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Digital Currency Abnormal Detection Based on Graph Neural Network

Digital Currency Abnormal Detection Based on Graph Neural Ne...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Que, Xingle Cheng, Yang Bai, Yunfei Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

In recent years, with the rapid development of the digital currency market, the security issues of digital currency transactions have become increasingly prominent, and abnormal transaction detection has become an important technical means to ensure the security of the financial system. Traditional detection methods face many challenges in dealing with complex blockchain data, as they cannot fully utilize the inherent structural information of transaction data, and are difficult to handle the problem of imbalanced categories. Accordingly, this study proposes a GNN (Graph Neural Network)-based abnormal transaction detection method, which models transaction data as a graph structure, extracts transaction features using Graph Convolutional Network (GCN) and uses small sample processing techniques to alleviate data imbalance. The proposed method showed that the experimental results could effectively improve the detection performance of abnormal transactions after multiple rounds of training, especially in terms of AUC evaluation indicators, which verifies its potential application in the detection of abnormal transactions in digital currencies. This study provides a novel and effective solution for anomaly detection in complex network data and points out the direction for future research. © 2024 IEEE.

关键词： Graph neural networks

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Trajectory Design and Resource Allocation for UAV-Assisted Computation Offloading 7

Trajectory Design and Resource Allocation for UAV-Assisted C...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Yunxin Wang, Jiale Wang, Junhao Que, Xingle Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper studies air-ground cooperative communication based on unmanned aerial vehicles (UAVs) and device-to-device (D2D) technology. This field has become a research hotspot due to its susceptibility to passive eavesdropping and privacy leakage. To enhance data transmission security, this paper proposes a full-duplex UAV-assisted scheme to address potential ground eavesdropping risks in UAV-assisted computation offloading tasks. The scheme transmits interference signals while receiving computation tasks to counteract eavesdroppers. Given the limited energy of UAVs and the inadequacies of existing energy optimization research, this paper establishes a UAV-assisted mobile edge computing (MEC) communication system model. To address the non-convex optimization problem within the model, a successive convex approximation algorithm is employed for simplification and decomposition. User scheduling, power allocation, and UAV trajectory design subproblems are solved through an alternating iteration method. Simulation results validate that the proposed method can effectively improve the system's secure communication performance even in the presence of uncertain eavesdropper locations. ©2024 IEEE.

关键词： Convex optimization

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Preprocessing Pathology Reports for Vision-Language Model Development

Preprocessing Pathology Reports for Vision-Language Model De...

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2024 MICCAI Workshop on Computational Pathology, MICCAI COMPAYL 2024

作者： Lucassen, Ruben T. van de Luijtgaarden, Tijn Moonemans, Sander P.J. Blokx, Willeke A.M. Veta, Mitko Dept. of Pathology University Medical Center Utrecht Netherlands Dept. of Biomedical Engineering Eindhoven University of Technology Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Pathology reports are increasingly being used for development of vision-language models. Because the reports often include information that cannot directly be derived from paired images, careful selection of information is required to prevent hallucinations in tasks like report generation. In this paper, we present a language model for subsentence segmentation based on the information content, as part of a preprocessing workflow for 27,500 pathology reports of cutaneous melanocytic lesions. After initial clean up, the reports were first translated from Dutch to English and then segmented by separate language models. Both models were developed using an iterative approach, in which the development dataset was expanded with manually corrected model predictions for previously unannotated reports before finetuning the next version of the models. Over the course of eight iterations, the development dataset was in the end scaled up to 1,500 translated and annotated reports. On the independent test set of 3,597 sentences from 150 reports, 219 translation errors (6,1%) of different severities were counted. The subsentence segmentation model achieved a strong predictive performance on the test set with a macro average F1-score of 0.921 (95% CI, 0.890-0.940) and a weighted average F1-score of 0.952 (95% CI, 0.944-0.960) over 13 different classes. The remaining 25,850 unannotated reports were translated and segmented using the final models to complete the dataset preprocessing. Differences in word count and class distribution between section types of the reports were explored in preparation for future vision-language modeling. The presented methodology is generic and can, therefore, easily be extended to multiple or different pathology domains beyond melanocytic skin lesions. Code and trained model parameters are made publicly available. ©2024 Lucassen et al.

关键词： Visual languages

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Enhancing Lymph Node Classification in Rectal Cancer MRI Images Through a Multi-Model Approach

Enhancing Lymph Node Classification in Rectal Cancer MRI Ima...

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2024 IEEE International Conferences of AI, science, engineering, and technology, AIxSET 2024

作者： Gupta, Rishit Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Bhatt, Manish Kijsirikul, Boonserm Wang, Aili Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Chulalongkorn University Dept. of Computer Engineering Bangkok Thailand Harbin University of Science and Technology Higher Educational Key Laboratory Harbin China Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350390995

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Localization of Dengue Virus Likelihood Using Image Processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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EfficientNetB0-based Rice Disease Classification: A Strategic Fine-Tuning Approach 5

EfficientNetB0-based Rice Disease Classification: A Strategi...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Mishra, Shivani Yadav, Chavali Sai Sreeram Raju, Akansha Balasundaram, A. Shaik, Ayesha Vellore Institute of Technology Chennai Dept. of Computer Science and Engineering Chennai India Vellore Institute of Technology Chennai Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331504403

Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers' incomes and global food supply. While current detection systems face accuracy and robustness challenges, deep learning models, particularly the EfficientNetB0, show promise. Despite their sensitivity to noise and variations in leaf appearance, ongoing research addresses these issues. This study introduces the use of EfficientNetB0 for accurate classification, incorporating a fine-tuning strategy in a brief five-epoch training period. This approach optimally balances model adaptation and prevents overfitting, leveraging the deep neural network's ability to capture complex hierarchical features. The limited fine-tuning duration addresses potential risks while maximizing the model's robust features. This unique amalgamation of advanced architecture and tailored training strategy aims to achieve efficient and accurate disease classification. The research signifies a step toward revolutionizing rice disease detection, emphasizing the importance of striking a balance between cutting-edge methodologies and practical considerations in the pursuit of agricultural sustainability. Before Fine Tuning the Training Accuracy was 98.46%, Validation Accuracy was 99.15% and Test Accuracy was 98.44%.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be 99.77%, Validation Accuracy is 99.43% and Test Accuracy: 99.12% © 2024 IEEE.

关键词： Plant diseases

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Wind Energy Forecasts with Supervised Machine Learning Techniques

Wind Energy Forecasts with Supervised Machine Learning Techn...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Hussain, Mariam Ahammad, Kawsar Noor, Moshiur R. Sharmin, Nusrat Shafiul, Sumayea B. Islam, Shakarul Research of Integrated Green and Holistic Training Center Bogura Bangladesh Dept. of Electrical and Electronic Engineering Eastern University Dhaka Bangladesh Dept. of Computer Science and Engineering University of Science and Technology Chittagong Bangladesh Dept. of Epidemiology Bangladesh University of Health Sciences Dhaka Bangladesh

ISBN: (纸本)9781837242689

United Nations set clean and affordable energy (7th) sustainable development goals. Among renewables, wind energy requires frequent observations. Due to limited finance in developing countries like Bangladesh, accurate and wide observations are one of the major challenges in operation and maintenance for energy harvests. Thus, this paper developed energy forecast models with supervised machine learning (ML) algorithms: support vector regressor (SVR) and random forest (RF) and compared with linear regression (LR) model. With global wind atlas (GWA) datasets (heights: 10 to 200m), training and testing performed with SVR, RF, and LR models. Model performances evaluated with Pearson correlation, coefficient of determinations, mean absolute percentage (MAPE), and root mean square errors (RMSE). During training and testing, ML estimations outperformed LR model as MAPE indicated ≤15% and ≥50% respectively for global and local-scale datasets. For an agricultural land with limited observations, ML predictions were performed for Bogura city with NASA-POWER dataset representing an MAPE of 4.84% (SVR) and 0.91% (RF). At Bogura on average, the final output showed annual energy production of 189 Kilowatt where probability for wind speeds about 70% (3 to 6 m/s at 50m). In conclusion, ML model accuracy is efficient for successful energy operations in Bangladesh and other developing countries with limited observations. © The Institution of engineering & technology 2024.

关键词： Wind forecasting

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Efficient Lane Detection based on Feature Aggregation for Advanced Driver Assistance Systems

Efficient Lane Detection based on Feature Aggregation for Ad...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Jhong, Sin-Ye Ko, Ching-Hsiang Su, Yi-Feng Hua, Kai-Lung Chen, Yung-Yao National Cheng Kung University Dept. of Engineering Science Tainan Taiwan National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Automotive Research and Testing Center Research and Development Division Changhua Taiwan National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350302714

As we enter the era of smart cars and electric vehicles, the significance of autonomous driving systems, especially crucial components like lane detection technology, increases dramatically. However, advanced solutions often face daunting challenges, especially under adverse environmental and lighting conditions, yielding varying detection results. This variability prompts automakers to enforce numerous restrictions on auxiliary functions, often deterring potential customers from upgrading relevant services. This study addresses these limitations and introduces a sophisticated deep learning-based lane detection model to supersede existing technologies. The model fuses an efficient feature extraction network with a Recurrent Feature-Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). These elements work harmoniously, enabling enhanced global information extraction and fortifying detection capability under challenging conditions. The methodology of this study is segregated into three parts: Efficient Backbone and Neck for feature extraction, Global Feature Enhancement Decoder for maximizing feature map details, and the actual implementation of a Lane Assist System. These modules collectively help us calculate high-accuracy lane coordinates, thereby overcoming the limitations of previous lane detection methods. Following the successful creation and validation of the model, the study extends the implementation to the Hexagon vehicle control platform, where the Lane Departure and Lane Keeping assistance systems are deployed. The proposed technology is validated through rigorous experiments and analyses, utilizing the highly recognized TuSimple dataset, custom-collected Taiwanese scene data, and actual road tests. Results illustrate high and consistent detection performance under diverse environmental conditions, validating the technology's effectiveness and feasibility. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Channel Allocation in 5G Networks using Ant Colony Optimization Algorithm 6

Channel Allocation in 5G Networks using Ant Colony Optimizat...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Hussein, Areej Anbar, Mohammad Molhem, Mohammed Oudeh, Atheer Yousif Nassr, Mohammad Fardoun, Habib University of Tartous Dept. of Communication Technology Engineering Tartous Syria Al-Ayen University Thi-Qar Dept. of Computer Engineering Technology Iraq College of Education for Pure Science University of Thi-Qar Scientific Research Center Al-Ayen University Thi-Qar Dept. of Computer Science Iraq Canadian University Dubai United Arab Emirates

ISBN: (纸本)9798350382891

Efficient channel allocation is crucial for enabling concurrent calls and data sessions within a cellular network in general. When channels are inefficiently allocated, network congestion may occur leading to call blocking due to a lack of available channels. In this research, an Ant Colony Optimization (ACO) algorithm-based model has been proposed to tackle channel allocation problem in 5G cellular networks. The proposed model has been simulated using Python. The experimental study focused on minimizing Call Block Probability (CBP) by optimizing channel allocation as the number of network users increase. © 2024 IEEE.

关键词： Python

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Support Vector Machine Classification Model Based on Grey Wolf Optimizer 7

Support Vector Machine Classification Model Based on Grey Wo...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Han, Xinxin Wang, Jibin Zheng, Kaiyuan He, Dingkun Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Chang Chien College Nantong University Nantong China School of Computer Science Beijing Information Science & Technology University Beijing China School of National Defense Science Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper proposes a support vector machine (SVM) classification model based on the grey wolf optimizer (GWO) to solve high-dimensional nonlinear classification problems in industrial production and daily life. To address the sensitivity of traditional SVM to parameter settings, this study utilizes the global search capability and fast convergence characteristics of the grey wolf optimizer to optimize the key parameters of SVM, including the penalty parameter C and the width parameter g of the radial basis function (RBF). By using the best accuracy rate achieved through five-fold cross-validation as the target function, the precise adjustment of SVM parameters was realized. Experiments using the Italian wine dataset were conducted for verification, and the results showed that the GWO-SVM model achieved a 100% accuracy rate on the test set, proving that the model has high classification accuracy and good generalization ability in handling complex classification problems. ©2024 IEEE.

关键词： Radial basis function networks

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