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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

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computer Systems science & engineering 2023年第6期45卷 3215-3229页

作者： Aghila Rajagopal Sultan Ahmad Sudan Jha Ramachandran Alagarsamy Abdullah Alharbi Bader Alouffi Department of Computer Science and Business Systems Sethu Institute of TechnologyKariapattiVirudhunagar626115TamilnaduIndia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science and Engineering School of EngineeringKathmandu UniversityBanepaKathmanduNepal Department of CSE University College of EngineeringPanrutiTamilnaduIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

关键词： Covid-19 CT images multi-scale improved ResNet AI inception 14 and VGG-16 models

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Contrastive Domain Adaptation for Time-Series Via Temporal Mixup

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1185-1194页

作者： Eldele, Emadeldeen Ragab, Mohamed Chen, Zhenghua Wu, Min Kwoh, Chee-Keong Li, Xiaoli School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Centre for Frontier Ai Research Agency for Science Technology and Research Singapore138632 Singapore Institute for Infocomm Research Agency for Science Technology and Research Singapore138632 Singapore

Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains toward a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. Impact Statement-Unsupervised domain adaptation (UDA) aims to reduce the gap between two related but shifted domains. CurrentUDAmethods for time-series data are based on adversarial or discrepancy approaches. Thesemethods are complex in training and cannot efficiently address the large domain shift. Therefore, in this work, we propose a time-series UDA framework based purely on contrastive learning, which is simpler in implementation and training. To leverage contrastive learning to mitigate domain shift, we propose a temporal mixup strategy to generate augmentations that are robust to the domain shift and can move both domains towards an intermediate

关键词： Semantics

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Towards efficient generative AI and beyond-AI computing:New trends on ISSCC 2024 machine learning accelerators

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Journal of Semiconductors 2024年第4期45卷 12-15页

作者： Bohan Yang Jia Chen Fengbin Tu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and TechnologyHong KongChina School of the Gifted Young University of Science and Technology of ChinaHefei 230026Chin

Compared to the last decade when the convolution neu-ral network(CNN)dominated the research field,machine learn-ing(ML)algorithms have reached a pivotal moment called the generative artificial intelligence(AI)*** the emer-gence of large-scale foundation models[1],such as large multi-modal model(LMM)GPT-4[2]and text-to-image generative model DALL·E[3].

关键词： ISSCC beyond AI

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A Segmentation-Based Scheme to Expedite Block Propagation in Blockchain Networks 21

A Segmentation-Based Scheme to Expedite Block Propagation in...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Qiu, Zhihan Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Liu, Yi Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology Taipa China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate. © 2024 IEEE.

关键词： Blockchain

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Exploring the Design Space of Unsupervised Blocking with Pre-trained Language Models in Entity Resolution 9th

Exploring the Design Space of Unsupervised Blocking with Pre...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Sun, Chenchen Jin, Yuyuan Xu, Yang Shen, Derong Nie, Tiezheng Wang, Xite School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (纸本)9783031466601

Entity resolution (ER) finds records that refer to the same entities in the real world. Blocking is an important task in ER, filtering out unnecessary comparisons and speeding up ER. Blocking is usually an unsupervised task. In this paper, we develop an unsupervised blocking framework based on pre-trained language models (B-PLM). B-PLM exploits the powerful linguistic expressiveness of the pre-trained language models. A design space for B-PLM contains two steps. (1) The Record Embedding step generates record embeddings with pre-trained language models like BERT and Sentence-BERT. (2) The Block Generation step generates blocks with clustering algorithms and similarity search methods. We explore multiple combinations in above two dimensions of B-PLM. We evaluate B-PLM on six datasets (Structured + dirty, and Textual). The B-PLM is superior to previous deep learning methods in textual and dirty datasets. We perform sufficient experiments to compare and analyze different combinations of record embedding and block generation. Finally, we recommend some good combinations in B-PLM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data integration

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Machine Learning-Based Fault-Tolerant Inertial Navigation System for Micro Tunnel Boring Machines

Machine Learning-Based Fault-Tolerant Inertial Navigation Sy...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Roudra, Nafis Ashraf Saha, Neelavro Sakib, Saadman Al Mamun, Rahat Shithil, Shaekh Mohammad Iqbal, M Shahriar Tasnim, Rumana BRAC University Department of Computer Science and Engineering Bangladesh Independent University Department of Computer Science and Engineering Bangladesh School of Mechanical Engineering Universiti Teknologi Malaysia Malaysia Rajshahi University of Engineering & Technology Department of Mechanical Engineering Bangladesh World University of Bangladesh Department of Mechatronics Engineering Bangladesh

ISBN: (纸本)9798350369106

Underground tunneling poses a navigation challenge as Micro Tunnel Boring Machines (mTBMs) operate in GPS denied environments. In such settings, Inertial Measurement Units (IMUs) offer an alternative navigation solution. However, faults in mTBM operation caused by various environmental and operational factors lead to inaccuracies in IMU data. This paper presents a machine learning-based fault tolerance system for IMU based mTBM navigation. IMU data corresponding to four specific mTBM faults: vibration, high temperature, magnetic interference and mTBM precession were collected under rigorous testing in a lab environment. Support Vector Machine (SVM) was applied to classify these faults and predict the mTBM's operational state based on the IMU readings. The results demonstrate the effectiveness of the SVM model in identifying and classifying the faults, thereby enhancing the reliability and safety of mTBM operations in challenging environments. © 2024 IEEE.

关键词： Inertial navigation systems

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Differential Evolution Algorithm Based Hyper-Parameters Selection of Convolutional Neural Network for Speech Command Recognition 15

Differential Evolution Algorithm Based Hyper-Parameters Sele...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Dhar, Sandipan Sen, Anuvab Bandyopadhyay, Aritra Jana, Nanda Dulal Ghosh, Arjun Sarayloo, Zahra Computer Science and Engineering National Institute of Technology West Bengal Durgapur India Electronics and Telecommunication Indian Institute of Engineering Science and Technology Shibpur Howrah India Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah India School of Computer Science University of Waterloo ON Canada

ISBN: (纸本)9789897586743

Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Convolutional Neural Networks (CNNs) in SCR tasks, their efficacy relies heavily on hyperparameter selection, which is typically laborious and time-consuming when done manually. This paper introduces a hyperparameter selection method for CNNs based on the Differential Evolution (DE) algorithm, aiming to enhance performance in SCR tasks. Training and testing with the Google Speech Command (GSC) dataset, the proposed approach showed effectiveness in classifying speech commands. Moreover, a comparative analysis with Genetic Algorithm-based selections and other deep CNN (DCNN) models highlighted the efficiency of the proposed DE algorithm in hyperparameter selection for CNNs in SCR tasks. © 2023 by SCITEPRESS – science and technology Publications, Lda.

关键词： Genetic algorithms

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Genetic Algorithm Modeling and Analysis of Acyclic Coloring in High Chromatic Graphs 3

Genetic Algorithm Modeling and Analysis of Acyclic Coloring ...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Navis, V. Vinitha Marappan, Raja School of Advanced Sciences Vellore Institute of Technology Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331543358

The acyclic coloring problem, a specialized area within graph coloring, has numerous applications across diverse fields. It involves assigning colors to the vertices of a graph such that no two adjacent vertices share the same color and no bichromatic cycles exist. This problem becomes particularly challenging in high chromatic graphs. In this paper, we introduce a genetic algorithm (GA) with structure-aware selection, crossover, and mutation operators to enhance performance on some benchmark graphs having high chromatic numbers. Compared to the state-of-the-art methods, the proposed GA achieves a 5% to 12.5% reduction in the acyclic chromatic number. Success rates remain high, exceeding 90% for queen graphs, 80% to 90% for Leighton graphs, and 70% to 80% for random graphs. High crossover rates, approximately 17000 to 20000 on an average, maintain diversity, while adaptive mutation strategies enhance convergence. These findings highlight the advantage of integrating structural awareness into evolutionary algorithms for efficient acyclic coloring solutions. © 2025 IEEE.

关键词： Genetic algorithms

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Judicial nested named entity recognition method with MRC framework

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 118-126页

作者： Zhang, Hu Guo, Jiayu Wang, Yujie Zhang, Zhen Zhao, Hansen School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Department of Computation Science and Engineering George Institute of Technology Atlanta United States

Judicial named entity recognition (JNER) is a basic task of judicial intelligence and judicial service informatization. At present, the research of JNER has attracted extensive attention. However, the existing JNER methods usually can only assign a single label to a token in the input sequence, which is not applicable to nested entities where a token may be assigned two or more different labels at the same time. Therefore, this paper introduces the machine reading comprehension (MRC) framework into JNER, and proposes a judicial nested NER method based on the MRC. Firstly, we design the question template according to the characteristics of judicial nested named entities, and construct the legal text named entity dataset in MRC format. Next, we introduce the span extraction MRC model based on the pre-trained to encode the question and text, and learn the context knowledge of the entity in the question. Finally, we extract the starting and end positions of the matching span respectively through two classifiers, to get the corresponding entities. The experimental results on the information extraction dataset in "CAIL2021" show, compared with the existing baseline models, the proposed method effectively improves the recognition effect of nested entities commonly existing in the judicial field. © 2023

关键词： Informatization

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