Miao Liao, Zhiwei Chi, Huizhu Wu, S. Di, Yonghua Hu, Yunyi Li
Early detection of lung nodules is an important means of reducing the lung cancer mortality rate. In this paper, we propose a three-dimensional CT image lung nodule detection method based on parallel pooling and dense blocks, which includes two parts, i.e., candidate nodule extraction and false positive suppression. First, a dense U-shaped backbone network with parallel pooling is proposed to obtain the candidate nodule probability map. The parallel pooling structure uses multiple pooling operations for downsampling to capture spatial information comprehensively and address the problem of information loss resulting from maximum and average pooling in the shallow layers. Then, a parasitic network with parallel pooling, dense blocks, and attention modules is designed to suppress false positive nodules. The parasitic network takes the multiscale feature maps of the backbone network as the input. The experimental results demonstrate that the proposed method significantly improves the accuracy of lung nodule detection, achieving a CPM score of 0.91, which outperforms many existing methods.
早期发现肺结节是降低肺癌死亡率的重要手段。本文提出了一种基于并行池化和密集块的三维 CT 图像肺结节检测方法，包括候选结节提取和假阳性抑制两部分。首先，提出了并行池化的密集 U 型骨干网络，以获得候选结节概率图。并行池化结构利用多次池化操作进行下采样，全面捕捉空间信息，解决了浅层最大池化和平均池化导致的信息丢失问题。然后，设计了一个包含并行池化、密集块和注意力模块的寄生网络来抑制假阳性结节。寄生网络将骨干网络的多尺度特征图作为输入。实验结果表明，所提出的方法显著提高了肺结节检测的准确性，CPM 得分为 0.91，优于许多现有方法。
{"title":"Pulmonary Nodule Detection from 3D CT Image with a Two-Stage Network","authors":"Miao Liao, Zhiwei Chi, Huizhu Wu, S. Di, Yonghua Hu, Yunyi Li","doi":"10.1155/2023/3028869","DOIUrl":"https://doi.org/10.1155/2023/3028869","url":null,"abstract":"Early detection of lung nodules is an important means of reducing the lung cancer mortality rate. In this paper, we propose a three-dimensional CT image lung nodule detection method based on parallel pooling and dense blocks, which includes two parts, i.e., candidate nodule extraction and false positive suppression. First, a dense U-shaped backbone network with parallel pooling is proposed to obtain the candidate nodule probability map. The parallel pooling structure uses multiple pooling operations for downsampling to capture spatial information comprehensively and address the problem of information loss resulting from maximum and average pooling in the shallow layers. Then, a parasitic network with parallel pooling, dense blocks, and attention modules is designed to suppress false positive nodules. The parasitic network takes the multiscale feature maps of the backbone network as the input. The experimental results demonstrate that the proposed method significantly improves the accuracy of lung nodule detection, achieving a CPM score of 0.91, which outperforms many existing methods.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"118 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. K. Alzahrani, A. Alsheikhy, T. Shawly, Mohammad Barr, Hossam E. Ahmed
Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep-learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease based on a deep-learning tool (DLT). The developed DLT is designed using a UNET architecture. The proposed approach is evaluated for different performance quantities on a dataset and provides promising results. An average obtained accuracy ranged between 82% and 87% with around 86% of the F-score. The obtained outcomes can open the door to applying DLMs to predict and identify ALS disease.
目前，肌萎缩性脊髓侧索硬化症（ALS）被认为是一种致命疾病，因为它会影响中枢神经系统，而且没有治愈或明确的治疗方法。这种疾病会影响脊髓，特别是大脑内的下运动神经元（LMN）和上运动神经元（UMN）及其网络。目前已开发出多种预测 ALS 的解决方案。其中一些解决方案是利用不同的深度学习方法（DLM）实现的。然而，这种疾病被认为是一项艰巨的任务和巨大的挑战。本文提出了一种基于深度学习工具（DLT）的预测 ALS 疾病的可靠模型。开发的 DLT 采用 UNET 架构设计。本文针对数据集上的不同性能量对所提出的方法进行了评估，结果令人鼓舞。平均准确率在 82% 到 87% 之间，F-score 约为 86%。这些结果为应用 DLM 预测和识别 ALS 疾病打开了大门。
{"title":"A New Artificial Intelligence-Based Model for Amyotrophic Lateral Sclerosis Prediction","authors":"A. K. Alzahrani, A. Alsheikhy, T. Shawly, Mohammad Barr, Hossam E. Ahmed","doi":"10.1155/2023/1172288","DOIUrl":"https://doi.org/10.1155/2023/1172288","url":null,"abstract":"Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep-learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease based on a deep-learning tool (DLT). The developed DLT is designed using a UNET architecture. The proposed approach is evaluated for different performance quantities on a dataset and provides promising results. An average obtained accuracy ranged between 82% and 87% with around 86% of the F-score. The obtained outcomes can open the door to applying DLMs to predict and identify ALS disease.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"121 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139134781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hongwei Feng, Yuanyuan Liu, Conggui Huang, Linbo Xie, Bin Qi
In wireless power transfer (WPT) systems, inverters are used to achieve high-frequency conversion of DC/AC, and their conversion efficiency and working frequency are key factors affecting the system’s power transfer efficiency. In practical applications, many hardware issues, such as power transistor shutdown and loss, are the main reasons that affect the inverter conversion efficiency. On the other hand, the working frequency of WPT systems ranges from hundreds of kHz to a few MHz, and traditional voltage and current phasor estimation requires a very high sampling rate which is difficult to achieve. To overcome these limitations, this paper introduces a phase-shifting full bridge inverter using a zero-voltage switching (ZVS) soft switching technology to optimize the conversion efficiency of the inverter. Meanwhile, apparent power is introduced to detect the operating frequency and phase angle. Combined with an FPGA soft switching control strategy, this approach allows for the quick adjustment of the driving pulse of MOS transistors, as well as the voltage and current at the transmitting end, to a completely symmetrical state in real-time, effectively suppressing frequency offset and achieving efficient frequency tracking control and maximum efficiency tracking (MET) control of the WPT system. Through simulation and experiments, the ZVS soft switching technology has been achieved with the inverter control strategy, leading to improved conversion efficiency. The frequency offset that can be corrected can reach 0.1 Hz using the apparent power detection method, and the maximum transfer efficiency of the WPT system can reach 91%.
{"title":"Real-Time Frequency Adaptive Tracking Control of the WPT System Based on Apparent Power Detection","authors":"Hongwei Feng, Yuanyuan Liu, Conggui Huang, Linbo Xie, Bin Qi","doi":"10.1155/2023/1390828","DOIUrl":"https://doi.org/10.1155/2023/1390828","url":null,"abstract":"In wireless power transfer (WPT) systems, inverters are used to achieve high-frequency conversion of DC/AC, and their conversion efficiency and working frequency are key factors affecting the system’s power transfer efficiency. In practical applications, many hardware issues, such as power transistor shutdown and loss, are the main reasons that affect the inverter conversion efficiency. On the other hand, the working frequency of WPT systems ranges from hundreds of kHz to a few MHz, and traditional voltage and current phasor estimation requires a very high sampling rate which is difficult to achieve. To overcome these limitations, this paper introduces a phase-shifting full bridge inverter using a zero-voltage switching (ZVS) soft switching technology to optimize the conversion efficiency of the inverter. Meanwhile, apparent power is introduced to detect the operating frequency and phase angle. Combined with an FPGA soft switching control strategy, this approach allows for the quick adjustment of the driving pulse of MOS transistors, as well as the voltage and current at the transmitting end, to a completely symmetrical state in real-time, effectively suppressing frequency offset and achieving efficient frequency tracking control and maximum efficiency tracking (MET) control of the WPT system. Through simulation and experiments, the ZVS soft switching technology has been achieved with the inverter control strategy, leading to improved conversion efficiency. The frequency offset that can be corrected can reach 0.1 Hz using the apparent power detection method, and the maximum transfer efficiency of the WPT system can reach 91%.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"78 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139155024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.
{"title":"Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension","authors":"Yaru Zhao, Bo Cheng, Yakun Huang, Zhiguo Wan","doi":"10.1155/2023/9267487","DOIUrl":"https://doi.org/10.1155/2023/9267487","url":null,"abstract":"Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"15 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139161569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing variety and quantity of end-of-life (EOL) products, the traditional disassembly process has become inefficient. In response to this phenomenon, this article proposes a random multiproduct U-shaped mixed-flow incomplete disassembly line balancing problem (MUPDLBP). MUPDLBP introduces a mixed disassembly method for multiple products and incomplete disassembly method into the traditional DLBP, while considering the characteristics of U-shaped disassembly lines and the uncertainty of the disassembly process. First, mixed-flow disassembly can improve the efficiency of disassembly lines, reducing factory construction and maintenance costs. Second, by utilizing the characteristics of incomplete disassembly to reduce the number of dismantled components and the flexibility and efficiency of U-shaped disassembly lines in allocating disassembly tasks, further improvement in disassembly efficiency can be achieved. In addition, this paper also addresses the characteristics of EOL products with heavy weight and high rigidity. While retaining the basic settings of MUPDLBP, the stability of the assembly during the disassembly process is considered, and a new problem called MUPDLBP_S, which takes into account the disassembly stability, is further proposed. The corresponding mathematical model is provided. To obtain high-quality disassembly plans, a new and improved algorithm called INSGAII is proposed. The INSGAII algorithm uses the initialization method based on Monte Carlo tree simulation (MCTI) and the Group Global Crowd Degree Comparison (GCDC) operator to replace the initialization method and crowding distance comparison operator in the NSGAII algorithm, effectively improving the coverage of the initial population individuals in the entire solution space and the evenness and spread of the Pareto front. Finally, INSGAII’s effectiveness has been affirmed by tackling both current disassembly line balancing problems and the proposed MUPDLBP and MUPDLBP_S. Importantly, INSGAII outshines six comparison algorithms with a top rank of 1 in the Friedman test, highlighting its superior performance.
随着报废（EOL）产品的种类和数量不断增加，传统的拆卸流程变得效率低下。针对这一现象，本文提出了随机多产品 U 型混流不完全拆卸线平衡问题（MUPDLBP）。MUPDLBP 将多产品混流拆卸法和不完全拆卸法引入传统的 DLBP，同时考虑了 U 形拆卸线的特点和拆卸过程的不确定性。首先，混流式拆卸可以提高拆卸线的效率，降低工厂建设和维护成本。其次，利用不完全拆卸的特点减少拆卸部件的数量，以及 U 型拆卸线在分配拆卸任务时的灵活性和高效性，可以进一步提高拆卸效率。此外，本文还针对 EOL 产品重量大、刚度高的特点进行了探讨。在保留 MUPDLBP 基本设置的同时，考虑了拆卸过程中装配的稳定性，并进一步提出了考虑拆卸稳定性的新问题 MUPDLBP_S。并提供了相应的数学模型。为了获得高质量的拆卸计划，提出了一种名为 INSGAII 的改进算法。INSGAII 算法采用基于蒙特卡洛树模拟（MCTI）的初始化方法和群组全局拥挤度比较（GCDC）算子，取代了 NSGAII 算法中的初始化方法和拥挤距离比较算子，有效提高了初始种群个体在整个解空间的覆盖率以及帕累托前沿的均匀性和扩散性。最后，INSGAII 在处理当前的拆卸线平衡问题以及所提出的 MUPDLBP 和 MUPDLBP_S 时的有效性得到了肯定。重要的是，INSGAII 超越了六种比较算法，在 Friedman 测试中排名第一，彰显了其卓越的性能。
{"title":"A New Pareto Discrete NSGAII Algorithm for Disassembly Line Balance Problem","authors":"ZhenYu Xu, Yong Han, ZhenXin Li, YiXin Zou, YuWei Chen","doi":"10.1155/2023/8847164","DOIUrl":"https://doi.org/10.1155/2023/8847164","url":null,"abstract":"With the increasing variety and quantity of end-of-life (EOL) products, the traditional disassembly process has become inefficient. In response to this phenomenon, this article proposes a random multiproduct U-shaped mixed-flow incomplete disassembly line balancing problem (MUPDLBP). MUPDLBP introduces a mixed disassembly method for multiple products and incomplete disassembly method into the traditional DLBP, while considering the characteristics of U-shaped disassembly lines and the uncertainty of the disassembly process. First, mixed-flow disassembly can improve the efficiency of disassembly lines, reducing factory construction and maintenance costs. Second, by utilizing the characteristics of incomplete disassembly to reduce the number of dismantled components and the flexibility and efficiency of U-shaped disassembly lines in allocating disassembly tasks, further improvement in disassembly efficiency can be achieved. In addition, this paper also addresses the characteristics of EOL products with heavy weight and high rigidity. While retaining the basic settings of MUPDLBP, the stability of the assembly during the disassembly process is considered, and a new problem called MUPDLBP_S, which takes into account the disassembly stability, is further proposed. The corresponding mathematical model is provided. To obtain high-quality disassembly plans, a new and improved algorithm called INSGAII is proposed. The INSGAII algorithm uses the initialization method based on Monte Carlo tree simulation (MCTI) and the Group Global Crowd Degree Comparison (GCDC) operator to replace the initialization method and crowding distance comparison operator in the NSGAII algorithm, effectively improving the coverage of the initial population individuals in the entire solution space and the evenness and spread of the Pareto front. Finally, INSGAII’s effectiveness has been affirmed by tackling both current disassembly line balancing problems and the proposed MUPDLBP and MUPDLBP_S. Importantly, INSGAII outshines six comparison algorithms with a top rank of 1 in the Friedman test, highlighting its superior performance.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"31 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article discusses issues with disturbance rejection and periodic signal tracking in a specific type of time-varying delay nonlinear systems. The proposed approach, known as the modified repetitive controller (MRC) scheme, utilizes an equivalent-input-disturbance (EID) estimator to enhance the system’s performance. It effectively improves the system’s ability to reject both aperiodic and periodic unknown disturbances, while also achieving accurate tracking of periodic reference signals. A T-S fuzzy model has been used to roughly represent the system nonlinearity. Additionally, a fuzzy state observer based on an adaptive periodic event-triggered mechanism (APETM-FSO) has been used to decrease data transfer, energy use, and communication resource utilization. The APETM is able to identify the occurrence of an event by surpassing a predetermined threshold with the error signal, thanks to the designed adaptive event triggering condition. Transmission of the current data only takes place when the event happens, while data can remain unchanged using a zero-order hold if the event does not occur. In addition to, controller parameters are tuned using a particle swarm optimization (PSO) approach. Hence, T-S fuzzy model-based EID, MRC, FSO-APETM, and PSO construct the overall system. In order to ensure the asymptotic stability of the entire system in the presence of unknown disturbances, the article establishes sufficient conditions using the Lyapunov–Krasovskii functional stability theory and linear matrix inequalities (LMIs). These conditions are derived to guarantee the desired stability properties of the system. To demonstrate the effectiveness and feasibility of the proposed scheme, simulation results with comparative study are presented. The proposed controller has achieved better tracking performance with less tracking error with maximum value of 0.05. In addition, the suggested APETM has minimum triggering times which is 34 as comparison with PETM which is 40 times, and hence, APETM is more effective than PETM in reducing data transmission frequency and using less communication resources overall.
{"title":"Design of Adaptive Periodic Event-Triggered Mechanism-Based EID with MRC Based on PSO Algorithm for T-S Fuzzy Systems","authors":"Mohamed Soliman, M. Gulzar, Adnan Shakoor","doi":"10.1155/2023/6957327","DOIUrl":"https://doi.org/10.1155/2023/6957327","url":null,"abstract":"This article discusses issues with disturbance rejection and periodic signal tracking in a specific type of time-varying delay nonlinear systems. The proposed approach, known as the modified repetitive controller (MRC) scheme, utilizes an equivalent-input-disturbance (EID) estimator to enhance the system’s performance. It effectively improves the system’s ability to reject both aperiodic and periodic unknown disturbances, while also achieving accurate tracking of periodic reference signals. A T-S fuzzy model has been used to roughly represent the system nonlinearity. Additionally, a fuzzy state observer based on an adaptive periodic event-triggered mechanism (APETM-FSO) has been used to decrease data transfer, energy use, and communication resource utilization. The APETM is able to identify the occurrence of an event by surpassing a predetermined threshold with the error signal, thanks to the designed adaptive event triggering condition. Transmission of the current data only takes place when the event happens, while data can remain unchanged using a zero-order hold if the event does not occur. In addition to, controller parameters are tuned using a particle swarm optimization (PSO) approach. Hence, T-S fuzzy model-based EID, MRC, FSO-APETM, and PSO construct the overall system. In order to ensure the asymptotic stability of the entire system in the presence of unknown disturbances, the article establishes sufficient conditions using the Lyapunov–Krasovskii functional stability theory and linear matrix inequalities (LMIs). These conditions are derived to guarantee the desired stability properties of the system. To demonstrate the effectiveness and feasibility of the proposed scheme, simulation results with comparative study are presented. The proposed controller has achieved better tracking performance with less tracking error with maximum value of 0.05. In addition, the suggested APETM has minimum triggering times which is 34 as comparison with PETM which is 40 times, and hence, APETM is more effective than PETM in reducing data transmission frequency and using less communication resources overall.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139232927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hui Tian, Weiping Ye, Jia Wang, Hanyu Quan, Chin-Chen Chang
In the context of healthcare 4.0, cloud-based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper-proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy-preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state-of-the-art ones.
{"title":"Certificateless Public Auditing for Cloud-Based Medical Data in Healthcare Industry 4.0","authors":"Hui Tian, Weiping Ye, Jia Wang, Hanyu Quan, Chin-Chen Chang","doi":"10.1155/2023/3375823","DOIUrl":"https://doi.org/10.1155/2023/3375823","url":null,"abstract":"In the context of healthcare 4.0, cloud-based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper-proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy-preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state-of-the-art ones.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youming Ge, Zitong Chen, Weiyang Kong, Yubao Liu, Raymond Chi-Wing Wong, Sen Zhang
The computation of a group Steiner tree (GST) in various types of graph networks, such as social network and transportation network, is a fundamental graph problem in graphs, with important applications. In these graphs, time is a common and necessary dimension, for example, time information in social network can be the time when a user sends a message to another user. Graphs with time information can be called temporal graphs. However, few studies have been conducted on GST in terms of temporal graphs. This study analyzes the computation of GST for temporal graphs, i.e., the computation of temporal GST (TGST), which is shown to be an NP-hard problem. We propose an efficient solution based on a dynamic programming algorithm for our problem. This study adopts new optimization techniques, including graph simplification, state pruning, and A ∗ search, are adopted to dramatically reduce the algorithm search space. Moreover, we consider three extensions for our problem, namely the TGST with unspecified tree root, the progressive search of TGST, and the top-N search of TGST. Results of the experimental study performed on real temporal networks verify the efficiency and effectiveness of our algorithms.
{"title":"An Efficient Dynamic Programming Algorithm for Finding Group Steiner Trees in Temporal Graphs","authors":"Youming Ge, Zitong Chen, Weiyang Kong, Yubao Liu, Raymond Chi-Wing Wong, Sen Zhang","doi":"10.1155/2023/1974161","DOIUrl":"https://doi.org/10.1155/2023/1974161","url":null,"abstract":"The computation of a group Steiner tree (GST) in various types of graph networks, such as social network and transportation network, is a fundamental graph problem in graphs, with important applications. In these graphs, time is a common and necessary dimension, for example, time information in social network can be the time when a user sends a message to another user. Graphs with time information can be called temporal graphs. However, few studies have been conducted on GST in terms of temporal graphs. This study analyzes the computation of GST for temporal graphs, i.e., the computation of temporal GST (TGST), which is shown to be an NP-hard problem. We propose an efficient solution based on a dynamic programming algorithm for our problem. This study adopts new optimization techniques, including graph simplification, state pruning, and A ∗ search, are adopted to dramatically reduce the algorithm search space. Moreover, we consider three extensions for our problem, namely the TGST with unspecified tree root, the progressive search of TGST, and the top-N search of TGST. Results of the experimental study performed on real temporal networks verify the efficiency and effectiveness of our algorithms.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The vibration signal is easily interfered by noise due to the influence of environment and other factors, which can lead to the poor adaptability, low accuracy of remaining useful life (RUL) prediction, and other problems. To solve this problem, this paper proposes a novel RUL prediction method, which is based on multiscale stacking deep residual shrinkage network (MSDRSN). MSDRSN combines the ability of stacking in improving prediction accuracy and the advantages of deep residual shrinkage network (DRSN) in denoising. First, cumulative sum (CUSUM) from statistics is used to divide the full life cycle of the rolling bearings and discover the points of failure. Second, stacking is used for feature learning on the raw data, multiple convolutional kernels of different scales are selected as base-learners, and fully connected neural networks are selected as meta-learners for feature fusion and learning. Then, DRSN is used to do prediction, and the acquired results are fitted with Savitzky–Golay (SG) smoothing. Finally, the effectiveness of the proposed method is proved by the IEEE PHM 2012 data challenge dataset. Compared with the multiscale convolutional neural network with fully connected layer (MSCNN-FC) and the bidirectional long short-term memory (BiLSTM) for RUL prediction under the noise. Using the proposed method, the mean absolute error (MSE) of the best result is 0.002 and the mean square error (MSE) is 0.014; meanwhile, the coefficient of determination (R2) of the best prediction result can reach 97.6%. It is also compared with other machine learning methods, and all the results prove the accuracy and effectiveness of the proposed method for RUL prediction applications.
{"title":"A Hybrid Deep Learning Prediction Method of Remaining Useful Life for Rolling Bearings Using Multiscale Stacking Deep Residual Shrinkage Network","authors":"Xudong Song, Qi Zhang, Rui Sun, Rui Tian, Jialiang Sun, Changxiang Li, Yunxian Cui","doi":"10.1155/2023/6665534","DOIUrl":"https://doi.org/10.1155/2023/6665534","url":null,"abstract":"The vibration signal is easily interfered by noise due to the influence of environment and other factors, which can lead to the poor adaptability, low accuracy of remaining useful life (RUL) prediction, and other problems. To solve this problem, this paper proposes a novel RUL prediction method, which is based on multiscale stacking deep residual shrinkage network (MSDRSN). MSDRSN combines the ability of stacking in improving prediction accuracy and the advantages of deep residual shrinkage network (DRSN) in denoising. First, cumulative sum (CUSUM) from statistics is used to divide the full life cycle of the rolling bearings and discover the points of failure. Second, stacking is used for feature learning on the raw data, multiple convolutional kernels of different scales are selected as base-learners, and fully connected neural networks are selected as meta-learners for feature fusion and learning. Then, DRSN is used to do prediction, and the acquired results are fitted with Savitzky–Golay (SG) smoothing. Finally, the effectiveness of the proposed method is proved by the IEEE PHM 2012 data challenge dataset. Compared with the multiscale convolutional neural network with fully connected layer (MSCNN-FC) and the bidirectional long short-term memory (BiLSTM) for RUL prediction under the noise. Using the proposed method, the mean absolute error (MSE) of the best result is 0.002 and the mean square error (MSE) is 0.014; meanwhile, the coefficient of determination (R2) of the best prediction result can reach 97.6%. It is also compared with other machine learning methods, and all the results prove the accuracy and effectiveness of the proposed method for RUL prediction applications.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High-energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN-based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.
{"title":"Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection","authors":"Zhendong Wang, Jingfei Li, Zhenyu Xu, Shuxin Yang, Daojing He, Sammy Chan","doi":"10.1155/2023/8825587","DOIUrl":"https://doi.org/10.1155/2023/8825587","url":null,"abstract":"In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High-energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN-based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139269132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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