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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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Health-Care Monitoring of Patient using CNN based Model in Internet of Things 1

Health-Care Monitoring of Patient using CNN based Model in I...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Parameshachari, B.D. Hemalatha, K.L. Rahebi, Javad Baker, Mohammed Rashad Upendra Roy, B.P. Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Istanbul Topkapi University Department of Software Engineering Turkey University of Kirkuk College of Computer Science and Information Technology Department of Software Kirkuk Iraq Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350323795

There is rising public concern about exposure to radiofrequency (RF) electromagnetic fields (EMF) as more and more wireless communications become concentrated in everyday living surroundings. There are a number of obstacles, primarily originating from infrastructure expenses, but recent technology breakthroughs are shifting attention to Internet of Things (IoT) devices that enable automatic and continuous real-time EMF monitoring. The Internet of Things (IoT) has made the world a better place by expanding the capabilities of telemedicine and allowing for more precise remote monitoring of patients. Improved healthcare facilities for both patients and doctors/hospitals are only one way that IoT is making a significant impact in the medical field. Five main parts make up the proposed system: patient data gathering, primary report creation, hospital care, pharmacy care, and diagnostics. This study presents an Internet of Things (IoT)-based healthcare scheme accomplished of real-time nursing of energetic signs and environmental factors for discrete patients. Five sensors, including a heart-rate monitor, a body temperature monitor, a room temperature monitor, a carbon monoxide monitor, and a carbon dioxide monitor, are utilised to collect data from the hospital setting. Medical personnel receive patient updates through a portal and use this data for further diagnosis and care. The success of the system demonstrates that the built prototype is ideal for healthcare monitoring. This study has investigated unique approaches to the automatic construction of convolutional neural network (CNN) topologies, and it has also developed a new method for diagnosis. The results of the trials prove that the recommended algorithm achieves improved than the state-of-the-art techniques. © 2023 IEEE.

关键词： Diagnosis

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Research on Inverted Pendulum Control Strategy Based on Deep Reinforcement Learning 3

Research on Inverted Pendulum Control Strategy Based on Deep...

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3rd International Conference on Electrical engineering and Control science, IC2ECS 2023

作者： Zheng, Jiehong Wang, Xinzui Yang, Yanli Jihua Medical Institute Jihua Laboratory Foshan528251 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou215163 China

ISBN: (纸本)9798350382426

Aiming at the control planning of inverted pendulum task, the planning strategy based on Soft Actor-Critic (SAC) algorithm was studied. An agent based on Actor-Critic framework is designed, which takes inverted pendulum state as input and planned target position as output as controller. The agent has a total of 5 neural networks, namely, action network actor, 2 current evaluation networks and 2 target evaluation networks. Actor network outputs the next planned position according to the current inverted pendulum state, and the current Critic network and target Critic network respectively output the value evaluation of the current action and the next action according to the reward feedback from the environment. The PD control method is used to convert the planned target position of the mobile network output into the control voltage value, and actually control the inverted swing. Simulation results show that the proposed method is effective and efficient. © 2023 IEEE.

关键词： deep reinforcement learning motion control neural network rotary inverted pendulum Soft Actor-Critic algorithm

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Enhancing Sales Forecasting Accuracy in the Presence of Missing Data

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Jun Tang Jingpeng Sun Bing Guo Yan Shen Shengxin Dai Peng Wang College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai UAE School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. The proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios.

关键词： Interpolation Accuracy Computational modeling Predictive models Network architecture Feature extraction Data models Stability analysis Convolutional neural networks Forecasting

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Anomaly Detection in Spatial Information Networks: A Security Knowledge Graph-based Approach

Anomaly Detection in Spatial Information Networks: A Securit...

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Intelligent Computing and Robotics (ICICR), International Conference on

作者： Yayue Tan Yaqi Zhang Hui Liu Yongqing Tang College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network security-oriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience.

关键词： Knowledge engineering Convolution Network topology Knowledge graphs Market research computer security Anomaly detection

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Improve Robustness of Safe Reinforcement Learning Against Adversarial Attacks

Improve Robustness of Safe Reinforcement Learning Against Ad...

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Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Xiaoyan Wang Yujuan Zhang Lan Huang College of Computer Science and Technology Jilin University Changchun China Software Engineering Jilin University Changchun China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Models trained with adversarial attack can be significantly improved stability and performance when faced with new uncertain environment. In this paper, we propose the robust training framework based on Wasserstein SA-MDP for safe RL, which combines ideas from reinforcement learning, adversarial examples generation, Wasserstein metric and robust training. This framework can improve the robustness of safe RL models against adversarial attacks by considering the dynamics of the adversarial interaction as a sequential decision-making process. We made the experiment in safety gym based on the proposed framework and report the experimental results.

关键词： Training Measurement computer science Costs Decision making Reinforcement learning Robustness Stability analysis Safety Proposals

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing*

Group Signature with Time-Bound Keys for Secure E-health Rec...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junze Lu Chunqiang Hu Conghao Ruan Bin Cai Tao Xiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient.

关键词： Buildings Solids Security Time factors Internet of Things Bioinformatics

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Repeated Padding for Sequential Recommendation

arXiv

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arXiv 2024年

作者： Dang, Yizhou Liu, Yuting Yang, Enneng Guo, Guibing Jiang, Linying Wang, Xingwei Zhao, Jianzhe Software College Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences;2) Batch-based training needs to ensure that the sequences in each batch have the same length. The special value 0 is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has never been explored in the recommendation field: Can we utilize this idle input space by padding other content to improve model performance and training efficiency further? In this paper, we propose a simple yet effective padding method called Repeated Padding (RepPad). Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training. This operation can be performed a finite number of times or repeated until the input sequences’ length reaches the maximum limit. Our RepPad can be considered as a sequence-level data augmentation strategy. Unlike most existing works, our method contains no trainable parameters or hyperparameters and is a plug-and-play data augmentation operation. Extensive experiments on various categories of sequential models and five real-world datasets demonstrate the effectiveness and efficiency of our approach. The average recommendation performance improvement is up to 60.3% on GRU4Rec and 24.3% on SASRec. We also provide in-depth analysis and explanation of what makes RepPad effective from multiple perspectives. Our datasets and codes are available at https://***/KingGugu/RepPad. © 2024, CC BY.

关键词： Recommender systems

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Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Su, Huaqiang Lei, Haijun Chen, Guoliang Chen, Xin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Guangzhou First People's Hospital Department of Radiology Guangzhou China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China

ISBN: (纸本)9798350337488

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network's ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16. © 2023 IEEE.

关键词： Diffusion probability model feature fusion module medical segmentation mutual graph learning network

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ALQ: An Efficient Active Learning Recommendation Algorithm

ALQ: An Efficient Active Learning Recommendation Algorithm

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Ao Song Zhou Lei Shengbo Chen College of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem.

关键词： Training Costs Transforms Classification algorithms Internet Information management Labeling

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