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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haijun Lei Jia Zhao Guanjie Tong Longjiang Zhang Huaqiang Su Baiying Lei College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Radiology Jinling Hospital Medical School of Nanjing University Shenzhen University Nanjing China School of Biomedical Engineering& Shenzhen University Medical School Shenzhen University Shenzhen China

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment.

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A systematic literature review on phishing and anti-phishing techniques

arXiv

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arXiv 2021年

作者： Arshad, Ayesha Ur Rehman, Attique Javaid, Sabeen Ali, Tahir Muhammad Sheikh, Javed Anjum Azeem, Muhammad Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University of Science and Technology Kuwait

Phishing is the number one threat in the world of internet. Phishing attacks are from decades and with each passing year it is becoming a major problem for internet users as attackers are coming with unique and creative ideas to breach the security. In this paper, different types of phishing and anti-phishing techniques are presented. For this purpose, the Systematic Literature Review(SLR) approach is followed to critically define the proposed research questions. At first 80 articles were extracted from different repositories. These articles were then filtered out using Tollgate Approach to find out different types of phishing and anti-phishing techniques. Research study evaluated that spear phishing, Email Spoofing, Email Manipulation and phone phishing are the most commonly used phishing techniques. On the other hand, according to the SLR, machine learning approaches have the highest accuracy of preventing and detecting phishing attacks among all other anti-phishing approaches. © 2021, CC BY-SA.

关键词： computer crime

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Extended Multi-stream Temporal-attention Module for Skeleton-based Human Action Recognition (HAR)

arXiv

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arXiv 2024年

作者： Mehmood, Faisal Guo, Xin Chen, Enqing Akbar, Muhammad Azeem Khan, Arif Ali Ullah, Sami School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou450001 China Henan Xintong Intelligent IOT Co. Ltd. No. 1-303 Intersection of Ruyun Road and Meihe Road Henan Zhengzhou450007 China Department of Software Engineering LUT University Lahti15100 Finland M3S Empirical Software Engineering Research University of Oulu Oulu90570 Finland Department of Computer Science Government College University Faisalabad Punjab Faisalabad38000 Pakistan

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach;(II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results. © 2024, CC BY.

关键词： Input output programs

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Qian Liu Kangxing Huang Xueliang Wei Yihong Zheng Hao Xiang School of Automation Wuhan University of Technology Wuhan China Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Hongkong China College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Software Development Department Wuhan iActuator Robotics Co. Ltd Wuhan China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability.

关键词： Solid modeling Costs Capacitors Voltage Permanent magnet motors Capacitance Stability analysis Optimization Thermal stability System analysis and design

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Learning by reusing previous advice in teacher-student paradigm 19

Learning by reusing previous advice in teacher-student parad...

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19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Zhu, Changxi Cai, Yi Leung, Ho-Fung Hu, Shuyue School of Software Engineering South China University of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781450375184

Reinforcement Learning (RL) has been widely used to solve sequential decision-making problems. However, RL algorithms suffer from poor sample efficiency and require a long time to learn a suitable policy, especially when multiple agents are learning without prior knowledge. This problem can be alleviated through reusing knowledge from other agents during the learning process. One notable approach is advising actions based on a teacher-student relationship, where the decision of a student agent during learning is aided by an experienced teacher agent. A critical assumption in teacher-student paradigm is that the communication may be limited, so that a student may wait for a while and learn by itself before receiving the next advice. More importantly, in some noisy or stochastic environments, the student may not be able to master the advised actions when they are only performed once. We propose three methods for agents choosing between learning by exploration, asking for advice and reusing previous advice. The results show that our approaches significantly outperform existing advising methods without reusing advice. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

关键词： Reinforcement learning

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A feedforward artificial neural network model for classification and detection of type 2 diabetes 1

A feedforward artificial neural network model for classifica...

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1st International Conference on Recent Trends in Applied Research, ICoRTAR 2020

作者： Frimpong, Enoch A. Oluwasanmi, Ariyo Baagyere, Edward Y. Zhiguang, Qin School of Information and Software Engineering University of Electronic Science and Technology of China No.4 North Jianshe Road Section 2 Chengdu Chengdu610054 China Department of Computer Science University for Development Studies Box TL 1350 Tamale Ghana

Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics;however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks

arXiv

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arXiv 2024年

作者： Wang, Xi Zhao, Xiaotong Wang, Juncheng Li, You Shi, Qingjiang The School of Software Engineering Tongji University Shanghai201804 China The Department of Computer Science Hong Kong Baptist University Hong Kong Huawei Technologies Co. Ltd. Chengdu611730 China The Shenzhen Research Institute of Big Data Shenzhen518172 China

We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of maximum number of receive streams at UEs in practical networks, and formulate a joint beamforming and transmit stream allocation problem for WSR maximization under per-AP transmit power constraints. Since the integer number of transmit streams determines the dimension of the beamformer, the joint optimization problem is mixed-integer and nonconvex with coupled decision variables that is inherently NP-hard. In this paper, we first propose a distributed low-interaction reduced weighted minimum mean square error (RWMMSE) beamforming algorithm for WSR maximization with fixed streams. Our proposed RWMMSE algorithm requires significantly less interaction across the network and has the current lowest computational complexity that scales linearly with the number of transmit antennas, without any compromise on WSR. We draw insights on the joint beamforming and stream allocation problem to decouple the decision variables and relax the mixed-integer constraints. We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem. Simulation results demonstrate substantial performance gain of our proposed algorithms over the current best alternatives in both WSR performance and convergence time. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Mammogram Screening for Breast Density Classification using a soft voting ensemble of Swin Transformers and ConvNext models

Mammogram Screening for Breast Density Classification using ...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Muhammad Hussain Fahman Saeed Mariam Busaleh Hatim Aboalsamh Fazal-e-Amin Department of Computer Science King Saud University Riyadh Saudi Arabia Department of Computer Science King Saud University Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Department of Software Engineering King Saud University Riyadh Saudi Arabia

Breast cancer risk is increased by dense breast tissue. The existing mammographic density classification methods cannot provide sufficient classification accuracy. The classification of breast density is still a challenging issue. In this paper, we propose an ensemble method for improving the accuracy of mammographic breast density classification. The ensemble method uses two views of screening mammography and employs state-of-the-art deep learning-based methods such as Swin Transformer and ConvNeXts model as base learners for local decisions and soft voting to predict the final decision. The method has been thoroughly evaluated on the benchmark DDSM dataset; it achieved an accuracy of 97.61%, a sensitivity of 95.11%, and a specificity of 98.32%. The comparison shows that the method significantly outperforms the state-of-the-art methods for breast density classification. This study provides insight into the application of deep learning to classify breast density.

关键词： Learning systems Sensitivity Breast Predictive models Transformers Feature extraction Delta-sigma modulation

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Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review

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Heliyon 2024年第22期10卷 e40025页

作者： Adewale, Muyideen Dele Azeta, Ambrose Abayomi-Alli, Adebayo Sambo-Magaji, Amina Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Software Engineering Namibia University of Science and Technology Namibia Department of Computer Science Federal University of Agriculture Abeokuta Nigeria Digital Literacy & Capacity Development Department National Information Technology Development Agency Abuja Nigeria

The role of artificial intelligence (AI) in education has been extensively studied, focusing on its ability to enhance learning and teaching processes. However, the precise impact of AI adoption on academic performance in open and distance learning (ODL) remains largely unexplored. This systematic literature review critically evaluates AI's impact on academic performance within ODL environments. Drawing from a curated selection of 64 papers from an initial pool of 700, spanning from 2017 to 2023 and sourced from Scopus, Google Scholar, and Web of science, this study delves into the multifaceted role of AI in enhancing learning outcomes. The meta-analysis reveals a diverse methodological landscape: machine learning methods, employed in 29.69 % of the studies, stand out for their ability to predict academic achievement, which is matched in prevalence by classical statistical methods. Although less common at 3.13 %, hybrid methods are a burgeoning area of research, while a significant 40.63 % of works prioritise nonempirical methods, focusing on theoretical analysis and literature reviews. This investigation highlights the critical factors driving AI adoption in education and its tangible benefits for student performance. It identifies a crucial literature gap: the absence of a process-based framework designed to forecast AI's educational impacts with greater precision, especially across gender and regional lines. By proposing this framework, this study contributes to the academic discourse on AI in education. It underscores the urgent need for structured methodologies to navigate the challenges and opportunities of AI integration. This framework, aligned with UNESCO's 2030 educational objectives, promises to bridge educational divides, ensuring equitable access to quality education across diverse demographics. The findings advocate for future research to design, refine, and test such a framework, paving the way for more inclusive and effective educational technologies

关键词： Academic performance AI in education Artificial intelligence Gender and geographical differences Open and distance learning

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