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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Donghua Feng Yahong Li Sa Wu Software and Communication School Tianjin Sino-German University of Applied Sciences Tianjin China School of Information Engineering Tianjin College Beijing University of Science and Technology Tianjin China Faculty of Science Agricultuer and Engineering University of Newcastle upon Tyne Newcaslt Upon Tyne UK

Aiming at the complex characteristics of engine fault and fuzzy boundary of fault area, a method of engine fault diagnosis based on convolution neural network and bidirectional LSTM is proposed. To explore the causes of common engine faults, a convolutional neural network feature extraction model is established, the modified linear unit is used to improve the over fitting problem of the model, and the SoftMax classifier is used to lock the fault area, and the long short-term is established Memory rural network model, the introduction of remaining life tag, using min max method to complete data preprocessing, adding a dropout layer in the bidirectional LSTM layer and full connection layer, removing the output value of hidden layer, obtaining the final fault diagnosis results, and using root mean square error and asymmetric score function to evaluate the reliability of the method. The simulation results show that the fault diagnosis has fast convergence rate, high diagnosis accuracy and strong applicability, which provides valuable technical reference for engine maintenance and maintenance.

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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$\mathtt{SFPoW}$SFPoW: Constructing Secure and Flexible Proof-of-Work Sidechains for Cross-Chain Interoperability With Wrapped Assets

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IEEE Transactions on Computers 2025年第7期74卷 2278-2292页

作者： Zhihong Deng Chunming Tang Taotao Li Zhikang Zeng Parhat Abla Debiao He School of Mathematics and Information Science Guangzhou University Guangzhou China Guangzhou Center for Applied Mathematics Guangzhou University Guangzhou China Key Laboratory of Information Security Technology Jointly by Guangdong Province and Guangzhou City Guangzhou University Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China School of Artificial Intelligence South China Normal University Foshan China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China

Sidechain techniques enhance blockchain scalability and interoperability, enabling decentralized exchanges and cross-chain operations for wrapped digital assets. However, existing PoW sidechains face challenges, including centralization, high communication costs, and incomplete PoW-based security proofs. This paper introduces $\mathtt{SFPoW}$ , a S ecure and F lexible Proof-of-Work sidechains for cross-chain interoperability with wrapped assets. $\mathtt{SFPoW}$ facilitates decentralized asset transfers and token swaps across nearly all PoW-based cryptocurrencies without requiring soft or hard forks or fixed PoW targets. It establishes a decentralized, fair validation set within the sidechain, improving adaptability and reducing competition and confirmation periods. A pluggable cross-chain proof generation method is proposed, effectively filtering lazy nodes, incentivizing active participation, and minimizing on-chain verification overhead to a proof size of 200.5 bytes. Through mining behavior analysis and cryptographic reductions, $\mathtt{SFPoW}$ satisfies weak and strong atomicity. Experiments on the Ronin blockchain and an Ethereum testnet demonstrate a round-trip cost of $6.55 and latency of 372.1–382.6 seconds, confirming its practicality and efficiency.

关键词： Blockchains Protocols Bitcoin Proof of Work Interoperability Security Costs Scalability Monitoring Artificial intelligence

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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On average orders of automorphism groups of bilinear maps over finite fields

arXiv

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arXiv 2025年

作者： Bläser, Markus Li, Yinan Qiao, Youming Rogovskyy, Alexander Saarland University Saarland Informatics Campus Saarbrücken Germany School of Artificial Intelligence Wuhan University National Center for Applied Mathematics in Hubei Hubei Key Laboratory of Computational Science Wuhan China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

Let φ: V × V → W be a bilinear map of finite vector spaces V and W over a finite field Fq. We present asymptotic bounds on the number of isomorphism classes of bilinear maps under the natural action of GL(V) and GL(W), when dim(V) and dim(W) are linearly related. As motivations and applications of the results, we present almost tight upper bounds on the number of p-groups of Frattini class 2 as first studied by Higman (Proc. Lond. Math. Soc., 1960). Such bounds lead to answers for some open questions by Blackburn, Neumann, and Venkataraman (Cambridge Tracts in Mathematics, 2007). Further applications include sampling matrix spaces with the trivial automorphism group, and asymptotic bounds on the number of isomorphism classes of finite cube-zero commutative algebras. Copyright © 2025, The Authors. All rights reserved.

关键词： Vector spaces

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Performance Benchmarking of NewSQL Databases with Yahoo Cloud Serving Benchmark

Performance Benchmarking of NewSQL Databases with Yahoo Clou...

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Future Technologies Conference, FTC 2020

作者： Astrova, Irina Koschel, Arne Wellermann, Nils Klostermeyer, Philip Department of Software Science School of IT Tallinn University of Technology Akadeemia Tee 21 Tallinn12618 Estonia Faculty IV Department of Computer Science University of Applied Sciences and Arts Hannover Ricklinger Stadtweg 120 Hannover30459 Germany

ISBN: (纸本)9783030630881

Selecting a NewSQL database product is an important process. Like any other successful database management system technology, the product selected today begins to define the legacy of the future. There are many different parameters that can be used to evaluate the NewSQL database alternatives, and there is no single most-correct process for conducting such an evaluation. Not only are there many possible pertinent evaluation criteria, but there also is typically a degree of uncertainty about the requirements and characteristics of the intended application environment. This paper focuses on three NewSQL databases (viz., VoltDB, MemSQL and NuoDB), with emphasis on evaluating their performance. The evaluation is done based on Yahoo Cloud Serving Benchmark. © 2021, Springer Nature Switzerland AG.

关键词： Database systems

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On Testing Microservice Systems

On Testing Microservice Systems

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Future Technologies Conference, FTC 2020

作者： Koschel, Arne Astrova, Irina Bartels, Mirco Helmers, Mark Lyko, Marcel Faculty IV Department of Computer Science University of Applied Sciences and Arts Hannover Ricklinger Stadtweg 120 Hannover30459 Germany Department of Software Science School of IT Tallinn University of Technology Akadeemia tee 21 Tallinn12618 Estonia

ISBN: (纸本)9783030630911

Microservices gain more and more importance and are already widely used in many software systems. While using a microservice architecture can bring a lot of benefits, there are also lots of new challenges arising. The general complexity of loosely coupled microservice systems is higher than in mono-lithic software systems. As a result, the testing of microservice systems can be more difficult and calls for additional testing methods and techniques. Furthermore, the usage of additional testing tools is recommended. © 2021, Springer Nature Switzerland AG.

关键词： Testing

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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Computer Systems science & Engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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Computer Systems science & Engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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Artificial Intelligence (ICAI), International Conference on

作者： Zia Ul Islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

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