Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma by Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, Xuemei Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen

“…Results The integrated multi-omics approach has identified PLG as a critical node within the gene regulatory network associated with Anoikis resistance and immunometabolic phenotypes. …”
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Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks by Nadia Shamshad, Lei Wang, Kiran Saleem, Danish Sarwr, Salil Bharany, Ahmad Almogren, Jaeyoung Choi, Ateeq Ur Rehman, Ayman Altameem

“…This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. …”
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GuardianAI: Privacy-preserving federated anomaly detection with differential privacy by Abdulatif Alabdulatif

“…In the rapidly evolving landscape of cybersecurity, privacy-preserving anomaly detection has become crucial, particularly with the rise of sophisticated privacy attacks in distributed learning systems. Traditional centralized anomaly detection systems face challenges related to data privacy and scalability, making federated learning a promising alternative. …”
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Environment Semantic Communication: Enabling Distributed Sensing Aided Networks by Shoaib Imran, Gouranga Charan, Ahmed Alkhateeb

“…To overcome these limitations, our paper proposes the deployment of multiple distributed sensing nodes, each equipped with an RGB camera. These nodes focus on extracting environmental semantics from the captured RGB images. …”
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An Ensemble-Based Classifier to Determine the Harmonic Contribution Responsibility Between Utility’s Grid and Microgrids by Matheus C. Mella, Ricardo A. S. Fernandes, Daniel Barbosa

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Global confidence degree based graph neural network for financial fraud detection by Jiaxun Liu, Yue Tian, Guanjun Liu

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MambaCAttnGCN+: a comprehensive framework integrating MambaTextCNN, cross-attention and graph convolution network for piRNA-disease association prediction by Dengju Yao, Xiangkui Li, Xiaojuan Zhan, Bo Zhang, Jian Zhang

“…Utilizing the Mamba module, we developed an innovative sequence embedding model, MambaTextCNN, to extract features from piRNA sequences, which we used as node attributes within the heterogeneous graph. A heterogeneous graph convolution method was then applied to identify potential associations between piRNAs and diseases, with cross-attention mechanisms further enhancing node features. …”
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Ordered embeddings and intrinsic dimensionalities with information-ordered bottlenecks by Matthew Ho, Xiaosheng Zhao, Benjamin D Wandelt

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Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks by Kashi Sai Prasad, E. Laxmi Lydia, M. V. Rajesh, K. Radhika, Janjhyam Venkata Naga Ramesh, N. Neelima, Srinivasa Rao Pokuri

“…In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. …”
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Research on application of artificial intelligence safety production management and control platform in Shendong mining area by Yazhong CUI, Jianrong HE, Yanyan REN

“…The initial formation includes infrastructure, AI development framework, dataset, AI training, AI deployment The AI service capability and business application of the Shendong mining area's artificial intelligence platform architecture are bottom-up. Supervised learning, semi supervised learning, transfer learning and other technologies are applied to improve the efficiency and quality of model training. …”
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Behavior Modeling-Based QoS Preserving Model With Intelligent Trust Ranking for 6G by Santosh Sharma, Rakesh Kumar Jha, Anil Kumar

“…Machine learning is used to provide trust ranking to all users, where different ways of communication are present in another layer. …”
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A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle by Shayan Hassanpour, Matthias Hummert, Dirk Wubben, Armin Dekorsy

“…Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple error-prone and rate-limited channels towards a (remote) processing unit. …”
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A cooperative intrusion detection system for internet of things using fuzzy logic and ensemble of convolutional neural networks by Xiongwei Qiu, Lianzhi Shi, Pengtong Fan

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I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection by Ibrahim Gulatas, H. Hakan Kilinc, A. Halim Zaim, M. Ali Aydin

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Limits of Depth: Over-Smoothing and Over-Squashing in GNNs by Aafaq Mohi ud din, Shaima Qureshi

“…Graph Neural Networks (GNNs) have become a widely used tool for learning and analyzing data on graph structures, largely due to their ability to preserve graph structure and properties via graph representation learning. …”
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PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences by Amin Khodaei, Sania Eskandari, Hadi Sharifi, Behzad Mozaffari-Tazehkand

“…The analysis of viral genomic sequences can be efficacious in evaluating the present and potentially forthcoming condition of viruses. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. …”
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Heterogeneous Graph Neural-Network-Based Scheduling Optimization for Multi-Product and Variable-Batch Production in Flexible Job Shops by Yuxin Peng, Youlong Lyu, Jie Zhang, Ying Chu

“…In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinforcement learning. …”
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PictureGuard: Enhancing Software-Defined Networking–Internet of Things Security with Novel Image-Based Authentication and Artificial Intelligence-Powered Two-Stage Intrusion Detect... by Hazem (Moh’d Said) Hatamleh, As’ad Mahmoud As’ad Alnaser, Said S. Saloum, Ahmed Sharadqeh, Jawdat S. Alkasassbeh

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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
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