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Deep CNN based brain tumor detection in intelligent systems

International Journal of Intelligent Networks 2024年第1期5卷 30-37页

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taiwan Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Department of Business Administration Asia University Taiwan Taiwan Dehradun India

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. © 2024 The Authors

关键词： Deep learning

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Learning Latent Structures in Network Games via data-Dependent Gated-Prior Graph Variational Autoencoders 41

Learning Latent Structures in Network Games via Data-Depende...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Xue Li, Muchen Leng, Yan Liao, Renjie Center for Applied Statistics School of Statistics Renmin University of China Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China Department of Computer Science University of British Columbia VancouverBC Canada McCombs School of Business The University of Texas at Austin AustinTX United States Vector Institute for AI Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)

关键词： Monte Carlo methods

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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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Computational Modeling of Multilevel Organizational Learning: From Conceptual to Computational Mechanisms 1

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2nd International Conference on Information Technology, InCITe 2022

作者： Canbaloğlu, Gülay Treur, Jan Wiewiora, Anna Department of Computer Engineering Koç University Istanbul Turkey Social AI Group Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Center for Safety in Healthcare Delft University of Technology Delft Netherlands School of Management QUT Business School Queensland University of Technology Brisbane Australia

ISBN: (数字)9789811973468

ISBN: (纸本)9789811973451

This paper addresses formalization and computational modeling of multilevel organizational learning, which is one of the major challenges for the area of organizational learning. It is discussed how various conceptual mechanisms in multilevel organizational learning as identified in the literature, can be formalized by computational mechanisms which provide mathematical formalizations that enable computer simulation. The formalizations have been expressed using a self-modeling network modeling approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Learning systems

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Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Fan, Qinglan Bi, Ying Xue, Bing Zhang, Mengjie School of Computer Science and Technology Shandong University of Finance and Economics Jinan China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

data-efficient image classification, which focuses on achieving accurate classification performance with limited labeled data, has garnered significant attention. Genetic programming (GP) has achieved impressive progress in image classification, particularly in scenarios involving small amounts of labeled data. GP research typically focuses on designing tree-based model representations to learn useful image features for classification. However, most GP methods are proposed for gray-scale images and ignore the color features. Furthermore, the existing GP methods typically learn features on a single scale/resolution, restricting potential accuracy enhancements. To address these issues, this paper proposes a new multi-tree GP In single-tree GP (or simply GP), each individual consists of a single tree. In contrast, in multi-tree GP, each individual comprises multiple trees. representation for image feature learning and classification. In each individual, three trees are included to extract discriminative features from the red, green, and blue channels of the image. With the new image resizing layer in the tree representation, the proposed approach can achieve multi-scale feature extraction, i.e., flexibly learning fine-grained details and coarse-grained structures in the image, improving the classification performance. In addition, since a limitation of GP is premature convergence due to a decline in population diversity, this paper develops a hybrid parent selection method consisting of tournament and lexicase selection to increase population diversity, find the best individual, and improve classification accuracy. The experiments on six image classification datasets indicate that the proposed approach outperforms state-of-the-art neural network-based and GP-based methods in almost all comparisons. Further analyses demonstrate the effectiveness of each component and the potentially high interpretability of the proposed approach. IEEE

关键词： Genetic programming

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An Unsupervised Network Architecture Search Method for Solving Partial Differential Equations

arXiv

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arXiv 2025年

作者： Li, Qing Chen, Jingrun School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Mathematical Sciences Suzhou Institute for Advanced Research University of Science and Technology of China China Suzhou Big Data & AI Research and Engineering Center China

Solving partial differential equations (PDEs) has been indispensable in scientific and engineering applications. Recently, deep learning methods have been widely used to solve high-dimensional problems. The interplay between deep learning and numerical PDEs has resulted in a variety of new methods, one of which is the physics-informed neural network (PINN). Typically, a deep learning method has three main components: a neural network, a loss function, and an optimizer. While the construction of the loss function is rooted in the definition of solution space, how to choose a optimal neural network is somewhat ad hoc, leaving much room for improvement. In the framework of PINN, we propose an unsupervised network architecture search method for solving PDEs, termed PINN-DARTS, which applies the differentiable architecture search (DARTS) to find the optimal network architecture structure in a given set of neural networks. In this set, the number of layers and the number of neurons in each layer can change. In the searching phase, both network and architecture parameters are updated simultaneously, so the running time is close to that of PINN with a pre-determined network structure. Unlike available works, our approach is unsupervised and purely based on the PDE residual without any prior usage of solutions. PINN-DARTS outputs the optimal network structure as well as the associated numerical solution. The performance of PINN-DARTS is verified on several benchmark PDEs, including elliptic, parabolic, wave, and Burgers’ equations. Compared to traditional architecture search methods, PINN-DARTS achieves significantly higher architectural accuracy. Another interesting observation is that both the solution complexity and the PDE type have a prominent impact on the optimal network architecture. Our study suggests that architectures with uneven widths from layer to layer may have superior performance across different solution complexities and different PDE types. Copyright © 202

关键词： Wave equations

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Investigation of sex-related functional connectivity alterations in autism using class imbalance mitigation approach 46

Investigation of sex-related functional connectivity alterat...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Namgung, Jong Young Mun, Jongmin Park, Yeong Jun Kim, Jaeoh Park, Bo-Yong Inha University Department of Data Science Incheon Korea Republic of University of Southern California Marshall School of Business Los Angeles United States Inha University Department of Computer Engineering Incheon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of

ISBN: (纸本)9798350371499

Autism spectrum disorder is primarily diagnosed in males, leading to the lack of understanding of brain disorganization in female individuals with autism. To fill the gap, we applied a Gaussian mixture model-based oversampling technique to the functional connectivity data to adjust for the sex imbalance in autism. Leveraging a dimensionality reduction technique, we generated a low-dimensional principal component (i.e., gradient) and assessed its between-group differences between the sexes. We observed significant sex-related differences in sensorimotor, attention, and default mode networks, which were associated with higher-order cognitive control processes. Transcriptomic association analysis provided a potential biological underpinning, specifying gene enrichment in the cortex, thalamus, and striatum. Finally, symptom severity prediction analysis suggested that the functional gradient was only associated with symptoms in female individuals with autism. © 2024 IEEE.

关键词： Dimensionality reduction

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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

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Journal of computer science & Technology 2022年第4期37卷 797-813页

作者： Hui Yu Xin-Yu Jiang Jin Zhao Hao Qi Yu Zhang Xiao-Fei Liao Hai-Kun Liu Fu-Bing Mao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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Contrastive Learning for Adapting Language Model to Sequential Recommendation 24

Contrastive Learning for Adapting Language Model to Sequenti...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Liang, Fei-Yao Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks. © 2024 IEEE.

关键词： Contrastive Learning

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