1,053 Result(s)
within
Engineering
Information Systems and Communication Service
Qi** Li
Chapter
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Chapter
FedBA: Non-IID Federated Learning Framework in UAV Networks
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Chapter
Enhancing Epilepsy Diagnosis with Deep Learning and Multi-channel Processing of EEG Signals
Epilepsy is a neurological disorder that seriously affects patients’ lives and health. The accurate identification of epilepsy species is essential for develo** effective treatment and management plans. This...
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Chapter
A Phonetics and Semantics-Based Chinese Short Text Fusion Algorithm
With the rapid development of the Internet, short text has become more and more popular on the Internet and many short texts with a length of a few words to dozens of words have exploded, such as chats, text m...
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Chapter
Feature Extension for Chinese Short Text Based on Tongyici Cilin
Since the short text has characteristics such as sparse features, calculating its similarity is a considerable challenge. However, there is less research on the method of Chinese short text feature extension i...
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Typhoon Track Prediction Based on TimeForce CNN-LSTM Hybrid Model
The accurate prediction of typhoon trajectory can greatly reduce the loss of life and property, which is of great significance for reducing typhoon disasters and conducting risk assessment. With the improvemen...
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Chapter
Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures
Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subject...
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Chapter and Conference Paper
Construction of Gene Network Based on Inter-tumor Heterogeneity for Tumor Type Identification
Tumor heterogeneity is one of the challenges to study malignant tumors. In general, tumors are driven by combinations of mutated genes that vary greatly from patient to patient. Constructing cancer gene networ...
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Chapter and Conference Paper
The CNV Predict Model in Esophagus Cancer
Copy number variations (CNVs) are critical factors in esophageal cancer carcinogenesis. The present study identified molecular signatures that predict prognosis in esophageal cancer by comprehensively analyzin...
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Chapter and Conference Paper
GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations
Numerous studies reveal that Circular RNAs (circRNAs) are critical for human physiological and pathological processes. Research on the disease-related circRNAs can provide insight into the mechanisms of extrao...
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Chapter and Conference Paper
Elucidating Quantum Semi-empirical Based QSAR, for Predicting Tannins’ Anti-oxidant Activity with the Help of Artificial Neural Network
Tannins are potential curatives, besides being an effective antioxidants. Here, tannin based QSAR with machine learning pipeline is elucidated. IC50 values of tannins’ antioxidant activity were adapted from li...
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Chapter and Conference Paper
Research on the Potential Mechanism of Rhizoma Drynariae in the Treatment of Periodontitis Based on Network Pharmacology
Objective: To explore the potential mechanism of compound Rhizoma Drynariae on periodontitis. Methods: the main compounds and corresponding targets of Rhizoma Drynariae were got from the Chinese Medicine Syste...
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Chapter and Conference Paper
The Prognosis Model of Clear Cell Renal Cell Carcinoma Based on Allograft Rejection Markers
Renal cell carcinoma (RCC), also called as renal adenocarcinoma or hypernephroma, is the most common form cancer that occurs in kidney. About 9 out 10 malignant complications that occurs in kidney are RCC, and...
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Chapter and Conference Paper
K-Nearest Neighbor Based Local Distribution Alignment
When massive labeled data are unavailable, domain adaptation can transfer knowledge from a different source domain. Many recent domain adaptation methods merely focus on extracting domain-invariant features vi...
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Chapter and Conference Paper
MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction
Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the...
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Chapter and Conference Paper
Glioblastoma Subty** by Immuogenomics
Objective: To analyze and establish immunophenoty** of glioblastoma (GBM) by genomics study and further explore its clinical application value. Methods: RNA-seq and clinical data from TCGA (from onset to May...
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Chapter and Conference Paper
Predicting Drug-Disease Associations by Self-topological Generalized Matrix Factorization with Neighborhood Constraints
Predicting drug-disease associations (DDAs) is a significant part of drug discovery. With the continuous accumulation of biomedical data, multidimensional metrics about drugs and diseases are obtained, therefo...
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Chapter and Conference Paper
An Optimization Method for Drug-Target Interaction Prediction Based on RandSAS Strategy
Predicting drug-target interactions plays an important role in shortening the cycle and reducing the cost of drug development. Although many existing approaches have been successful, most of them mainly start ...
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Chapter and Conference Paper
Arbitrary Voice Conversion via Adversarial Learning and Cycle Consistency Loss
Recently, Non-parallel voice conversion (VC) has attracted the attention of many researchers in the field of speech. However, such model suffers from the limitation that how to improve the generalization of th...
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Chapter and Conference Paper
SCDF: A Novel Single-Cell Classification Method Based on Dimension-Reduced Data Fusion
Single-cell RNA sequencing (scRNA-seq) technology has recently brought a new insight into identifying and characterizing novel cell types and gene expression patterns. The study of single-cell RNA sequencing i...
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Chapter and Conference Paper
A Novel Synthetic Lethality Prediction Method Based on Bidirectional Attention Learning
Simultaneous mutations in synthetic lethality genes can lead to cancer cell apoptosis and it can be utilized in cancer target therapy. However, high-throughput wet laboratory screening methods are expensive an...
