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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

IEEE Access 2025年 13卷 37724-37736页

作者： Alharbi, Fadi Vakanski, Aleksandar Zhang, Boyu Elbashir, Murtada K. Mohammed, Mohanad University of Idaho College of Engineering Department of Computer Science MoscowID83844 United States Jouf University College of Computer and Information Sciences Department of Information Systems Al-Jouf Sakaka72441 Saudi Arabia University of KwaZulu-Natal School of Mathematics Statistics and Computer Science Pietermaritzburg3209 South Africa

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection;hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. Experimental validation is performed with a dataset of 8,464 samples from 31 cancer types and normal tissue, comprising messenger-RNA, micro-RNA, and DNA methylation data. The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. The findings also suggest that correlation-based graph structures enhance the models' ability to identify shared cancer-specific signatures across patients in comparison to protein-protein interaction networks-based graph structures. The code and data used in this study are available in the link (https://***/FadiAlharbi2024/Graph_Based_***). © 2013 IEEE.

关键词： Graph neural networks

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Human Pose Estimation and Event Recognition via Feature Extraction and Neuro-Fuzzy Classifier

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IEEE Access 2025年 13卷 37328-37343页

作者： Hanzla, Muhammad Alshammari, Naif S. Alharbi, Shuaa S. Wahid, Wasim Almujally, Nouf Abdullah Jalal, Ahmad Liu, Hui Air University Department of Computer Science Islamabad44000 Pakistan Majmaah University College of Computer and Information Sciences Department of Computer Science Al Majma’ah11952 Saudi Arabia Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems P.O. Box 84428 Riyadh11671 Saudi Arabia Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Lab Bremen28359 Germany

This paper introduces an advanced framework for Human Pose Estimation (HPE) and Semantic Event Classification (SEC), addressing the growing demand for sophisticated human skeleton modeling, context-aware feature extraction, and machine learning techniques for accurate event recognition in real-world scenarios. HPE, a fundamental task in applications such as sports analytics and surveillance systems, involves predicting human joint locations from visual data. Despite substantial progress driven by deep learning advancements, particularly in handling occlusions and crowded environments, a comprehensive integration of these innovations remains limited in current literature. To bridge this gap, we propose a novel HPE and SEC system featuring a robust six-stage pipeline. The workflow begins with preprocessing steps, including video-to-image sequence conversion, floor removal to reduce noise, grayscale transformation, and human silhouette extraction via binary masks. Human detection is performed using the GrabCut algorithm. Full-body feature extraction incorporates 1-D Fourier Transform, 2-D energy-based motion analysis, 0–180° intensity mapping via the Radon Transform algorithm, and movable body part detection using Fourier-Based Correlation. Key point features are characterized through the degree of freedom, human landmark detection via the HSV algorithm, and angular point analysis using the media pipe algorithm. Features are then optimized using the Ray Optimizer and classified through a Neuro-Fuzzy classifier. The proposed system achieves classification accuracies of 94.82%, 92.33%, and 93.63% on the UCF Sports Actions, Human Action Recognition (HAR), and MPII Human Pose datasets, respectively, outperforming state-of-the-art methods and demonstrating its effectiveness in tackling real-world challenges. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/

关键词： Sports

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Memory Complexity of Estimating Entropy and Mutual Information

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IEEE Transactions on Information Theory 2025年第5期71卷 3334-3349页

作者： Berg, Tomer Ordentlich, Or Shayevitz, Ofer Tel Aviv University Department of Electrical Engineering—Systems Tel Aviv-Yafo69978 Israel Hebrew University of Jerusalem School of Computer Science and Engineering Jerusalem91904 Israel

We observe an infinite sequence of independent identically distributed random variables X1, X2, . . . drawn from an unknown distribution p over [n], and our goal is to estimate the entropy H(p) = − E[log p(X)] within an Ε-additive *** that end, at each time point we are allowed to update a finite-state machine with S states, using a possibly randomized but time-invariant rule, where each state of the machine is assigned an entropy estimate. Our goal is to characterize the minimax memory complexity S∗ of this problem, which is the minimal number of states for which the estimation task is feasible with probability at least 1 − δ asymptotically, uniformly in ***, we show that there exist universal constants C1and C2 such that S∗ (Formula presented) for Ε not too small, andS∗ (Formula presented) for Ε not too large. The upper bound is proved using approximate counting to estimate the logarithm of p, and a finite memory bias estimation machine to estimate the expectation operation. The lower bound is proved via a reductionof entropy estimation to uniformity testing. We also apply these results to derive bounds on the memory complexity of mutual information estimation. © 1963-2012 IEEE.

关键词： Entropy Estimation Complexity theory Memory management Mutual information Lower bound Additives Upper bound Testing Automata

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Lu, Benxue Gao, Kaizhou Duan, Peiyong Slowik, Adam School of Computer Science Liaocheng University Liaocheng252000 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Information and Automation Engineering Qilu University of Technology Jinan250353 China Koszalin University of Technology Department of Electronics and Computer Science Koszalin75-453 Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different O-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with O-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains. © 2021 TUP.

关键词： Energy utilization

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A Novel Framework for Gauging Information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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UAV-Assisted Zero Knowledge Model Proof for Generative AI: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13441-13454页

作者： Hao, Min Shang, Chen Wang, Siming Jiang, Wenchao Nie, Jiangtian Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University School of Computer Science and Engineering 638788 Singapore

As more users seek generative AI models to enhance work efficiency, generative AI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize generative AI models provided by the service provider, they cannot be certain that the model's quality matches the provider's claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multi-agent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on non-interactive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an age of verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a trade-off optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multi-agent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500ms during the verification phase, meeting practical requirements. © 2025 IEEE.

关键词： Generative adversarial networks

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Virtual-physical digital twin testbed for heterogeneous crowd operations

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science China(Information sciences) 2025年第5期68卷 404-406页

作者： Mingliang XU Zheng WANG Yang MENG Wencan LUO Hua WANG Shuo HE Chaochao LI Yibo GUO Yafei LI Pei LV School of Computer Science and Artificial Intelligence Zhengzhou University Engineering Research Center of Intelligent Swarm Systems Ministry of Education National Supercomputing Center in Zhengzhou

Heterogeneous crowd operations involve complex procedural subtasks performed by dynamic teams with diverse agent behaviors,tailored to specific task *** of such operations include carrier aircraft support,airport ground handling,and logistics *** a hybrid virtual-physical digital twin testbed for scenario generation and plan verification in heterogeneous crowd operations addresses the issues of low credibility in virtual simulations and the high costs associated with real-world *** is becoming increasingly important in practical applications.

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A Global Approach to Artificial Intelligence

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IEEE Access 2025年 13卷 76946-76962页

作者： De Miguel, Ana M. Gonzalez Sarasa-Cabezuelo, Antonio Complutense University of Madrid Computer Science Faculty Department of Software Engineering and Artificial Intelligence Madrid Spain Complutense University of Madrid Computer Science Faculty Department of Information Systems and Computing Madrid Spain

This paper presents the initial foundations of a new Global approach to Artificial Intelligence based on the modeling of global intelligence and the development of artificial cooperative systems to support this. The research work brings numerous investigations and some of our most recent creations to develop intelligent systems in urgent global, cooperative domains. The investigations present an overall view of current issues like true natural i.e. global intelligence. The creations include several results in global solutions explorations as well as new models, services, architectures and processes which could be of special interest for global organizations that urgently need to achieve intelligent cooperation. The major aim of the approach is to help these organizations, and other related institutions, to succeed in their complex daily duties where true thinking, more natural intelligence and new global artificial systems are required. Further, the paper provides some guidelines for the next evolutions of the work in the form of a brief, open research agenda and, some general actions for deploying Global Artificial Intelligence into our organizations, suggesting how to build new global artificial intelligent systems in real and virtual ecosystems. © 2013 IEEE.

关键词： engineering education

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