Jianhua Ma

Jianhua Ma

Hosei University

H-index: 59

Asia-Japan

About Jianhua Ma

Jianhua Ma, With an exceptional h-index of 59 and a recent h-index of 48 (since 2020), a distinguished researcher at Hosei University, specializes in the field of Network, ubiquitous computing, social computing, cyber technology.

His recent articles reflect a diverse array of research interests and contributions to the field:

Individualized intensive insulin therapy of diabetes: Not only the goal, but also the time

A model-based MR parameter mapping network robust to substantial variations in acquisition settings

Gastric emptying of a glucose drink is predictive of the glycaemic response to oral glucose and mixed meals, but unrelated to antecedent glycaemic control, in type 2 diabetes

Federated distillation and blockchain empowered secure knowledge sharing for Internet of medical Things

Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning

A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

GENII: A graph neural network-based model for citywide litter prediction leveraging crowdsensing data

Jianhua Ma Information

University

Hosei University

Position

Professor of Computer Science

Citations(all)

13862

Citations(since 2020)

8725

Cited By

4525

hIndex(all)

59

hIndex(since 2020)

48

i10Index(all)

285

i10Index(since 2020)

177

Email

University Profile Page

Hosei University

Jianhua Ma Skills & Research Interests

Network

ubiquitous computing

social computing

cyber technology

Top articles of Jianhua Ma

Individualized intensive insulin therapy of diabetes: Not only the goal, but also the time

Authors

Yun Hu,Hong-Jing Chen,Jian-Hua Ma

Journal

World Journal of Diabetes

Published Date

2024/1/1

Intensive insulin therapy has been extensively used to control blood glucose levels because of its ability to reduce the risk of chronic complications of diabetes. According to current guidelines, intensive glycemic control requires individualized glucose goals rather than as low as possible. During intensive therapy, rapid blood glucose reduction can aggravate microvascular and macrovascular complications, and prolonged overuse of insulin can lead to treatment-induced neuropathy and retinopathy, hypoglycemia, obesity, lipodystrophy, and insulin antibody syndrome. Therefore, we need to develop individualized hypoglycemic plans for patients with diabetes, including the time required for blood glucose normalization and the duration of intensive insulin therapy, which deserves further study.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings

Authors

Qiqi Lu,Jialong Li,Zifeng Lian,Xinyuan Zhang,Qianjin Feng,Wufan Chen,Jianhua Ma,Yanqiu Feng

Journal

Medical Image Analysis

Published Date

2024/5/1

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in …

Gastric emptying of a glucose drink is predictive of the glycaemic response to oral glucose and mixed meals, but unrelated to antecedent glycaemic control, in type 2 diabetes

Authors

Chunjie Xiang,Yixuan Sun,Yong Luo,Cong Xie,Weikun Huang,Zilin Sun,Karen L Jones,Michael Horowitz,Christopher K Rayner,Jianhua Ma,Tongzhi Wu

Journal

Nutrition & Diabetes

Published Date

2024/4/8

BackgroundGastric emptying (GE), with wide inter-individual but lesser intra-individual variations, is a major determinant of postprandial glycaemia in health and type 2 diabetes (T2D). However, it is uncertain whether GE of a carbohydrate-containing liquid meal is predictive of the glycaemic response to physiological meals, and whether antecedent hyperglycaemia influences GE in T2D. We evaluated the relationships of (i) the glycaemic response to both a glucose drink and mixed meals with GE of a 75 g glucose drink, and (ii) GE of a glucose drink with antecedent glycaemic control, in T2D.MethodsFifty-five treatment-naive Chinese adults with newly diagnosed T2D consumed standardised meals at breakfast, lunch and dinner with continuous interstitial glucose monitoring. On the subsequent day, a 75 g glucose drink containing 150 mg 13C-acetate was ingested to assess GE (breath test) and plasma glucose …

Federated distillation and blockchain empowered secure knowledge sharing for Internet of medical Things

Authors

Xiaokang Zhou,Wang Huang,Wei Liang,Zheng Yan,Jianhua Ma,Yi Pan,I Kevin,Kai Wang

Journal

Information Sciences

Published Date

2024/3/1

With the development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, smart services have penetrated into every aspect of our daily lives, including the medical treatment and healthcare fields. However, due to security and privacy issues, medical data cannot be easily shared, which may lead to the situation of the so-called data silos. Challenges in existing approaches when building medical data sharing models can be summarized as: i) It is very challenging to ensure that the privacy of the medical data is protected and to identify the ownership of medical data; ii) Their models always result in poor performance or have the problem of excessive communication overhead due to the large amount of model parameters; iii) The current scenario of combining federated learning and blockchain generally ignores the load on nodes, which may easily lead to a lack of efficiency and fairness during the …

Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning

Authors

Xiaokang Zhou,Qiuyue Yang,Qiang Liu,Wei Liang,Kevin Wang,Zhi Liu,Jianhua Ma,Qun Jin

Journal

Information Fusion

Published Date

2024/5/1

With the development of advanced embedded and communication systems, location information has become a crucial factor in supporting context-aware or location-aware intelligent services. Among these services, modern Intelligent Transportation System (ITS) has the strictest requirements for real-time, accurate, and private location data. In this study, a Spatial-Temporal Federated Transfer Learning (ST-FTL) framework is designed and introduced to achieve more precise cooperative positioning with multi-sensor data fusion while protecting the location data privacy in urban ITS. Specifically, a three-layer FTL architecture is constructed to enhance the prediction accuracy on GPS positioning errors for vehicles in different regions especially when facing missing local data in some specific scenarios (e.g., urban canyons), in which Transfer Learning (TL) is incorporated to optimize the initialization of global model with …

A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders

Authors

Yufeng Wang,Yangming Zhou,Jianhua Ma

Journal

Applied Soft Computing

Published Date

2024/1/1

Stealthy FDIA (False Data Injection Attack) is a serious cyber threat that can modify state estimation of smart grid through maliciously altering the measurement data, but can’t be detected by traditional bad data detection system in smart grid. There exist two weakpoints for numerous deep neural networks (DNNs) based data-driven schemes against FDIA. First, they mainly focus on detecting the presence of FDIA, but fail to localize the specific bus/nodes affected. Second, their performance is not sufficiently desirable under small attack, i.e., anomalies caused by the attack closely resemble normal data. To address the above issues, this paper proposes an effective locational FDIA detection framework based on data reconstruction, AT-GVAE, which seamlessly integrates the variational autoencoders (VAEs) and generative adversarial network paradigm. Specifically, our contributions are threefold. First, the proposed AT …

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Authors

Lei Wang,Ming Qiang Meng,Shixuan Chen,Zhaoying Bian,Dong Zeng,Deyu Meng,Jianhua Ma

Journal

Physics in Medicine and Biology

Published Date

2024/2/29

Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of Deep Learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms. In this paper, we introduce a Semi-supervised Iterative Adaptive Network (SIA-Net) for LDCT imaging …

GENII: A graph neural network-based model for citywide litter prediction leveraging crowdsensing data

Authors

Zhiting Wang,Yuhao Chen,Fanwei Zhu,Zengwei Zheng,Jianhua Ma,Binbin Zhou

Journal

Expert Systems with Applications

Published Date

2024/3/1

Plastic litter and its associated environmental hazards have garnered global attention in recent years, highlighting the need for effective management. Improper handling of plastic litter can lead to environmental degradation, making it crucial to address this issue. In this paper, we propose an innovative approach to predict the spatial distribution and quantity of plastic litter at the city level by leveraging crowd-sensing data and designing a graph neural network-based model. Meteorological data is specifically used to enhance temporal correlation, while the spatial distribution of litter is clustered using the K-means method to capture spatial correlation. For each cluster, multiple graphs are constructed based on the points of interest (POIs) and litter distribution within the cluster. Graph attention neural networks and heterogeneous graph attention networks are then utilized to aggregate the adjacency information and …

A double‐dynamic‐bond crosslinked multifunctional conductive hydrogel with self‐adhesive, remoldability, and rapid self‐healing properties for wearable sensing

Authors

Xinfeng Li,Tianyi Zhang,Baiqing Song,Kaili Yang,Xiaoqiong Hao,Jianhua Ma

Journal

Polymers for Advanced Technologies

Published Date

2024/1

In recent years, conductive hydrogels have been designed as flexible sensors with broad application prospects in the field of smart wearable devices. However, the coordination of the mechanical properties, self‐healing capability, and conductivity of hydrogels has always been a challenge. In this work, a multifunctional conductive hydrogel with self‐adhesive, re‐shapeable, and rapid self‐healing abilities (PB‐PACS‐PM3 (self‐healing efficiency) = 98.8% in 15 s) was prepared by incorporating protocatechualdehyde modified carboxymethyl chitosan (PA@o‐CMCS) and PDA‐modified MXene (PDA@MXene) into the interchain of polyvinyl alcohol through dynamic borate ester crosslinking. The dynamic Schiff base reaction not only effectively introduced o‐CMCS as a mechanical enhancer but also improved the viscoelasticity and self‐healing capability of the hydrogel through the dual dynamic bond crosslinking …

Matrix metallopeptidase 9 contributes to the beginning of plaque and is a potential biomarker for the early identification of atherosclerosis in asymptomatic patients with diabetes

Authors

Bingli Liu,Liping Su,Sze Jie Loo,Yu Gao,Ester Khin,Xiaocen Kong,Rinkoo Dalan,Xiaofei Su,Kok-Onn Lee,Jianhua Ma,Lei Ye

Journal

Frontiers in Endocrinology

Published Date

2024/4/10

Aims To determine the roles of matrix metallopeptidase-9 (MMP9) on human coronary artery smooth muscle cells (HCASMCs) in vitro, early beginning of atherosclerosis in vivo in diabetic mice, and drug naïve patients with diabetes. Methods Active human MMP9 (act-hMMP9) was added to HCASMCs and the expressions of MCP-1, ICAM-1, and VCAM-1 were measured. Act-hMMP9 (n=16) or placebo (n=15) was administered to diabetic KK.Cg-Ay /J (KK) mice. Carotid artery inflammation and atherosclerosis measurements were made at 2 and 10 weeks after treatment. An observational study of newly diagnosed drug naïve patients with type 2 diabetes mellitus (T2DM n=234) and healthy matched controls (n=41) was performed and patients had ultrasound of carotid arteries and some had coronary computed tomography angiogram for the assessment of atherosclerosis. Serum MMP9 was measured and its correlation with carotid artery or coronary artery plaques was determined. Results In vitro, act-hMMP9 increased gene and protein expressions of MCP-1, ICAM-1, VCAM-1, and enhanced macrophage adhesion. Exogenous act-hMMP9 increased inflammation and initiated atherosclerosis in KK mice at 2 and 10 weeks: increased vessel wall thickness, lipid accumulation, and Galectin-3+ macrophage infiltration into the carotid arteries. In newly diagnosed T2DM patients, serum MMP9 correlated with carotid artery plaque size with a possible threshold cutoff point. In addition, serum MMP9 correlated with number of mixed plaques and grade of lumen stenosis in coronary arteries of patients with drug naïve T2DM. Conclusion MMP9 may …

Metformin normalizes mitochondrial function to delay astrocyte senescence in a mouse model of Parkinson’s disease through Mfn2-cGAS signaling

Authors

Min Wang,Tian Tian,Hong Zhou,Si-Yuan Jiang,Ying-Ying Jiao,Zhu Zhu,Jiang Xia,Jian-Hua Ma,Ren-Hong Du

Journal

Journal of Neuroinflammation

Published Date

2024/4/2

BackgroundSenescent astrocytes play crucial roles in age-associated neurodegenerative diseases, including Parkinson’s disease (PD). Metformin, a drug widely used for treating diabetes, exerts longevity effects and neuroprotective activities. However, its effect on astrocyte senescence in PD remains to be defined.MethodsLong culture-induced replicative senescence model and 1-methyl-4-phenylpyridinium/α-synuclein aggregate-induced premature senescence model, and a mouse model of PD were used to investigate the effect of metformin on astrocyte senescence in vivo and in vitro. Immunofluorescence staining and flow cytometric analyses were performed to evaluate the mitochondrial function. We stereotactically injected AAV carrying GFAP-promoter-cGAS-shRNA to mouse substantia nigra pars compacta regions to specifically reduce astrocytic cGAS expression to clarify the potential molecular …

Unveiling camouflaged and partially occluded colorectal polyps: Introducing CPSNet for accurate colon polyp segmentation

Authors

Huafeng Wang,Tianyu Hu,Yanan Zhang,Haodu Zhang,Yong Qi,Longzhen Wang,Jianhua Ma,Minghua Du

Journal

Computers in Biology and Medicine

Published Date

2024/2/21

BackgroundSegmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called “camouflaged” polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection.MethodsWe present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy.ResultsOur experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly …

Efficacy and safety of mazdutide in chinese patients with type 2 diabetes: A randomized, double-blind, placebo-controlled phase 2 trial

Authors

Bo Zhang,Zhifeng Cheng,Ji Chen,Xin Zhang,Dexue Liu,Hongwei Jiang,Guoqing Ma,Xiaoyun Wang,Shenglian Gan,Juan Sun,Ping Jin,Jianjun Yi,Bimin Shi,Jianhua Ma,Shandong Ye,Guixia Wang,Linong Ji,Xuejiang Gu,Ting Yu,Pei An,Huan Deng,Haoyu Li,Li Li,Qingyang Ma,Lei Qian,Wenying Yang

Journal

Diabetes Care

Published Date

2024/1/1

OBJECTIVE We conducted a randomized, double-blind, placebo-controlled phase 2 trial to evaluate the efficacy and safety of mazdutide, a once-weekly glucagon-like peptide 1 and glucagon receptor dual agonist, in Chinese patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Adults with type 2 diabetes inadequately controlled with diet and exercise alone or with stable metformin (glycated hemoglobin A1c [HbA1c] 7.0–10.5% [53–91 mmol/mol]) were randomly assigned to receive 3 mg mazdutide (n = 51), 4.5 mg mazdutide (n = 49), 6 mg mazdutide (n = 49), 1.5 mg open-label dulaglutide (n = 50), or placebo (n = 51) subcutaneously for 20 weeks. The primary outcome was change in HbA1c from baseline to week 20. RESULTS Mean changes in HbA1c from baseline to week 20 ranged from −1.41% to −1.67% with mazdutide (−1.35% with …

Personalized federation learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse

Authors

Xiaokang Zhou,Qiuyue Yang,Xuzhe Zheng,Wei Liang,I Kevin,Kai Wang,Jianhua Ma,Yi Pan,Qun Jin

Journal

IEEE Journal on Selected Areas in Communications

Published Date

2024/1/8

With the flourish of digital technologies and rapid development of 5G and beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting with virtual services. How to capture and model users’ multi-platform or cross-space data/behaviors become essential to enrich people with more realistic and immersed experience in Metaverse-enabled smart applications over 5G and beyond networks. In this study, we propose a Personalized Federated Learning with Model-Contrastive Learning (PFL-MCL) framework, which may efficiently enhance the communication and interaction in human-centric Metaverse environments by making use of the large-scale, heterogeneous, and multi-modal Metaverse data. Differing from the conventional Federated Learning (FL) architecture, a multi-center aggregation structure to learn multiple global …

Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization

Authors

Shanzhou Niu,Mengzhen Zhang,Yang Qiu,Shuo Li,Lijing Liang,Qiegen Liu,Tianye Niu,Jing Wang,Jianhua Ma

Journal

Physics in Medicine and Biology

Published Date

2024/4/8

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction (SIR) based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain …

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation

Authors

Mingqiang Meng,Yongbo Wang,Manman Zhu,Xi Tao,Zerui Mao,Jingyi Liao,Zhaoying Bian,Dong Zeng,Jianhua Ma

Journal

IEEE Journal of Biomedical and Health Informatics

Published Date

2024/3/18

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. And they only learn the posterior distribution of latent normal-dose CT (NDCT) images conditioned on observed LDCT images in the traditional maximum a posteriori (MAP) framework, while ignoring the noise generation process of LDCT images. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is …

Boron Ketoimine Fluorophores with Unique Optical Properties: Multicolor Tuning, Efficient Dual‐State Emission, and Reversible Acid‐Base Response

Authors

Dong Yang,Guangming Tian,Jianhua Ma,Xinhai He,Jie Kong

Journal

Advanced Optical Materials

Published Date

2024/1

Organic luminophores with high emission efficiency in solution as well as solid‐state have received increasing attention due to their promising applications in biosensors, biological imaging, and optoelectronic devices. Herein, four triphenylamine (TPA)‐substituted boron ketoimine molecules (TPA‐BKI a‐d) are designed and synthesized by combining a donor‐acceptor (D‐A) electronic system and highly twisted block. The asymmetry of molecular and the planarity of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) sides in the excited state endow TPA‐BKI a‐d with rigidified conformations, allowing bright emission in dilute solution. Additionally, their highly twisted structures avoid close contact between molecules and detrimental exciton interactions, leading to efficient emission in the solid state. Meanwhile, the emission colors are modulated by the incorporation …

Biomimetic shape-morphing actuators with controlled bending deformation and photo-mediated 3D shape programming for flexible smart devices

Authors

Guangming Tian,Rui Wen,Jianhua Ma,Chong Fu

Journal

Sensors and Actuators B: Chemical

Published Date

2024/6/15

Biomimetic soft actuators with programmable morphing behaviors and remote actuations have drawn significant attention in recent years. In this work, we present the network architecture of a four-arm crystalline shape memory polymer actuator via efficient thiol-acrylate coupling of polycaprolactone precursor bearing crosslinkable methacrylate end-groups with pentaerythritol, followed by post-approach surface chemistry of polydopamine (PDA) nanospheres coating. The excellent photothermal transfer efficiency of the coated PDA can enable rapid temperature increase over the transition temperature of polymeric matrix and optically drive the shape morphings. Combined with built-in internal stress, anisotropic polymer chain relaxation of the strained thin actuator film would generate asymmetric contractions and thus causes controlled bending deformations via surface directing of an 532 nm green laser. Beyond …

Polyurethane toughened covalent adaptive networks epoxy composite based on thermoreversible Diels‐Alder reaction: Self‐healable, shape memory, and recyclable

Authors

Xinfeng Li,Tianyi Zhang,Baiqing Song,Kaili Yang,Xiaoqiong Hao,Jianhua Ma

Journal

Journal of Applied Polymer Science

Published Date

2024/1/5

Covalent adaptive networks (CANs) epoxy based on the Diels‐Alder (DA) reaction usually are commonly used for self‐healing materials. However, poor toughness greatly limits its application in innovative materials. Herein, based on retaining the excellent dynamic characteristics of DA reactive CANs, we introduced thermoplastic polyurethane (TPU) in situ during crosslinking, improving the composite material's strength and toughness. A multicomponent polymeric system with advanced performance can be produced by the individual components on the condition of the synergistic hybrid effects. Molecular‐level interlocking polymer networks between the chain entanglement of TPU and the reversible covalent crosslink of DFB were formed by the topological reorganization of these two immiscible polymers. As the gelation proceeds, a homogenous structure instead of conventional phase separation is formed. When …

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT

Authors

Yuyan Song,Tianyi Yao,Shengwang Peng,Manman Zhu,Ming Qiang Meng,Jianhua Ma,Dong Zeng,Jing Huang,Yongbo Wang,Zhaoying Bian

Journal

Physics in Medicine and Biology

Published Date

2024/4/8

Objective Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact …

See List of Professors in Jianhua Ma University(Hosei University)

Jianhua Ma FAQs

What is Jianhua Ma's h-index at Hosei University?

The h-index of Jianhua Ma has been 48 since 2020 and 59 in total.

What are Jianhua Ma's top articles?

The articles with the titles of

Individualized intensive insulin therapy of diabetes: Not only the goal, but also the time

A model-based MR parameter mapping network robust to substantial variations in acquisition settings

Gastric emptying of a glucose drink is predictive of the glycaemic response to oral glucose and mixed meals, but unrelated to antecedent glycaemic control, in type 2 diabetes

Federated distillation and blockchain empowered secure knowledge sharing for Internet of medical Things

Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning

A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

GENII: A graph neural network-based model for citywide litter prediction leveraging crowdsensing data

...

are the top articles of Jianhua Ma at Hosei University.

What are Jianhua Ma's research interests?

The research interests of Jianhua Ma are: Network, ubiquitous computing, social computing, cyber technology

What is Jianhua Ma's total number of citations?

Jianhua Ma has 13,862 citations in total.

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