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Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy 1

International Conference on Advances in Communication technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Olatunji, Timothy A. Ajoboh, Oluwakemi A. Chen, Lee Iwendi, Celestine Omeoga, Nkechi Sinha, Anurag School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guizhou Guiyang550025 China School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun558000 China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Manchester Metropolitan University Manchester United Kingdom Department of Computer Science and Information Technology IGNOU New Delhi India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on ***. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radial basis function networks

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Evolving Generalizable Parallel Algorithm Portfolios via Domain-Agnostic Instance Generation

arXiv

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arXiv 2025年

作者： Wang, Zhiyuan Liu, Shengcai Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers. Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population to eventually obtain PAPs with good generalization. Yet, when applied to a specific problem class, these approaches have a major limitation. They require practitioners to provide instance generators specially tailored to the problem class, which is often non-trivial to design. This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE), for binary optimization problems where decision variables take values of 0 or 1. The key novelty of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that eliminates the need for domain-specific instance generators. The strong generality of DACE is validated across three real-world binary optimization problems: the complementary influence maximization problem (CIMP), the compiler arguments optimization problem (CAOP), and the contamination control problem (CCP). Given only a small set of training instances from these problem classes, DACE, without requiring domain knowledge, constructs PAPs with even better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators. Copyright © 2025, The Authors. All rights reserved.

关键词： Cellular automata

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A Study on the Mechanism of Fluorination Enhanced Thermal Stability of IGZO Thin-Film Transistors Based on a Kinetic Model of Donor-Defects

A Study on the Mechanism of Fluorination Enhanced Thermal St...

引用

SID International Symposium Digest of Technical Papers, 2023

作者： Wang, Yuqi Jiang, Wei Xia, Zhihe Wong, Man State Key Laboratory on Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518057 China

The stability against thermal stress of thin-film transistors based on fluorinated indium-gallium-zinc oxide (IGZO:F) is known to be better than that based on IGZO. The mechanism responsible for the improvement has been studied by analyzing the extracted parameters associated with a recently proposed kinetic model governing the oxidation and reduction of donordefects in metal-oxide semiconductors. Compared to those in IGZO, the donor-defects in IGZO:F are shown to be more easily oxidized but less readily reduced during a heat-treatment. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Thin film transistors

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A Channel Gain Measurement Method for Magnetic Resonance Human Body Communication

A Channel Gain Measurement Method for Magnetic Resonance Hum...

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2023 Cross Strait Radio science and Wireless technology Conference, CSRSWTC 2023

作者： Chen, Bingheng Wei, Ziliang Huang, Lixuan Pun, Sio Hang Vai, Mang I. Gao, Yueming College of Physics and Information Engineering Fuzhou University China Fuzhou University The International Joint Laboratory on Health Intelligent Monitoring Systems China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9798350358971

Magnetic Resonance Coupled Human Body Communication (MR-HBC) is a promising human communication method with high efficiency and stability. Designing devices that utilize MR-HBC for communication requires accurate channel transmission characteristics for effective communication. However, MR-HBC has difficulty obtaining reliable channel gains. Due to the existence of an additional return path caused by parasitic capacitance during channel gain measurements, there are significant differences in the results of different measurement methods, and the measured channel gain does not necessarily correspond to the actual channel gain. In this regard, this paper proposes a method that can accurately measure the actual channel gain of MR-HBC. By modeling the electrical connection during the measurement of the measuring instrument, this paper shows that the additional return path between the transmitter and receiver in the experimental measurement is the reason for the discrepancy between the channel gain measurement results and the actual ones. Proposed that a balun device should be added to suppress the additional return path during the MR-HBC channel gain measurement in order to measure the realistic channel gain, and this is verified by experiments. The results show that before adding the balun, the MR-HBC channel gain measurements of different measurement methods show significant differences (maximum difference of 20.4 dB). When the additional return path is suppressed by adding the balun device, the measurement results of different measurement methods are consistent and close to the realistic measurement results measured using a small device (maximum difference of 2.5 dB). This study addresses the accuracy of measurement results during MR-HBC channel gain measurements. © 2023 IEEE.

关键词： additional return path channel gain MR-HBC

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Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Hou, Junlin Xu, Jilan Xiao, Fan Zhang, Bo Xu, Yiqian Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy is the leading cause of legal blindness in patients with pathologic myopia. Automated myopic maculopathy diagnosis is of vital importance to early treatment and progression slowdown. However, the scarcity of labeled fundus images with myopic maculopathy makes it challenging to improve diagnostic performance via deep learning models. In this paper, we construct a label-efficient deep learning framework for myopic maculopathy classification. In specific, we exploit two categories of pre-training methods, i.e., vision-language pre-training and self-supervised visual representation learning, to alleviate the overfitting problem caused by the limited number of training images. Moreover, we adopt a semi-supervised learning technique, namely pseudo labeling, to leverage a large number of unlabeled fundus images from external datasets. We also investigate the impact of other key components in model training for better performance, including backbone architecture, input resolution, and loss function. Our method is evaluated in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). Among 17 participating teams, our ensembled model ranked 1st on the leaderboard with an average score of 0.8752. The code will be publicly available at https://***/FDU-VTS/MMAC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Application of a Kinetic Model to the Characterization of Donor-Defects in a Fluorinated Metal-Oxide Semiconductor

Application of a Kinetic Model to the Characterization of Do...

引用

The 2023 International Conference on Display technology, (ICDT 2023)

A recently proposed kinetic model describing the oxidation and reduction of donor-defects in semiconducting metaloxide thin films has been applied to characterize the properties of the donor-defects in fluorinated indium gallium-zinc oxide. The excellent agreement of the measured data with the modelling curves is a nice demonstration of the utility of the model as a useful tool for characterizing the nature of the donor-defects in metal oxide films. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Thin film transistors

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Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation

arXiv

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arXiv 2025年

作者： Wang, Yin Li, Mu Liu, Jiapeng Leng, Zhiying Li, Frederick W.B. Zhang, Ziyao Liang, Xiaohui B. State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science University of Durham United Kingdom Zhongguancun Laboratory Beijing China

We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed semantic cues regarding body parts, (2) not fully modeling linguistic structures between words to comprehend text comprehensively. To tackle these limitations, we propose a novel fine-grained framework Fg-T2M++ that consists of: (1) an LLMs semantic parsing module to extract body part descriptions and semantics from text, (2) a hyperbolic text representation module to encode relational information between text units by embedding the syntactic dependency graph into hyperbolic space, and (3) a multi-modal fusion module to hierarchically fuse text and motion features. Extensive experiments on HumanML3D and KIT-ML datasets demonstrate that Fg-T2M++ outperforms SOTA methods, validating its ability to accurately generate motions adhering to comprehensive text semantics. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Mode-switching single-pixel imaging via a high-performance perovskite-Si dual-mode photodetector

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Photonics Research 2024年第12期12卷 2873-2880页

作者： YUJIN LIU DONGXU LIN JINGYANG XING HANYAN ZHAO HONGLING WAN HAO WANG ZHONG JI XUELI CHEN Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of TechnologyXidian UniversityGuangzhou 510555China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Department of Mechanical and Energy EngineeringSUSTech Energy Institute for Carbon NeutralitySouthern University of Science and TechnologyShenzhen 518055China Center for Biomedical-Photonics and Molecular Imaging Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi ProvinceSchool of Life Science and TechnologyXidian UniversityXi’an 710126China

Visible–near-infrared(VIS-NIR)dual-mode imaging can expand the human perception ***,the development of dual-mode image sensors is still challenging due to complex fabrication processes and readout circuit ***,we design a simple-structured(perovskite-Au/Si/Ag)dual-mode *** unique asymmetric electrode design allows the device to support two operating modes at zero *** device exhibits a detection range covering 400–1100 nm under top illumination mode,with a peak specific detectivity of up to 5.56×1013 *** bottom illumination mode,the device demonstrates pronounced narrowband NIR response *** importantly,we develop a dual-mode single-pixel imaging system based on this device,bypassing the fabrication processes of high-density array image *** system exhibits excellent VISNIR dual-mode imaging results,effectively separating NIR and VIS information and enhancing infrared details in the fused ***,we discover that the system can effectively suppress ringing artifacts,achieving infrared information perception at a low sampling rate,which can accelerate the imaging speed by∼16 times(reduced from∼3.2 s to∼0.2 s).Our proposed dual-mode single-pixel imaging technology offers new means for material identification and intelligent perception.

关键词： mode illumination details

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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