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Decision Aid Sciences and Application (DASA), International Conference on

作者： Mohammed Noori Mohsin Ahmed Amer Ibrahim Ali Saeed Mohammad Khishe Mahmoud Mahfuri Al-ma'moon University College Baghdad Iraq Department of Computer Science and Software Engineering College of Information Technology Computer Center Northern Technical University Ninevah Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer and information Technology Department International University for Science and Technology

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results.

关键词： Accuracy Magnetic resonance imaging Decision making Brain tumors Predictive models Brain modeling Data augmentation Data models Optimization Medical diagnostic imaging

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EFMLP: A Novel Model for Web Service QoS Prediction 16th

EFMLP: A Novel Model for Web Service QoS Prediction

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Ye, Kailing Yu, Huiqun Fan, Guisheng Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9783030675394

With the emergence of service-oriented architecture, quality of service (QoS) has become a crucial factor in describing the non-functional characteristics of Web services. In the real world, the user only requests limited Web services, the QoS record of Web services is sparsity. In this paper, we propose an approach named factorization machine and multi-layer perceptron model based on embedding technology (EFMLP) to solve the problem of sparsity and high dimension. First, the input data will be sent to embedding layer to reduce the data dimension. Then, the embedded feature vector will send to the factorization machine. After that, the first-order and second-order weights of the factorization machine are used as the initial weights of the first layer of the multi-layer perceptron. And the multi-layer perceptron is trained to adjust the weights. Finally, 1,974,675 pieces of data from an open dataset is used as experiment data to validate the model, and the result shows that our EFMLP model can predict QoS value accurately on the client side. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

arXiv

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arXiv 2022年

作者： Panayiotou, Tania Michalopoulou, Maria Ellinas, Georgios Department of Electrical and Computer Engineering KIOS Research Innovation Center of Excellence University of Cyprus Nicosia1678 Cyprus

he unprecedented growth of the global Internet traffic, coupled with the large spatio-temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are motivating the evolution from reactive to proactive and eventually towards adaptive optical networks. In these networks, traffic-driven service provisioning can address the problem of network over-provisioning and better adapt to traffic variations, while keeping the quality-of-service at the required levels. Such an approach will reduce network resource over-provisioning and thus reduce the total network cost. This survey provides a comprehensive review of the state of the art on machine learning (ML)-based techniques at the optical layer for traffic-driven service provisioning. The evolution of service provisioning in optical networks is initially presented, followed by an overview of the ML techniques utilized for traffic-driven service provisioning. ML-aided service provisioning approaches are presented in detail, including predictive and prescriptive service provisioning frameworks in proactive and adaptive networks. For all techniques outlined, a discussion on their limitations, research challenges, and potential opportunities is also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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A CPK-Based Identity Authentication Scheme for IoT

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computer Systems Science & engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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S^3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation 39

S<sup>3-Mamba</sup>: Small-Size-Sensitive Mamba for Lesion S...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Gui Li, Yuexiang Chen, Wenting Ding, Meidan Cheah, Wooi Ping Qu, Rong Ren, Jianfeng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Medical AI ReSearch (MARS) Group University Engineering Research Center of Digital Medicine and Healthcare Guangxi Medical University Guangxi Nanning China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (纸本)157735897X

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down-sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a Small-Size-Sensitive Mamba (S^3-Mamba), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S^3-Mamba, especially in segmenting small lesions. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Data Augmentation On-the-fly and Active Learning in Data Stream Classification

arXiv

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arXiv 2022年

作者： Malialis, Kleanthis Papatheodoulou, Dimitris Filippou, Stylianos Panayiotou, Christos G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another significant challenge is that of class imbalance. This work introduces the novel Augmented Queues method, which addresses the dual-problem by combining in a synergistic manner online active learning, data augmentation, and a multi-queue memory to maintain separate and balanced queues for each class. We perform an extensive experimental study using image and time-series augmentations, in which we examine the roles of the active learning budget, memory size, imbalance level, and neural network type. We demonstrate two major advantages of Augmented Queues. First, it does not reserve additional memory space as the generation of synthetic data occurs only at training times. Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size. Learning on-the-fly poses major challenges which, typically, hinder the deployment of learning models. Augmented Queues significantly improves the performance in terms of learning quality and speed. Our code is made publicly available. © 2022, CC BY-NC-SA.

关键词： Classification (of information)

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Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System

arXiv

引用

arXiv 2022年

作者： Pouyan, Rayhane Kalamati, Hadi Ebrahimian, Hannane Karrabi, Mohammad Akbarzadeh, Mohammad R.T. Department of Computer engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran

Movies are a great source of entertainment. However, the problem arises when one is trying to find the desired content within this vast amount of data which is significantly increasing every year. Recommender systems (RSs) can provide appropriate algorithms to solve this problem. The content-based technique has found popularity due to the lack of available user data in most cases. Content-based RSs are based on similarity of items demographic information;Term Frequency – Inverse Document Frequency (TF-IDF) and Knowledge Graph Embedding (KGE) are two approaches used to vectorize data to calculate these similarities. In this paper, we propose a weighted content-based movie RS by combining TF-IDF which is an appropriate approach for embedding textual data such as plot/description and KGE which is used to embed named entities such as director’s name. The weights between features are determined using a Genetic algorithm. Additionally, Iranian movies dataset is created by scraping data from movie-related websites. This dataset and the structure of the FarsBase KG are used to create the MovieFarsBase KG which is a component in the implementation process of the proposed content-based RS. Using precision, recall, and F1 score metrics, this study shows that the proposed approach outperforms the conventional approach that uses TF-IDF for embedding all attributes. © 2022, CC BY.

关键词： Genetic algorithms

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Cultivating Archipelago of Forests: Evolving Robust Decision Trees Through Island Coevolution 39

Cultivating Archipelago of Forests: Evolving Robust Decision...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zychowski, Adam Perrault, Andrew Mańdziuk, Jacek Faculty of Mathematics and Information Science Warsaw University of Technology Poland Department of Computer Science and Engineering The Ohio State University United States Faculty of Computer Science AGH University of Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Poland

ISBN: (纸本)157735897X

Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets, demonstrating its superior performance compared to state-ofthe-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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