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Deep learning based water leakage detection for shield tunnel lining

Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

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computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Ahmed, Marzia Mohamad, Ahmad Johari Rahman, Mostafijur Sulaiman, Mohd Herwan Kashem, Mohammod Abul Faculty of Electrical & Electronics Engineering Technology Pahang Malaysia Daffodil International University Department of Software Engineering Dhaka Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Bangladesh DUET Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics. © 2023 IEEE.

关键词： COVID-19

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Towards Safe Online Machine Learning Model Training and Inference on Edge Networks 22

Towards Safe Online Machine Learning Model Training and Infe...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Maruf, Md Al Azim, Akramul Auluck, Nitin Sahi, Mansi Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada Indian Institute of Technology Departmcnt of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350345346

With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and inference is essential. Although data and model parallelism offer significant advantages for large machine learning model training, adopting parallel computing architecture in edge networks is challenging. It introduces safety concerns while splitting and integrating machine learning models over different computing nodes, which can pose risks to the integrity and reliability of the system. Therefore, online model training and inference in edge networks require a safe parallel computing architecture to achieve improved performance with optimal resource utilization. To address this challenge, we propose an efficient machine learning model partitioning algorithm that considers the safety constraint and requirements of edge networks and includes the triple-modular redundancy (TMR) technique for trusted computation. Our proposed approach achieves a significant speedup of approximately 56.3% in net training time compared to the non-partitioning approach, making it more efficient and suitable for real-time applications in edge networks. © 2023 IEEE.

关键词： Edge Networks Machine Learning Model Partitioning Triple Modular Redundancy (TMR)

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Viability Analysis Of Drug-Treated Tumor Spheroids Using Machine Learning

Viability Analysis Of Drug-Treated Tumor Spheroids Using Mac...

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2024 Medical Technologies Congress, TIPTEKNO 2024

作者： Oǧuz, Kaya Aslan, Arda Evcin, Emre Özoǧul, Emre Sönmez, Mehmet Eren Karabacak, Yaren Korkmaz, Ilker Karagonlar, Zeynep Fırtına Department of Computer Engineering Izmir University of Economics Izmir Turkey Department of Genetics and Bioengineering Izmir University of Economics Izmir Turkey Department of Software Engineering Izmir University of Economics Izmir Turkey

ISBN: (纸本)9798331529819

3D spheroids that are able to mimic the microenvironment of tumors effectively have emerged as significant structures in cancer biology and drug development. This study aims to help cancer researchers monitor the changes in human liver cancer spheroids in response to drug treatment by offering a software tool for evaluating cell viability within 3D spheroids. A dataset of spheroid images are collected, processed, and classified using alternative machine learning models constructed with Random Forest, Logistic Regression, Support Vector Machine and Extreme Gradient Boosting methods. The classification performances of the models are evaluated in terms of the prediction accuracy, precision, recall, and F1-score values. Based on the test experiments conducted, Extreme Gradient Boosting model achieved the highest ratios for all of the performance metrics. Furthermore, a standalone desktop application is implemented to perform analyses of the images with the help of its user-friendly interface. © 2024 IEEE.

关键词： Support vector regression

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Deep Learning-Based Audio Splicing Forgery Detection Using PVTv2 Transformer

Deep Learning-Based Audio Splicing Forgery Detection Using P...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Kanca, Elif Gulsoy, Tolgahan Ustubioglu, Arda Ustubioglu, Beste Ayas, Selen Baykal Kablan, Elif Ulutas, Guzin Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Management Information Systems Trabzon University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (纸本)9798350379433

Attacks on multimedia files by malicious users have become quite common, especially with the increase in the number of editing tools and their ease of use. Considering that such files can now be used both as evidence and for social visibility in all kinds of environments, it has become important to prove their authenticity. With the proposed method, the detection of merging forgeries in audio files has been carried out. For this purpose, the audio files received from the input are converted into cochleagram images. The PVTv2 based deep network architecture is trained with the generated cochleagram images. As a result of the training, the suspicious audio file given as input is labelled as original/fake. The proposed method gives 96.11% accuracy for 2s database, 94.63% accuracy for 3s database and 95.19% accuracy for 2s-3s database. © 2024 IEEE.

关键词： Deep learning

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Mathematical Methods for Reducing the Search Space for Solutions in 'Big Data' Analysis and Management 14

Mathematical Methods for Reducing the Search Space for Solut...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Dmytro Udovyk, Iryna Martynenko, Andrii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

ISBN: (纸本)9798350350036

This paper addresses mathematical methods to reduce or segment the search space for big data solutions into distinct subspaces with partial solutions. It is achieved by using a data organization structure of 'm-tuples based on ordered sets of arbitrary cardinality.' We provide a mathematical description of this structure and review contemporary methodologies to address similar problems. The study highlights how these properties can significantly reduce the analyzed state space and includes graphs that demonstrate changes in data volume within these subspaces. Additionally, we analyze how variations in four specific arguments affect data volume changes and provide an overview of the efficiency gains in resource use from applying the mathematical methods discussed. © 2024 IEEE.

关键词： Data structures

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Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Qamar, Muhammad Salman Haq, Ihsan Ul Munir, Muhammad Fahad Roslee, Mardeni Bin Awan, Adnan Anwar Waseem, Athar International Islamic University Department of Electrical and Computer Engineering Islamabad Pakistan Multimedia University Centre for Wireless Technology Faculty of Engineering Cyberjaya63100 Malaysia School of Engineering California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798331530747

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN. © 2024 IEEE.

关键词： Sensor nodes

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Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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