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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Yang, Mingyu Chen, Xiangyang Lv, Weijie School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

With the rapid development of computer hardware and machine learning algorithms in recent decades, artificial intelligence has created a boom in various industries. computer technology has been widely used in all stages of drug discovery, from machine learning to deep learning, and scientists have conducted numerous studies on the physicochemical properties of drugs. With the advancement of computer technology and deep learning algorithms, it has become possible to obtain large-scale molecular characterization data, extract different types of molecular representations, and construct more subtle models. Among them, molecular graph structure data is increasingly used in deep learning to predict molecular properties. It directly extracts the two-dimensional structural information of molecules, avoiding the defects of indirect structural information extraction in traditional methods. However, the performance of different algorithms based on molecular fingerprints, descriptors, molecular pictures and molecular graph structures is currently controversial. Some studies have found that descriptor-based models outperform graph-based models, while others do the opposite. This shows the difference in the impact of different tasks and datasets on the models. In this experiment, molecular graphs and molecular fingerprints are combined, and molecular graphs are fed into graph neural networks to extract features after the attention mechanism, molecular fingerprints are fed into a fully connected layer to extract features, and the features extracted from the two molecular representations are fused together and a specific activation function is adopted to see if the accuracy and stability of the model can be effectively improved. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Dynamic-DETR: Dynamic Perception for RT -DETR 6

Dynamic-DETR: Dynamic Perception for RT -DETR

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Wei, Jianghao Shen, Wei Hu, Tao Liu, Qiang Yu, Jie Huang, Jiahuan School of Computer Science and Engineering Wuhan institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Object detection is a crucial branch of computer vision. RT-DETR, a recent advancement building upon DETR, has showcased superior performance over YOLO in real-time object detection. However, RT-DETR may struggle with complex inputs due to its limited dynamic perception. To address this, we propose Dynamic-DETR, an enhanced object detection algorithm characterized by its dynamic and flexible nature. In the backbone stage, we introduce the ideas of adaptive dilation and deformability to enable the model to adapt to complex scale variations and geometries;in the neck stage, we introduce content-aware feature reassembly to further enhance the dynamic perception of the model. Experiments have shown that our model outperforms RT -DETR in the 2D supervised object detection task on the KITTI and SODA10M datasets. © 2024 IEEE.

关键词： Object recognition

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An Improved YOLOv8 Algorithm for Oriented Object Detection in Remote Sensing Images 6

An Improved YOLOv8 Algorithm for Oriented Object Detection i...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Yu, Jie Huang, Wei Shen, Wei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Remote sensing target detection is faced with the problems of random target direction, difficult detection of small targets, and dense arrangement. In order to solve the above problems, we propose a lightweight rotating remote sensing target detection algorithm based on YOLOv8, namely Slim-YOLOR. Firstly, we reduce the number of feature map channels in the feature fusion module, and incorporate a large kernel convolutional spatial attention (LSA) module to increase the model sensing field. Secondly, dynamic upsampling is used to reduce the information loss in the process of small target feature reconstruction and enhance the detection ability of dense small targets. Finally, the shared convolution is used to interact with the classification and regression tasks, and the deformable convolution is combined to enhance the localization of the oriented target. Experimental results on the DIOR-R dataset demonstrate that compared to YOLOv8, Slim-YOLOR reduces the number of parameters by 39.5% while increasing the mAP@O.5:0.95 by 1.6%. Additionally, Slim-YOLOR shows clear advantages over other classical algorithms. © 2024 IEEE.

关键词： Optical remote sensing

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Handling and Research on Imbalanced Data Using VAE-SAGAN 24

Handling and Research on Imbalanced Data Using VAE-SAGAN

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9th International Conference on Cyber Security and Information engineering, ICCSIE 2024

作者： Ye, Chaoqian Wu, Shaofei Liu, Jun Hu, Zhuoya School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400718137

Fraud detection datasets typically contain a large number of normal transaction samples and very few fraudulent samples, resulting in a severely imbalanced dataset. This imbalance affects the model’s classification ability, making it prone to misjudgments, which can lead to significant financial losses. Therefore, addressing data imbalances is crucial for improving the accuracy and reliability of fraud detection models. In this study, we propose a fusion model based on Variational Autoencoder and Self-Attention Generative Adversarial Network (VAE-SAGAN) to tackle the data imbalance problem. First, we preprocess the majority of samples using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to filter out overlapping and noisy samples and remove them. Then, we input the minority samples into the VAE-SAGAN model for training, enabling the model to generate more realistic minority samples. In this study, we compare the proposed sampling method with existing sampling algorithms across different datasets and classifiers. Experimental results show that the proposed method outperforms existing sampling algorithms in terms of F1, G-mean, AUC, and Accuracy, and exhibits relatively stable generalization ability across multiple datasets. This research not only enhances the performance of fraud detection models but also provides more reliable technical support for risk management in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Formal Verification of Distributed Self-Adaptive Systems Under Environmental Uncertainty 3

Formal Verification of Distributed Self-Adaptive Systems Und...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Wang, Zihao Liu, Wei Guo, Kao Xu, Fan School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Distributed self-adaptive system is a multi-component collaborative system that automatically adjusts its behavior and structure through adaptive mechanisms to maintain system performance and stability in dynamic environments. The adaptability and robustness of these systems depend on the correctness of self-adaptive decisions in real-world scenarios. However, environmental uncertainty makes it difficult for the systems to accurately perceive their operating environments, which may result in misjudgments and consequently erroneous self-adaptive decisions. To address this issue, we propose a Stochastic Timed Automata-based Adaptive Verification method. By collecting data during system operation and using Bayesian methods to transform it into transition probabilities between system states, a stochastic timed automata model is constructed to simulate the system's operational process. Key properties are verified using statistical model checking to ensure the correctness of self-adaptive decisions. The effectiveness and efficiency of the method are demonstrated by verifying the adaptability and robustness of a multi-roadside unit collaborative system in intelligent transportation vehicle-road coordination. © 2024 IEEE.

关键词： Stochastic systems

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AIDEDNet:anti-interference and detail enhancement dehazing network for real-world scenes

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Frontiers of computer science 2023年第2期17卷 225-235页

作者： Jian ZHANG Fazhi HE Yansong DUAN Shizhen YANG School of Computer Science Wuhan UniversityWuhan 430072China College of Sport Engineering and Information Technology Wuhan Sports UniversityWuhan 430079China School of Remote Sensing and Information Engineering Wuhan UniversityWuhan 430072China

The haze phenomenon seriously interferes the image acquisition and reduces image *** to many uncertain factors,dehazing is typically a challenge in image *** most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing ***,the data set trained in deep learning does not match well this model for three ***,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing ***,it is difficult to get the depth of outdoor scenes for ***,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this *** this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ***,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement *** interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed *** on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for ***,we propose a new way to construct a haze patch on the flight of network *** patch is randomly selected from the input images and the thickness of haze is also randomly *** experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.

关键词： dehaze anti-interference detail enhancement network

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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