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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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Smell technology: Advancements and Prospects in Digital Scent technology and Fragrance Algorithms

Smell Technology: Advancements and Prospects in Digital Scen...

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International Conference on computing and Networking technology (ICCNT)

作者： Anurag Sinha Vandana Sharma Monika Gupta G. Madhukar Rao Arman Raj Anita Kumari Ahmed Alkhayyat Department of Computer Science and Information Technology IGNOU New Delhi India Institute of Information Technology CHRIST University Department of Physics Rungta College of Engineering and Technology Bhilia CG India Koneru Lakshmaiah Education Foundation India School of Computing Science and Engineering Galgotias University Greater Noida India RKDF University College of Technical Engineering The Islamic University Najaf Iraq

Smell technology, a rapidly expanding sector of the scent business, aims to digitally replicate and transmit aromas. Its applications include virtual reality, e-commerce, and healthcare. Recent advances in the field include the creation of smell algorithms and the use of artificial intelligence to create more realistic fragrances. Fragrance algorithms are mathematical models that predict the scent of a fragrance based on its chemical composition. They might be used to the perfume industry to streamline the production of perfumes and do away with the need for expensive trial-and-error methods. Artificial intelligence is also being used to create digital representations of fragrances that closely resemble the real thing by analysing the chemical composition of actual odours. A possible benefit of this technology in the healthcare sector is that synthetic odours may mimic the scents of diseases and aid physicians in making more precise diagnoses. Additionally, some companies are developing small devices that can be connected to computers or mobile devices to emit odours on demand, providing users of virtual reality, gaming, and online shopping with a more realistic experience. In spite of these advancements, it is still exceedingly challenging to recreate the complexity of natural scents, which can include hundreds of different components. With more research and development, there is still a tonne of promise for fragrance technology in the future.

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Assistive Technical Communication of Montenegrin eServices: a Case Study 27

Assistive Technical Communication of Montenegrin eServices: ...

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27th International Conference on information technology, IT 2023

作者： Simon, Aniko Lakovic, Luka Kovacevic, Pavle Kara, Peter A. Ognjanovic, Ivana Sendelj, Ramo Reich, Christoph Roganovic, Milovan Mantas, John Bokor, Laszlo Sigma Technology Budapest1093 Hungary University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece

ISBN: (纸本)9798350397512

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity. © 2023 IEEE.

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A Prediction of Stock Time Series Data Using Single Layer Long Short-Term Memory

A Prediction of Stock Time Series Data Using Single Layer Lo...

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Data science and Network Security (ICDSNS), International Conference on

作者： Ramy Riad Al-Fatlawy Jayapal Lande S Meenakshi Sundaram N. Naga Saranya Rakesh Podaralla Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India School of Computing and Information Technology Reva university Bangalore India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Financial and cash flow data is a particular kind of past temporal sequence information which gives a huge quantity of data which is employed often in the task including data analysis. For investors and financial professionals, the major question is how to forecast stock time series data. Stock prices forecasting is a challenging task because of time series data volatility, non-linearity and significant noise on the value of stocks. The earlier study only focused on alone parameter stock called close cost, hence, Single Layer Long Short-term Memory (LSTM) model is proposed. This model suggests the contribution as stock information and it predicts two stocks characteristics namely high cost and close cost for the day after. The dataset for Shanghai composite index is utilized for conducting experiments and by the use of existing techniques the comparisons are done. From the results, it clearly shows that proposed single layer LSTM achieved MAE of 26.961, RMSE of 12.112 and R 2 of 0.987 which is better when compared with the existing models.

关键词： Analytical models Costs Time series analysis Noise Predictive models Network security Data models Indexes Forecasting Long short term memory

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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An In-Depth Exploration of Deep Reinforcement Learning Techniques for Game Playing

An In-Depth Exploration of Deep Reinforcement Learning Techn...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： P. Nancy M. Babu A. Devipriya R.Anto Arockia Rosaline M. Ramkumar Prabhu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Department of Information Technology Rajalakshmi Engineering Çollege Thandalam Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

This paper presents a brief survey of the approaches under the DRL umbrella for game playing and a discussion of the problems and the advances that have been witnessed in this promising research line. It is well understandable that the inputs are highly dimensional and the game environment is dynamic and hence the model described above incorporated both CNNs and RNNs. Other components of the methodology were the HRL that started by deconstructing a task into sub-tasks and then the sub-tasks were directed by a master policy. The DRL agents were tested on different platform environments: Specific and steady-build games and platforms and then to evaluate its performance, learning effect, and or its ability to generalize. It was found that the proposed methods were reasonably close to the other effective existing methods; in addition, the time required to train the model was less and the percentage of positive results in the games were also good showing an acceptable generality. The study has also has also extended the prior work on transfer learning as well as the efficient rewarding exploration approach to counter negative impacts of limited rewards and computational time. The data generated by this work contributes to the Doctoral study assessment and is relevant to various DRL themes and can be applied for Robotics and AS fields based on AI. In conclusion, the authors repeat their imperative for future work on expanding DRL approaches that are effective on a large scale, the limitations of application to multi-agent systems, and the transferability of learnt behaviour to improve the AI elements of game playing that is not limited to.

关键词： Training Surveys Recurrent neural networks Transfer learning Games Medical services Deep reinforcement learning Artificial intelligence Robots Multi-agent systems

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Exploring the Effectiveness of Traditional Machine Learning Models in IoT Malicious Traffic Detection

Exploring the Effectiveness of Traditional Machine Learning ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Chong Wang Chang Gao Famei He Songheng He Runshi Liu Qianli Li Wen Chen Xuren Wang College of Information Engineering Capital Normal University Beijing China Library of Beijing Institute of Technology Beijing Institute of Technology Beijing China School of Computing and Information University of Pittsburgh USA Department of Computer Science University of Sheffield Sheffield UK Century College Beijing University of Posts and Telecommunications Beijing China Key Laboratory of experimental physics and Computational Mathematics Beijing China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

As the Internet of Things (IoT) rapidly evolves, security concerns have become increasingly prominent. Machine learning-based network intrusion detection systems can identify and respond to malicious traffic with minimal latency. However, building an effective IoT malicious traffic detection system requires specialized knowledge, and the limited computational resources of IoT devices pose a trade-off between utility and security. Addressing these challenges, this study presents research on an IoT malicious traffic detection model based on traditional machine learning techniques. Compared to other methods, traditional machine learning models have fewer parameters and lower storage requirements. This study optimized the construction process of traditional machine learning models, using feature reduction and recursive feature elimination to minimize the number of features, thereby further reducing model parameters. Bayesian optimization was employed to mitigate performance errors from manual tuning, leading to the selection of the most optimal model. This research focuses on flow-level data classification, specifically selecting flow-level statistical features as model inputs. These features enable the model to conduct detailed analysis of data flows, accurately classifying them into different categories, such as benign traffic, DDoS attacks, and botnets. In experimental evaluations, the model demonstrated high accuracy, with an F1 score exceeding 0.75, validating its effectiveness and reliability in real-world applications.

关键词： Training Computational modeling Network intrusion detection Smart homes Throughput Data models Internet of Things Reliability Tuning Optimization

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Edge Intelligence-Driven Meteorological Knowledge Graph for Real-Time Decision-Making

Edge Intelligence-Driven Meteorological Knowledge Graph for ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zheng Yu Jielin Jiang Bingkun He Muhammad Bilal Dongqing Liu School of Software Nanjing University of Information Science and Technology Nanjing China School of Cyber Science and Engineering Wuhan University Wuhan China School of Computing and Communications Lancaster University Bailrigg United Kingdom Nanjing Meteorological Observatory Nanjing Meteorological Bureau Nanjing China

Meteorological decision-making is a crucial element in the meteorological disaster warning and prevention field. With the increasing frequency of meteorological disasters and the rapid development of edge intelligence, there is an urgent need to establish a meteorological early-warning platform that reduces human resource investment, decreases operating costs, and provides targeted information and response suggestions. Therefore, we propose the development of real-time decision-making based on edge intelligence-driven meteorological knowledge graph (EMKG), and aim to achieve meteorological emergency decision-making by combining the knowledge graph with edge intelligence. First, we collect data through edge devices and perform preprocessing and preliminary analysis on these devices to reduce the time and bandwidth requirements for data transmission to the cloud. Based on the above data, meteorological entity recognition and relation extraction were completed using techniques such as BERT, BiLSTM, CRF, and data augmentation. Then we trained a text generation model and deployed it on edge devices to achieve real-time meteorological decision-making. The experimental results show that EMKG effectively integrates edge intelligence and knowledge graph, and further improves the real-time and accuracy of meteorological decision-making.

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