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International Conference on Document Analysis and recognition

作者： Michele Alberti Lars Vögtlin Vinaychandran Pondenkandath Mathias Seuret Rolf Ingold Marcus Liwicki Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland University of Fribourg Fribourg Switzerland Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Machine Learning Group Luleå University of Technology Sweden

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.

关键词： Semantics Task analysis Image segmentation Degradation Layout Image color analysis Noise reduction

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Support-Vector-machine-based Adaptive Scheduling in Mode 4 Communication

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Computers, Materials & Continua 2022年第11期73卷 3319-3331页

作者： Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea College of Computing&Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Minhaj University LahoreLahore54000Pakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network ***,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one *** of the main requirements in a VANET is to provide self-decision capability to the *** memory,which stores all the previous routes,is used by the vehicles to choose the optimal *** networks,communication is *** cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each *** are allocated in a distributed manner,which is known as Mode 4 *** support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this *** k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 *** results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

关键词： Mode-4 communication ad-hoc vehicular network CV2X support vector machine

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Trainable spectrally initializable matrix transformations in convolutional neural networks

arXiv

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arXiv 2019年

作者： Alberti, Michele Botros, Angela Schütz, Narayan Ingold, Rolf Liwicki, Marcus Seuret, Mathias University of Fribourg Switzerland ARTORG Center for Biomedical Engineering Research University of Bern Switzerland EISLAB Machine Learning Luleå University of Technology Sweden Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany V7 Ltd London United Kingdom

In this work, we investigate the application of trainable and spectrally initializable matrix transformations on the feature maps produced by convolution operations. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers, ImageNet) images to historical documents (CB55) and handwriting recognition (GPDS). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases by an average of 2.2 % across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Inherently Interpretable Multi-label Classification Using Class-Specific Counterfactuals

arXiv

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arXiv 2023年

作者： Sun, Susu Woerner, Stefano Maier, Andreas Koch, Lisa M. Baumgartner, Christian F. Cluster of Excellence – Machine Learning for Science University of Tübingen Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Hertie Institute for Artificial Intelligence in Brain Health University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net1, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models. © 2023, CC BY.

关键词： Classification (of information)

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Convolutional Neural Network Based Intelligent Handwritten Document recognition

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Computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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Automated File labeling for Heterogeneous Files Organization Using machine learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Trainable Spectrally Initializable Matrix Transformations in...

引用

International Conference on pattern recognition

作者： Michele Alberti Angela Botros Narayan Schutz Rolf Ingold Marcus Liwicki Mathias Seuret Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland V7 Ltd London United Kingdom ARTORG Center for Biomedical Engineering Research University of Bern Switzerland EISLAB Machine Learning Luleå University of Technology Sweden Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

In this work, we introduce a new architectural component to Neural Network (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

关键词： Program processors Discrete Fourier transforms Transforms Documentation pattern recognition Discrete cosine transforms Convolutional neural networks

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LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

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International Journal of Communication Systems 2023年第9期36卷

作者： Mohammadian, Vahid Navimipour, Nima Jafari Hosseinzadeh, Mehdi Darwesh, Aso Department of Computer Engineering Qeshm Branch Islamic Azad University Qeshm Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pattern Recognition and Machine Learning Lab Gachon University Seongnam Korea Republic of Information Technology Departments University of Human Development Sulaimaniyah Iraq

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Ant colony optimization

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Memory and Schema in Human-Generative Artificial Intelligence Interactions

Memory and Schema in Human-Generative Artificial Intelligenc...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Dimitrios P. Panagoulias Persephone Papatheodosiou Anastasios Bonakis Dimitrios Dikeos Maria Virvou George A. Tsihrintzis Department of Informatics University of Piraeus Piraeus Greece Department of Psychiatry Sleep Research Unit National and Kapodistrian University of Athens Athens Greece Second Department of Neurology National and Kapodistrian University of Athens Athens Greece Department of Informatics Lab of Software Engineering University of Piraeus Piraeus Greece Department of Informatics Lab of Pattern Recognition and Machine Learning - Multimedia Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

In this paper, we explore memory and Schema through the lens of mathematical representation. By defining themes related to cognitive processes, we propose a compression algorithm that measures and retains important information of previous (historic) user-Generative AI interaction. Time and memory are interconnected via a decay mechanism, where context gets more abstract as time passes. At the same time, memories with thematic resemblance can be structured into Schemas using set theory, based on rules influenced by a maturity threshold. This threshold is determined by factors such as criticality, emotional-user interaction, and mass, which is the measure of the summation of related memories. Our approach reconstructs memories into reusable parameters and formulates Schemas, offering a foundation for more personalized and cost-effective human-GAI interactions.

关键词： Cognitive processes Set theory Artificial intelligence Compression algorithms Lenses

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Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep learning

Fetal Re-Identification in Multiple Pregnancy Ultrasound Ima...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Elisabeth Gabler Michael Nissen Thomas R. Altstidl Adriana Titzmann Kai Packhäuser Andreas Maier Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany Department of Computer Science Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany

Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and *** relevance— To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.

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