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arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Closed Shop Scheduling Optimisation using Max-Plus Automata 3

Closed Shop Scheduling Optimisation using Max-Plus Automata

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3rd International Conference on science, ICOS 2019

作者： Merdekawati, Dian Ayu Subiono Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Surabaya Indonesia

An appropriate scheduling system is needed in manufacturing activities to minimize production time and costs. Flow shop is one type of production flow that widely used in manufacturing. Based on the demand criteria, it has been known as a scheduling problem called closed shop. This scheduling emphasizes the make-to-stock (MTS) which produces a stock. We take the closed shop scheduling case studies from an existing literature and optimize scheduling using max-plus automata optimum. We compare the result with regular scheduling and the previous result in the literature. We show that the result of max-plus automata optimum scheduling are optimum compared than the existing regular scheduling and the existing previous result of the literature. © Published under licence by IOP Publishing Ltd.

关键词： Scheduling

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Deep Learning Algorithms-based Object Detection and Localization Revisited

Deep Learning Algorithms-based Object Detection and Localiza...

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International Laser Technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Waheed, Safa Riyadh Suaib, Norhaida Mohd Mohd Rahim, Mohd Shafry Mundher Adnan, Myasar Salim, A.A. Big Data Center and Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq UTM-IRDA Digital Media Institute of Human-Centred Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Islamic University Najaf Iraq Laser Center and Physics Department Faculty of Science Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

arXiv

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arXiv 2023年

作者： Islam, Md Shofiqul Hasan, Khondokar Fida Sultana, Sunjida Uddin, Shahadat Lio', Pietro Quinn, Julian M.W. Moni, Mohammad Ali Faculty of Computing Universiti Malaysia Pahang Gambang Pahang Kuantan26300 Malaysia Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia 2 George Street Brisbane4000 Australia Department of Computer Science and Engineering Islamic University Kushtia7600 Bangladesh School of Project Management Faculty of Engineering The University of Sydney Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Bone Research Group The Garvan Institute of Medical Research DarlinghurstNSW Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise. © 2023, CC BY.

关键词： Electrocardiograms

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Interaction of doxorubicin with carbon nanotubes in the capillaries surrounding cancer tumors using molecular dynamics simulation: The impact of pH on the thermal properties

引用

Results in Chemistry 2025年 16卷

作者： Zheoat, Ahmed Mohammed Attyah Al Luaibi, Abeer Issa Mohammed Darraji, Raad Kareem Alwan Al Singh, Narinderjit Singh Sawaran Salahshour, Soheil Sajadi, S. Mohammad Hekmatifar, M. Al-Manara College for Medical Sciences Department of Pharmacy Maysan Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Chemistry Payam e Noor University Saqqez Branch Kurdistan Saqqez Iran Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Environmental factors, including pH, can affect the efficacy of doxorubicin, a chemotherapeutic drug that is frequently used. This study employs molecular dynamics modeling to investigate the correlation between doxorubicin and carbon nanotubes in the capillaries surrounding malignant tumors. After a period of 10 ns, the system reaches to equilibrium when the kinetic and potential energies are stabilized at 0.93 kcal/mol and 5.68 kcal/mol, respectively. The maximum density increases from 0.0033 to 0.0036 atm/Å³ as the pH increased from 3 to 11. Conversely, the shear tension decreases from 3.25 to 3.11 Pa, and the maximum temperature decreases from 391.91 to 368.77 K. The enhancement in drug stability and minimal degradation under physiological conditions was demonstrated by the decrease in temperature and shear stress that occurred with an increase in pH. The root mean square deviation and mean squared displacement also suggested that structural stability was improved at higher pH levels. This work facilitated the development of pH-responsive drug delivery devices, which improved drug stability and facilitated the controlled release of pharmaceuticals at physiological pH. These results may have a direct impact on the development of more effective cancer medications, particularly in the form of pH-sensitive drug delivery systems. This study facilitated the development of personalized therapies that could improve the stability and controlled release of chemotherapeutic medications throughout the body by regulating the interactions between doxorubicin and carbon nanotubes, thereby paving the way for future clinical research. This method had the potential to enhance the precision of drug administration, mitigate adverse effects, and enhance therapeutic outcomes in the treatment of cancer. © 2025 The Authors

关键词： Atomic behavior Carbon nanotubes Drug doxorubicin Molecular dynamics simulation pH level Physical behavior

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Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

arXiv

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arXiv 2022年

作者： Wang, Xiangmeng Li, Qian Yu, Dianer Cui, Peng Wang, Zhichao Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia The School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia The Department of Computer Science and Technology Tsinghua University Beijing100084 China

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model. © 2022, CC BY.

关键词： Semantics

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Higher-order rich-club phenomenon in collaborative research grant networks

arXiv

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

关键词：

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A Multimodal Medical Image Fusion Method Incorporating an Adaptive Attention Mechanism

A Multimodal Medical Image Fusion Method Incorporating an Ad...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yingxian Zhang Yushui Geng Hu Liang Hai Zhong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China Department of Radiology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Multimodal medical image fusion technology provides more comprehensive and accurate image support for clinical diagnosis and treatment by integrating complementary information from different imaging modalities. Aiming at the problem that existing methods are still insufficient in detail feature extraction and inter-modal information fusion, this paper proposes a multimodal medical image fusion method combined with an adaptive attention mechanism. First, we design the Grouped Receptive Field Attentional Convolution (GRFAConv) to solve the problem of insufficient detail feature extraction capability. With the multi-head receptive field adaptive weighting strategy of grouped convolution, the range and weight of the receptive field of the convolution kernel can be adaptively adjusted according to the different demands of local and global features of the image to improve the effect of detail retention. Second, for the problem of information fusion between different modalities, we introduce an improved CBAM attention module in the feature fusion process, which adaptively selects and enhances the features in the key regions through the channel attention and spatial attention mechanisms, which greatly improves the clarity of the fused image details and the accuracy of the information expression in the key regions. Furthermore, experimental results on several medical image datasets show that the algorithm proposed in this paper can generate relatively high-quality fused images. It not only enriches the detailed features of the image, but also achieves significant advantages in several evaluation metrics.

关键词： Measurement Attention mechanisms Accuracy Convolution Federated learning Feature extraction Clinical diagnosis Kernel Medical diagnostic imaging Image fusion

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Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction

arXiv

引用

arXiv 2023年

作者： Yong, Suk Yee Ong, Cheng Soon Australia Astrophysics and Space Technologies Research Centre Macquarie University SydneyNSW2109 Australia School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia Machine Learning and Artificial Intelligence Future Science Platform CSIRO Australia Data61 CSIRO CanberraACT2601 Australia School of Computing The Australian National University CanberraACT2601 Australia

Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjected to biases and uncertainties as it is reliant on the scaling relation from a small sample of local active galactic nuclei. In this study, we propose the application of conformalised quantile regression (CQR) to quantify the uncertainties of the black hole predictions in a machine learning setting. We compare CQR with various prediction interval techniques and demonstrated that CQR can provide a more useful prediction interval indicator. In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties. That is it yields a tighter constraint on the prediction interval (hence more certain) for a larger black hole mass, and accordingly, bright and broad spectral line width source. Using a combination of neural network model and CQR framework, the recovered virial black hole mass predictions and uncertainties are comparable to those measured from the Sloan Digital Sky Survey. The code is publicly available at https://***/yongsukyee/uncertain_blackholemass. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Incentive-Driven Federated Learning for Collaborative Agricultural Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Zheng, Xiao Tahir, Muhammad Anwar, Muhammad Shahid Tang, Yongwei Ahmad, Sadique ShanDong University of Technology School of Computer Science and Technology Zibo 255000 China Defense View Faculty of Engineering Science and Technology(FEST) Iqra University Departmentof Computer Science Main Campus Defense View Sindh Karachi 75500 Pakistan Department of Ai and Software Gachon University Seongnam-si 13120 South Korea Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Prince Sultan University EIAS: Data Science and Blockchain Laboratory College of Computer and Information Sciences Riyadh 11586 Saudi Arabia

To address the challenges of data privacy protection and collaborative management in agricultural consumer electronics (ACE) devices, this study proposes a distributed data processing framework based on federated learning (FL). The framework employs a three-tier system architecture comprising a base station (BS), edge devices that train models using local data, and a BS that aggregates these models to generate a global one. This iterative process establishes a paradigm that balances privacy preservation and performance optimization. Additionally, we design an incentive mechanism based on auction theory to simulate the buyer-seller interaction between BSs and edge devices. In this mechanism, devices submit bids according to their minimum energy requirements. To maximize resource allocation utility, we propose a greedy auction algorithm that satisfies multiple economic properties. Simulation results demonstrate that the algorithm not only guarantees these properties but also enhances system utility and efficiency. © 2025 IEEE.

关键词： Agricultural Consumer Electronic Auction Game Federated Learning (FL) Incentive Mechanisms Resource Optimisation

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