Zhanlin Ji
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View article: Polymeric membrane concentration of lithium-magnesium solution for sustainable resource recovery with machine learning
Polymeric membrane concentration of lithium-magnesium solution for sustainable resource recovery with machine learning Open
It is crucial to efficiently recover high value sources from seawater and salt-lake brines for global sustainable development goals. However, conventional membrane-based recovery methods face some significant challenges such as insufficien…
View article: RDL-YOLO: A Method for the Detection of Leaf Pests and Diseases in Cotton Based on YOLOv11
RDL-YOLO: A Method for the Detection of Leaf Pests and Diseases in Cotton Based on YOLOv11 Open
Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show ins…
View article: Novel GRU-based Drug Recommendation Model
Novel GRU-based Drug Recommendation Model Open
With the application of information technology (IT) in the field of health, the electronic health records (EHRs) have been rapidly promoted, and a huge amount of EHR data has been accumulated. As EHR data contains a lot of practical inform…
View article: BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n Open
Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes…
View article: GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition
GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition Open
Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-…
View article: DenLsNet-C: a novel model for breast cancer classification in pathology images based on DenseNet and LSTM
DenLsNet-C: a novel model for breast cancer classification in pathology images based on DenseNet and LSTM Open
In the contemporary world, breast cancer is a common malignancy, whose early detection and timely treatment can increase the patients’ survival prospects. The automated classification of breast cancer types based on histopathological image…
View article: DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation
DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation Open
Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. H…
View article: An Interpolated Quantized Guard Band Algorithm for Physical Layer Key Generation
An Interpolated Quantized Guard Band Algorithm for Physical Layer Key Generation Open
With the continuous progress of communication technology, traditional encryption algorithms cannot meet the demands of modern wireless communication security. Secure communication based on physical layer encryption emerges as a solution. T…
View article: Automatic Pulmonary Nodule Detection and Management System
Automatic Pulmonary Nodule Detection and Management System Open
This paper presents a self-developed automatic pulmonary nodule detection and management system, built and operating on top of the IoT platform EMULSION as an effective tool for physicians and patients to conduct preliminary diagnoses of l…
View article: Multi-Scale Context-Aware Joint Source-Channel Coding for Semantic Image Transmission
Multi-Scale Context-Aware Joint Source-Channel Coding for Semantic Image Transmission Open
View article: FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms
FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms Open
Skin diseases represent a prevalent global health issue that significantly impacts the physical and mental well-being of patients. With the widespread application of computer vision technology in dermatology, automating skin lesion classif…
View article: Machine Learning-Guided Prediction of Polymeric Membrane Performance in Forward Osmosis
Machine Learning-Guided Prediction of Polymeric Membrane Performance in Forward Osmosis Open
View article: Multi-Scale Context-Aware Joint Source-Channel Coding for Semantic Image Transmission
Multi-Scale Context-Aware Joint Source-Channel Coding for Semantic Image Transmission Open
View article: Healthy Route Generation and Recommendation
Healthy Route Generation and Recommendation Open
This paper presents the utilization of a developed pilot wireless-based Air Quality Index (AQI) monitoring system, reporting live geo-grid resolved air quality data, for the purposes of healthy route generation and recommendation to users.…
View article: AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation
AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation Open
Skin lesion segmentation plays a pivotal role in the diagnosis and treatment of skin diseases. By using deep neural networks to segment lesion areas, doctors can more accurately assess the severity of health-related conditions of patients …
View article: DLGRAFE-Net: A double loss guided residual attention and feature enhancement network for polyp segmentation
DLGRAFE-Net: A double loss guided residual attention and feature enhancement network for polyp segmentation Open
Colon polyps represent a common gastrointestinal form. In order to effectively treat and prevent complications arising from colon polyps, colon polypectomy has become a commonly used therapeutic approach. Accurately segmenting polyps from …
View article: Knowledge Graph Embedding Using a Multi-Channel Interactive Convolutional Neural Network with Triple Attention
Knowledge Graph Embedding Using a Multi-Channel Interactive Convolutional Neural Network with Triple Attention Open
Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing kn…
View article: <scp>CafeNet</scp>: A Novel Multi‐Scale Context Aggregation and Multi‐Level Foreground Enhancement Network for Polyp Segmentation
<span>CafeNet</span>: A Novel Multi‐Scale Context Aggregation and Multi‐Level Foreground Enhancement Network for Polyp Segmentation Open
The detection of polyps plays a significant role in colonoscopy examinations, cancer diagnosis, and early patient treatment. However, due to the diversity in the size, color, and shape of polyps, as well as the presence of low image contra…
View article: LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation
LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation Open
Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of …
View article: DTONet a Lightweight Model for Melanoma Segmentation
DTONet a Lightweight Model for Melanoma Segmentation Open
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation …
View article: Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization
Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization Open
With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable …
View article: Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm
Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm Open
To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and…
View article: ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels
ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels Open
The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for compu…
View article: ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation
ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation Open
View article: Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users
Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users Open
With the continuous improvement of the living standard, people have changed their concept from disease treatment to health management. However, most of the current health management software makes recommendations based on users’ static inf…
View article: DSP-KD: Dual-Stage Progressive Knowledge Distillation for Skin Disease Classification
DSP-KD: Dual-Stage Progressive Knowledge Distillation for Skin Disease Classification Open
The increasing global demand for skin disease diagnostics emphasizes the urgent need for advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current practical medical systems, the primary challenge is balancing…
View article: DIRBW-Net: An Improved Inverted Residual Network Model for Underwater Image Enhancement
DIRBW-Net: An Improved Inverted Residual Network Model for Underwater Image Enhancement Open
Underwater photography is challenged by optical distortions caused by water absorption and scattering phenomena. These distortions manifest as color aberrations, image blurring, and reduced contrast in underwater scenes. To address these i…
View article: YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray Images
YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray Images Open
Chest X-ray is one of the most widely used methods for clinical diagnosis of chest diseases. In recent years, the development of deep learning technologies has driven progress in chest disease detection, but existing methods still face num…
View article: DCM-CNER: A Dual-Channel Model for Clinical Named Entity Recognition Based on Embedded ConvNet and Gated Dilated CNN
DCM-CNER: A Dual-Channel Model for Clinical Named Entity Recognition Based on Embedded ConvNet and Gated Dilated CNN Open
As the volume of Chinese electronic medical records (EMRs) experiences an explosive growth, the application of clinical named entity recognition (CNER) technology becomes crucial for the effective utilization of EMR data and practical impl…
View article: BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation
BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation Open
Accurately segmenting thyroid nodules in ultrasound images is crucial for computer-aided diagnosis. Despite the success of Convolutional Neural Networks (CNNs) and Transformers in natural images processing, they struggle with precise bound…