College of Engineering and Technology Dindigul ECE SRM Institute of Science and Technology Chennai Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) Trichy-5 Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tiruchirappalli Module Lead Data and Analytics Aspire Systems Pvt Ltd Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated.

关键词： Wireless communication Wireless sensor networks Gears Query processing Clustering algorithms Energy efficiency Whale optimization algorithms Routing protocols Security Monitoring

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Spherical-Dynamic Time Warping - a New Method for Similarity-Based Remaining Useful Life Prediction

SSRN

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SSRN 2023年

作者： Li, Xiaochuan Yang, Yingjie Lin, Tianran Mba, David School of Electrical Engineering and Automation Hefei University of Technology Hefei China School of Computer Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom Faculty of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao China Creative Computing Institute University of the Arts London London United Kingdom

Machinery prognostics and health management (PHM) plays a key role in the reliable and efficient operation of industrial processes. With the emerging big data era, data-driven prognostic methods which avoid considering complicated system models have attracted growing research interest. Among many data-driven models, similarity-based prediction methods have been popular due to their strong interpretability and relatively simple implementation process. Nevertheless, when quantifying the similarity between two trajectories, most existing works neglect the nonlinearity of fault evolution and degradation alignments with timing difference, which may not be sufficient to retrieve the most suitable trajectories for remaining useful life (RUL) prediction. To overcome these limitations, a Spherical-Dynamic Time Warping (Spherical-DTW) algorithm is put forward to find an optimal match between the test and training trajectories at the retrieval step. Dynamic Time Warping allows degradation alignments with timing difference through stretching or compressing the trajectories with regard to time, thereby the data in similar degradation levels can be well aligned across different units. Moreover, a newly defined nonlinear Spherical distance method is introduced and incorporated into the retrieval process to account for the nonlinearity of the damage propagation process. The significance of this study is that the newly proposed Spherical-DTW algorithm goes one step further to consider the nonlinearity of fault evolutions and allow degradation pattern alignments with timing difference when performing similarity-based prognostics. Two run-to-failure cases, involving a real-world industrial compressor failure case and a gas turbine engine failure dataset, are investigated to demonstrate the effectiveness and superiority of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Condition monitoring

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Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization

Adaptive Ant Selection for Pheromone Update in Ant Colony Op...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Biao Duan, Dan-Ting Yang, Qiang Zhao, Xiao-Yan Li, Tao Liu, Dong Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China Communication University of China Key Laboratory of Media Audio & Video Ministry of Education Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Artificial Intelligence Nankai University Tianjin China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9781665410205

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain;on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems. © 2024 IEEE.

关键词： Ant colony optimization

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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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An Internet of Things Assisted Autism Spectrum Disorder Prediction Using Deep Learning Principles with Medical Image Processing Assistance

An Internet of Things Assisted Autism Spectrum Disorder Pred...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： A. Bindhu A. Sruthi Mol T. Ravi Bodkhe R. G Amjad Omar Safori S. Vijayalakshmi Department of Computer Science Infant Jesus College of Arts and Science for Women Mulagumoodu Department of ECE Sathyabama Institute of Science and Technology Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Journalism and Digital Media Faculty of Media Zaraa University Zaraa Jordan University of Business and Technology Jeddah SaudiArabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Characteristics of the complex neuro-developmental condition Autism Spectrum condition (ASD) include impaired social communication and restricted, repetitive patterns of behavior. This study presents a novel approach for identifying ASD in young infants by integrating deep learning, Internet of Things (IoT) and medical image processing. The goal of this research is to create comprehensive and discriminative features by combining wearable sensor data with brain imaging and smart home environments. For the greatest results, early diagnosis is essential. The proposed method called IoT based Autism Predictive Framework (IoTAPF) has a high detection rate of 97.8% and is cross-validated against the current Learning based Autism Detection Strategy (LADS). The proposed approach aims at improving diagnostic specificity which helps in forecasting behaviors and thus pushing earlier interventions, providing better support for ASD individuals and their families. The paper discusses the design and implementation of the framework, including sensor integration, data management, and predictive modeling. Preliminary results from pilot studies are presented, demonstrating the framework's potential to improve diagnostic accuracy and support individualized care plans. The proposed solution represents a significant advancement in the application of IoT technology for early autism detection and management, paving the way for more proactive and data-driven approaches in the field of neuro-developmental disorders.

关键词： Deep learning Autism Technological innovation Accuracy Computational modeling Image processing Smart homes Predictive models Internet of Things Wearable sensors

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