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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Han, Xinchu Xu, Ruijie Ma, Lijia Shao, Zengyang Bai, Yuan Wang, Shanfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Laboratory of Data Discovery for Health Limited Hong Kong999077 Hong Kong School of Electronic Engineering Xidian University Xi'an710071 China

ISBN: (纸本)9798350393521

The potential imbalances among entities and connections within complex systems can pose significant threats to their overall functionality. In order to enhance the functional security of these complex systems, it becomes imperative to address and alleviate these underlying imbalances. Structural balance computation has gained attention because of its ability to balance relationships among entities within systems. Traditional methods in structural balance computation aim to minimize imbalances within signed networkss. However, signed networkss have limitations in effectively representing complex data relationships in multi-modal or multi-type data. In order to address this constraint, we propose a novel framework called SHMA (Structural Balance of Signed Hypergraphs based on Memetic Algorithm) which aims to identify and minimize imbalances within signed hypergraphs. More specifically, we formulate the degree of imbalance in signed hypergraphs as an extended energy function, thereby transforming the computation of structural balance of the hypergraphs into an optimization problem. Then, we utilize a multi-level memetic algorithm to optimize the energy function and obtain a solution with the least imbalances. To assess the performance of SHMA, we performed experiments on various real-world datasets and conducted a comparative analysis. The experimental results illustrate that SHMA outperforms the alternative algorithms in terms of structural balance computation of signed hypergraphs. In addition, SHMA exhibits fast convergence, enabling it to reach a balanced state more efficiently. © 2023 IEEE.

关键词： Large scale systems

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Forecasting Price Prediction for Vegetables and Fruits Using Recurrent Neural Network

Forecasting Price Prediction for Vegetables and Fruits Using...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： E. Gothai R.R. Rajalaxmi R. Thamilselvan Harshath S M Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Unpredictable fruit and vegetable prices create significant challenges for farmer livelihoods. This research proposes an innovative approach using recurrent neural networks (RNNs) to predict both minimum and maximum prices. By analyzing historical market data, these models can forecast potential price ranges, empowering farmers to make informed decisions about planting, harvesting, and sales strategies. This enhanced foresight helps farmers navigate market volatility and improve income stability.

关键词： Performance evaluation Recurrent neural networks Navigation Livelihood Predictive models Stability analysis Data models Forecasting Long short term memory Consumer electronics

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Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

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computers, Materials & Continua 2021年第5期67卷 1333-1351页

作者： Saleh Albahli Tahira Nazir Aun Irtaza Ali Javed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy(DR)which is one of the four main reasons for sightlessness all over the *** usually has no clear symptoms before the onset,thus making disease identication a challenging *** healthcare industry may face unfavorable consequences if the gap in identifying DR is not lled with effective ***,our objective is to develop an automatic and cost-effective method for classifying DR *** this work,we present a custom Faster-RCNN technique for the recognition and classication of DR lesions from retinal *** pre-processing,we generate the annotations of the dataset which is required for model ***,introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key ***,the Faster-RCNN localizes and classies the input sample into ve *** experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%.We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and ***,we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.

关键词： Deep learning medical informatics diabetic retinopathy healthcare computer vision

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A Survey on Multi-Behavior Sequential Recommendation

arXiv

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arXiv 2023年

作者： Chen, Xiaoqing Li, Zhitao Pan, Weike Ming, Zhong The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

People usually have the explicit or implicit desire to get the information they need and are most interested in from massive information, which has led to the creation of personalized recommender systems. Recommender systems are set up to address the issue of information overload in traditional information retrieval systems such as search engines, and have been a significant area of research focused on recommending information that is of most interest to users. There is a sequential nature to the behavior of a person interacting with a system, such as examining one item of clothing before examining others. The problem of taking this sequential nature into account in delivering recommendation is known as sequential recommendation (SR). The traditional sequential recommendation problem merely takes into account a single type of behavior of the users, while in real scenarios users tend to engage in multiple types of behaviors, such as examining and adding clothes to cart before purchasing them, leading to the proposal of multi-behavior sequential recommendation (MBSR). MBSR considers both sequentiality and heterogeneity of user behaviors, which can achieve state-of-the-art recommendation through suitable modeling. Hence, MBSR is a relatively new and worthy direction for in-depth research, for which some related works have been proposed. This survey aims to shed light on the MBSR problem. Firstly, we introduce MBSR in detail, including its problem definition, application scenarios and challenges faced. Secondly, we detail the classification of MBSR, including neighborhood-based methods, matrix factorization-based methods and deep learning-based methods, where we further classify the deep learning-based methods into different learning architectures based on RNN, GNN, Transformer, and generic architectures as well as architectures that integrate hybrid techniques. In each method, we present related works based on the data perspective and the modeling perspective, as well

关键词： Search engines

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Enhanced Loan Approval Prediction System using Ensemble Machine Learning Techniques

Enhanced Loan Approval Prediction System using Ensemble Mach...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： E. Gothai R.R. Rajalaxmi R. Thamilselvan Sridhar K Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%.

关键词： Support vector machines Renewable energy sources Accuracy Machine learning algorithms Supervised learning Artificial neural networks Nearest neighbor methods Brain modeling Vectors Biological neural networks

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SRGAN Optimization Through Enhanced Network Structure and Multi-Loss Functions

SRGAN Optimization Through Enhanced Network Structure and Mu...

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Data science and computer Application (ICDSCA), IEEE International Conference on

作者： Zexin Chen Xiaoxin Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

An enhanced SRGAN image super-resolution algorithm is proposed to address SRGAN's training instability and limited global information capture. This enhancement optimizes both the network structure and loss function. In network structure optimization, group normalization is introduced as an alternative to batch normalization, addressing its dependency on batch size and improving stability during small-batch training. Additionally, Global Context Block (GCBlock) is integrated into the residual block, enhancing the network's global information capture and detail representation capabilities. In regard to the loss function, this paper introduces L1 loss and MS-SSIM loss as alternatives to the conventional MSE loss. This approach aims to enhance the quality of the generated image in both pixel space and structural information while reducing the smoothing effect. In the experimental section, the ablation experiments evaluate the performance of models with and without GCBlock on five test sets, including U rban1 00 and Manga109. The results demonstrate that GCBlock improves both detail expressiveness and global information capture, with an average PSNR gain of 0.1388 and SSIM improvement of 0.008. In comparison experiments, the improved algorithm achieved an average PSNR increase of 0.270 and an average SSIM increase of 0.018 on the aforementioned test set, indicating a significant improvement in detail expression and visual quality. Finally, the limitations of the enhanced algorithm are discussed, and potential future research directions are proposed.

关键词： Training Visualization Smoothing methods Computational modeling Superresolution Stability analysis Data models Optimization Image reconstruction Thermal stability

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FewFine: Few-shot Malware Traffic Classification Via Transfer Learning based on Fine-tuning Strategy 19

FewFine: Few-shot Malware Traffic Classification Via Transfe...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Xingtong Shen, Meng Cui, Laizhong Ye, Ke Jia, Jizhe Yue, Guangchun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Shenzhen University College of Computer Science and Software Engineering Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350346558

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency. © 2022 IEEE.

关键词： Knowledge management

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Based on feature reconstruction and feature intersection, the Deep CTR Prediction Model

Based on feature reconstruction and feature intersection, th...

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Big Data, Information and computer Network (BDICN), International Conference on

作者： Ning Bian Li Wang College of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

Collecting a lot of feature data can enhance the prediction impact for the recommender system’s CTR prediction task. The second-order feature cross factorization machine model, which is the most popular, has the ability to automatically learn implicit features based on existing features. The factorization machine model, however, has the following issues: (1) The encoding is fixed, and the same feature represents the same in various contexts. (2) The encoding type is single, and the feature is represented by a single weight vector, which cannot completely express the information of the feature. To harvest possible feature information, recommender systems employ neural networks. A novel method of feature intersection is created by the Compressed Interaction Network (CIN) component suggested in xDeepFM. In order to address the issue of encoding fixation in the factorization machine, the original feature encoding is reconstructed in this paper using xDeepFM in conjunction with the feature refinement network (FRNet). Additionally, different feature representations are expressed in various contexts for the same feature. To address the issue of a single encoding type in factorization machines, the field-aware interactive machine (FIM) component provides feature information at the domain and feature levels, respectively. The model is known as the Field-aware Feature Refinement Depth Factorization Machine (FRDFFM). On the Criteo dataset and the Avazu dataset, respectively, the suggested model was evaluated, and it produced promising results.

关键词： Computational modeling Neural networks Predictive models Feature extraction Encoding Data models Data mining

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Multi-perception large kernel convnet for efficient image super-resolution

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四川大学学报(自然科学版) 2025年第1期62卷 67-78页

作者： MIAO Xuan LI Zheng XU Wen-Zheng College of Computer Science(College of Software) Sichuan UniversityChengdu 610065China Tianfu Engineering-Oriented Numerical Simulation&Software Innovation Center Sichuan UniversityChengdu 610207China

Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art *** efforts have explored the incorporation of Transformers to augment network performance in ***,the high computational cost of Transformers makes them less suitable for deployment on lightweight ***,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel *** this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel ***,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these *** addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel *** results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.

关键词： Single Image Super-Resolution Lightweight model Deep learning Large kernel

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning 12

A Real-Time Hand Sign Language Recognition System for Threat...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Shwany, Zardasht Abdulaziz Abdulkarim Ismail, Shayda Khudhur Khoshnaw, Karwan Hoshyar Khalid Mustafa, Twana Saeed Karabatak, Murat Othman, Nashwan Adnan Erbil Polytechnic University Physiotherapy Department Erbil Iraq Ministry of Higher Education and Scientific Research Network Department Erbil Iraq Erbil Polytechnic University Department of Information and Communication Technology Engineering Erbil Iraq Firat University Department of Software Elazig Turkey College of Science Knowledge University Department of Computer Science Erbil Iraq Knowledge University Department of Computer Engineering Erbil Iraq Al-Kitab University Department of Computer Engineering Altun Kupri Iraq

ISBN: (纸本)9798350330366

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing. © 2024 IEEE.

关键词： Convolutional neural networks

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