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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning

Multi-Granularity History and Entity Similarity Learning for...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mingcong, Shi Zhu, Chunjiang Zhang, Detian Wen, Shiting Li, Qing School of Computer Science and Technology Soochow University Suzhou China Department of Computer Science University of North Carolina Greensboro United States School of Computer and Data Engineering NingboTech University Ningbo China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Temporal Knowledge Graph (TKG) reasoning, aiming to predict future unknown facts based on historical information, has attracted considerable attention due to its great practical value. Insight into history is the key to predict the future. However, most existing TKG reasoning models singly capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. In this paper, we propose Multi-Granularity History and Entity Similarity Learning (MGESL) model for Temporal Knowledge Graph Reasoning, which models historical information from both coarse-grained and fine-grained history. Since similar entities tend to exhibit similar behavioural patterns, we also design a hypergraph convolution aggregator to capture the similarity between entities. Furthermore, we introduce a more realistic setting for the TKG reasoning, where candidate entities are already known at the timestamp to be predicted. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Lightweight Weight Update for Convolutional Neural Networks 1

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12th International Congress on Big data, Bigdata 2023

作者： Wang, Feipeng Ben, Kerong Zhang, Xian Yang, Meini College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer and Big Data Science Jiujiang University Jiujiang China

ISBN: (数字)9783031447259

ISBN: (纸本)9783031447242

Convolutional neural networks are usually composed of convolutional layers and pooling layers. Pooling operations effectively control the weight update of convolutional neural networks. The existing pooling operations result in a large number of weight parameter updates of convolutional neural networks, causing large memory usage. In this paper, a pooling operation called ApproxM is proposed to address this problem. The proposed pooling operation is a simple and similar to median pooling. It takes the mean of multiple values near the median as the pooling result, and CNNs only update the weights of these values during the back propagation. Finally, extensive experiments on benchmark datasets demonstrate that the proposed pooling operation achieves top-1 results of 92.65% and 68.24% and top-5 results of 99.84% and 91.31% model test accuracy on Cifar-10 and Cifar-100 based on ResNet-20, respectively, and the corresponding number of weight updates in an 8 × 8 pool is 4, which is better than other pooling techniques in the experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image recognition

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Towards Responsible Decisions with Limited Training data Using Human-in-the-Loop 20th

Towards Responsible Decisions with Limited Training Data Us...

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Image Inpainting Detection Based on High-Pass Filter Attention Network

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computer Systems science & engineering 2022年第12期43卷 1145-1154页

作者： Can Xiao Feng Li Dengyong Zhang Pu Huang Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409USA

Image inpainting based on deep learning has been greatly *** original purpose of image inpainting was to repair some broken photos, suchas inpainting artifacts. However, it may also be used for malicious operations,such as destroying evidence. Therefore, detection and localization of imageinpainting operations are essential. Recent research shows that high-pass filteringfull convolutional network (HPFCN) is applied to image inpainting detection andachieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, weintroduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtainmore information. Channel attention (cSE) is introduced in upsampling toenhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

关键词： Image inpainting detection spatial attention channel attention full convolutional network high-pass filter

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Modelling High-Order Social Relations for Item Recommendation (Extended Abstract) 39

Modelling High-Order Social Relations for Item Recommendatio...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Yang Chen, Liang He, Xiangnan Peng, Jiaying Zheng, Zibin Tang, Jie Sun Yat-sen University School of Computer Science and Engineering Guangzhou China University of Science and Technology of China School of Data Science School of Information Science and Technology Hefei China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350322279

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. © 2023 IEEE.

关键词： Online systems

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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Diseases

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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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science China(Information sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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