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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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Performance of Deep Learning Techniques in Leaf Disease Detection

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computer systems science & Engineering 2024年第5期48卷 1349-1366页

作者： Robertas Damasevicius Faheem Mahmood Yaseen Zaman Sobia Dastgeer Sajid Khan Centre of Real Time Computer Systems Kaunas University of TechnologyKaunas51386Lithuania Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science City UniversityPeshawar25000Pakistan

Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding ***,the identification of leaf diseases is essential in this field of *** brought on by bacteria,viruses,and fungi are a significant factor in reduced crop *** machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved *** study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected *** effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and *** focus was on optimizing the hyper-parameters of these deep learningmodels with prior *** the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.

关键词： Smart agriculture deep learning plant disease recognition

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Improving energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

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2022 International Conference on Networks, Communications and Information Technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improving the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the time-series block data is constructed according to the data shape of the Periodic block;then the time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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Online Reduction of Exploration Space for Automated Underwater Modem Optimization

Online Reduction of Exploration Space for Automated Underwat...

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MILCOM, Military Communications Conference

作者： Marcel Rieß Steffen Moser Frank Slomka Institute of Embedded Systems/Real-Time Systems Faculty of Engineering Computer Science and Psychology Ulm University Germany

Developing efficient underwater communication systems is complex, as multiple factors impact communication quality. While rapid prototyping has been a common approach, it carries a high risk of failure. To address this challenge, model-based development and implementation have gained popularity. However, selecting the optimal combination of modulation type, correction properties, synchronization properties, and other parameters remains time-consuming and intricate. This paper presents an innovative approach to streamline the development process by reducing the exploration space. The proposed method evaluates potential solutions based on scenario-specific, application-specific, and performance-related upper and lower bounds for the selectable communication system. These bounds consider common underwater challenges, such as SNR degradation and multiple bounce loss, and distinguish between dynamic and static system analyses. By adopting this approach, existing analysis and development workflow achieve more optimized communication systems and afford more efficient variant selection using narrowing down the solution space and considering the specific requirements of each scenario. The approach’s effectiveness is demonstrated through case studies and evaluations, highlighting the benefits of reducing the exploration space and utilizing scenario-, application-, and performance-based bounds. This enables the development of robust and tailored underwater communication systems, improving communication quality and overall system performance.

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Very Large Scale ITS Applications with Fast Fitness Evaluation

Optimizing Very Large Scale ITS Applications with Fast Fitne...

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IEEE Conference on Wireless Communications and Networking

作者： Nagacharan Teja Tangirala Rouven Rischert Christoph Sommer Alois Knoll Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

As wireless communication technology advances, the complexity of Vehicular Ad-hoc Network (VANET) simulations increases. This, however, is at odds with the need for increasingly large-scale Intelligent Transportation systems (ITS) scenarios to satisfy the demands of increasingly Artificial Intelligence (AI) based solutions. This paper aims to demonstrate that a high-performance simplified VANET simulator can be used for fitness evaluation without loss in solution quality. As an example, we implement a Roadside Unit (RSU) deployment approach based on a genetic algorithm. Disolv, a simplified VANET simulator, is used as a fitness evaluation tool. To validate the solution quality, the best solutions of a few select generations are evaluated with a fully-featured ns-3 driven VANET simulation. From the comparison of fitness values, it can be observed that the values from Disolv allow us to predict those obtained via ns-3. Further, the execution time analysis showcases the substantial performance gains of using a more abstract VANET simulator. A 4.6-hour analysis with Disolv contrasts approximately 300 days with ns-3 for the given scenario and settings. Finally, the potential applications and limitations of using a simplified VANET simulator are discussed.

关键词： Wireless communication Vehicular ad hoc networks Performance gain Complexity theory Artificial intelligence Optimization Intelligent transportation systems Genetic algorithms

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Poster: Disolv - Dataflow-Centric Integrated Simulation of Large-Scale VANETs

Poster: Disolv - Dataflow-Centric Integrated Simulation of L...

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IEEE Vehicular Networking Conference (VNC)

作者： Nagacharan Teja Tangirala Christoph Sommer Alois Knoll Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Large-scale VANET simulations are computationally intensive. Disolv is a simulation architecture proposed to support city-scale VANET studies. This paper describes software decisions taken to realize a concrete implementation of Disolv. We describe a common workflow and provide guidelines on best utilizing Disolv. Finally, a small experiment demonstrates the performance gains by comparing the execution times with state-of-the-art VANET simulators.

关键词： Computational modeling Vehicular ad hoc networks computer architecture Performance gain Software Guidelines

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Cost-Effective Network Packet Manipulation and Error-Detection Device

Cost-Effective Network Packet Manipulation and Error-Detecti...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alexander F. K. Janus Steffen Moser Frank Slomka Faculty of Engineering Computer Science and Psychology Institute of Embedded System/Real-Time Systems School of Advanced Professional Studies Ulm University Ulm Germany

real-time multimedia communication applications like video conferencing and screen sharing tools are an integral part of our daily communications. These time-critical services are handled via the Internet, a packet-switched and essentially best-effort network, not explicitly designed for robust real-time communication. From a video conferencing service provider's perspective, network issues cause a lot of user complaints due to various reasons: lossy WLAN channels, poorly configured home routers, or crowded carrier-grade network address translation devices. All of these issues have in common that they are often time-variant and non-deterministic. Furthermore, the end-to-end transmission in these highly inhomogeneous networks often suffers from a mixture of more than one error source. To enable a detailed and impartial analysis of multimedia service quality, it is necessary to analyze packets in defective online scenarios and introduce a broadly similar error pattern in an otherwise stable lab network. In this paper, we present SEDER, a hardware-software design of a layer two transparent Ethernet bridge that can be used for two purposes: error detection and error recreation. Implemented on low-cost hardware, the bridge can be easily deployed anywhere to allow debugging, even at a user's site. It is possible to parameterize our bridge to imitate almost all possible network effects, including complex combinations of more than one effect. In addition, we present two case studies, firstly emphasizing the capabilities of SEDER and secondly showing the need to use SEDER to harden open source Video Conferencing Tools against errors.

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