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IEEE International Conference on Systems, Man and Cybernetics

作者： Nirayo Hailu Gebreegziabher Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In this work, we focused on end-to-end speech recognition for less-resourced language, Amharic. The result can be integrated with other tasks such as spoken content retrieval. We explored three models, which consist of Convolutional Neural Networks, Recurrent Neural Networks, and Connectionist Temporal Classification, towards end-to-end speech recognition on less-resourced language. Further, we studied the possibility of having an end-to-end system with 1-best output keeping the network parameters and computational resource minimal. The paper gives attention to finding a more suitable sub-lexical unit for the Amharic end-to-end speech recognition system which can be used as an audio indexing unit. We present the first result comparing grapheme, phoneme, and syllable-based end-to-end speech recognition systems for our target language. The models are evaluated on approximately 52 hours of Amharic speech corpus containing read-speech, audiobooks, and multi-genre radio programs. On the test set, we report a character error rate (CER) of 19.21% and a syllable error rate (SER) of 39.98% for a syllable-based end-to-end model without lexicons and language model integrated.

关键词： Training Error analysis Computational modeling Speech recognition computer architecture data models Task analysis

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Advancements in Demodex mite detection: A comparative analysis of YOLOv5 and YOLOv8 utilizing microscopic examination images

Advancements in Demodex mite detection: A comparative analys...

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2023 International Conference on computer Application and Information Security, ICCAIS 2023

作者： Wang, Mini Han Fang, Xiaoxiao Lin, Zhiyuan Zeng, Peijin Yang, Yu Liu, Yunxiao Liu, Haoyang Hu, Wenhan Li, Xinyue Jiang, Xudong Chen, Guangshun Hou, Guanghui Chong, Kelvin K.L. Fang, Junbin The Faculty of Medicine Chinese University of Hong Kong 999077 Hong Kong City University of Macao The faculty of Data Sciences 999078 China Radiology and Imaging Department Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Guangdong Zhuhai519000 China AI and big data R&D department Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Guangdong Zhuhai519000 China AI and Ophthalmology R&D Laboratory Perspective Technology Group Guangdong Zhuhai519000 China The department of Dry Eye Disease Zhuhai Aier Eye Hospital Guangdong Zhuhai519000 China Beijing Institute of Technology Guangdong Zhuhai5190000 China School of Biomedical Engineering Guangdong Medical University Guangdong Dongguan523000 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519000 China School of Optometry and Ophthalmology Tianjin Medical University Tianjin300000 China The School of Computer Zhuhai College of Science and Technology Guangdong Zhuhai519000 China Department of Optoelectronic Engineering Jinan University Guangdong Guangzhou510000 China

ISBN: (纸本)9781510675032

This study conducts a rigorous comparative assessment of YOLOv5 and YOLOv8 for the detection of Demodex mites in microscopic examination images, leveraging crucial metrics such as accuracy, precision, recall, and F1-score. The investigation reveals the unequivocal superiority of YOLOv8, not only in quantitative measures but also substantiated by visual evidence, showcasing its applicability for real-time scenarios. YOLOv8 exhibits exceptional accuracy in overall detection and introduces a novel functionality for quantitative assessment of individual mites, providing essential granularity for precise diagnoses and therapeutic planning within dermatological and ophthalmological contexts. Positioned as a substantial advancement in object detection methodologies, YOLOv8 holds promise for significantly improving both accuracy and granularity in Demodex mite detection within microscopic examination images. While acknowledging potential limitations associated with dataset-specific considerations, this research underscores the imperative for further validation across diverse clinical scenarios. Computational considerations for real-time processing prompt future investigations to explore optimization strategies, particularly in resource-constrained environments. These findings position YOLOv8 as a valuable tool for clinicians and researchers engaged in dermatological and ophthalmological studies, offering heightened accuracy and nuanced insights. Ongoing research, encompassing clinical validations and comparative assessments with other state-of-the-art models, is anticipated to contribute to a more exhaustive understanding of YOLOv8's potential and limitations in real-world applications based on microscopic examination images. © 2024 SPIE.

关键词： Object detection

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A Light-weight Convolutional Neural Network based Speech Recognition for Spoken Content Retrieval Task

A Light-weight Convolutional Neural Network based Speech Rec...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nirayo Hailu Gebreegziabher Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Convolutional Neural Network has shown to achieve a state of the art performance in computer vision. They have also progressively become popular in speech recognition and other natural language processing tasks. In this study, we aim at designing a light-weight Convolutional Neural Network architecture for the under-resourced end-to-end speech recognition task. We present a carefully designed 1-dimensional Convolutional deep neural network architecture that could achieve reasonable accuracy to be cascaded with spoken content retrieval systems. We explored the usage of Convolutional Neural Networks with Connectionist Temporal Classification under resource-constrained conditions. The possibility of having an end-to-end system with the best decoding result keeping the network parameters and computational time minimum is also shown. The paper presents the results on the Amharic syllable-based end-to-end speech recognition system implementing the designed model. The architecture is trained and evaluated on ≈70 hours of Amharic read-speech, audiobooks, and multi-genre radio programs. On the development set, we report a character error rate of 12.60% and a syllable error rate of 27.28% without language-models integrated. Likewise, on the test set 18.38% character error rate and 27.71% syllable error rate is reached.

关键词： Training Error analysis Computational modeling Speech recognition computer architecture Convolutional neural networks Task analysis

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

arXiv

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arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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knowledge Cores in large formal contexts

arXiv

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arXiv 2020年

作者： Hanika, Tom Hirth, Johannes Knowledge and Data Engineering Group Dep. of Electrical Engineering and Computer Science University of Kassel

knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called k-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data *** Codes 68T30 03G10 97R40 Copyright © 2020, The Authors. All rights reserved.

关键词： knowledge based systems

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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CoSimLex: A resource for evaluating graded word similarity in context 12

CoSimLex: A resource for evaluating graded word similarity i...

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12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Armendariz, Carlos S. Purver, Matthew Ulcar, Matej Pollak, Senja Ljubešic, Nikola Robnik-Šikonja, Marko Granroth-Wilding, Mark Vaik, Kristiina Cognitive Science Research Group Queen Mary University of London London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Slovenia Department of Computer Science University of Helsinki Finland Department of Data Analysis Texta Estonia

ISBN: (纸本)9791095546344

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context;standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures;covers not only discrete differences in word sense but more subtle, graded changes in meaning;and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： Semantics

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Solving the Traveling Salesperson Problem using Frequency Fitness Assignment

Solving the Traveling Salesperson Problem using Frequency Fi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tianyu Liang Zhize Wu Jörg Lässig Daan van den Berg Thomas Weise Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui China Enterprise Application Development Group Faculty of Electrical Engineering and Computer Science Höchschule Zittau/Gorlitz Görlitz Germany Department of Computer Science Vrije Universiteit Amsterdam Amsterdam The Netherlands Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei Anhui China

ISBN: (纸本)9781665487696

Metaheuristic optimization is based on the idea that better solutions are preferable to worse ones. Frequency Fitness Assignment (FFA) is a module that can be plugged into most optimization algorithms. It replaces the objective values in the selection step with their encounter frequency during the search so far. The search effort is therefore distributed evenly over the whole range of the objective values. Recently, it was shown that using FFA can significantly improve the performance of algorithms on hard problems such as Trap and Jump functions and the NP-hard MaxSat problem. However, the objective functions of all of these problems have relatively small ranges. This work is the first to explore the impact of FFA on metaheuristics for solving Traveling Salesperson Problem (TSP) instances, whose objective values tend to cover a wider range. We plug FFA into the (1+1) EA to obtain the (1+1) FEA. We perform extensive experiments on 18 instances from TSPLIB using two different unary search operators. We find that the (1+1) FEA does not get stuck in local optima and can solve many more instances to optimality than the (1+1) EA. However, it tends to be slower in reaching good intermediate solutions. Its performance decreases with the problem scale and the number of different possible tour lengths.

关键词： Metaheuristics Search problems Linear programming Computational intelligence Plugs

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