Adaptive Network Research Articles

Multimodal fake news detection model using improved heuristic approach with optimal weighted integration and dilated adaptive deep learning

A lot of fake news is published with the help of television and social media. As a consequence, we are affected by the disinformation and misinformation spreading in our community. It is implemented to identify fake news creators to prevent spreading misinformation about the people. Thus, it is published like real news for the reputation and finances of an individual. It is challenging, because that is created by the integration of real and false details, and images are attached like originals to confuse the public. Few fake news detection tools are available to detect fake information. To solve this fake news spreading problem, an effectual multimodal fake news detection system is proposed based on the deep learning technique. In the beginning, the input image and text are gathered from benchmark data sources. Consequently, the deep features are extracted from raw images and text. The dilated Visual Geometry Group 16 (VGG16) is adopted to extract the image features and similarly, the text features are retrieved from the Dilated Text Convolutional Neural Network (DTCNN). After achieving two different features, it is upgraded into weighted fused features, in which the weight is tuned by an Adaptive Controlling Parameter-based Chameleon Swarm Algorithm (ACP-CSA). Finally, the fused features are fed as given to the Dilated Adaptive Deep Temporal Convolution Network with Bi-directional Long Short-Term Memory (DADTCN-Bi-LSTM) for predicting the fake news. The analysis is further performed by tuning the parameters in the model using developed ACP-CSA. The efficiency of the model is investigated and results are conducted. Thus, the analysis of the suggested system shows 95 regarding accuracy, sensitivity, and specificity. The analysis of the F1-score achieves the value of 90 in the developed model. Hence, the findings demonstrate that it achieves a better detection process to evade the existence of misinformation.

Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning

This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.

Flexible and Recyclable Epoxy Vitrimer With Improved Thermal Conductivity for Thermal Management

ABSTRACTThe accelerated evolution of electronic devices has resulted in an increased demand for thermal management materials. In this study, methoxy polyethylene glycol (MPEG) was introduced into a covalent adaptable epoxy network. It is demonstrated that the epoxy material becomes softer as the amount of MPEG content is increased, specifically exhibiting excellent flexibility with an impressive elongation at break of 444.68%. The thermal conductivity of the epoxy resin was enhanced by the incorporation of boron nitride (BN) filler, and the findings demonstrated that the thermal conductivity of the composites was markedly elevated to 3.34 Wm−1 K−1 when the proportion of BN filler reached 50%. The potential use of such composites as thermal management materials is demonstrated in their application for heat dissipation in LED chips. In addition, the covalent adaptive network enables the decomposition of the composites prepared in this study under chemical conditions, with the fillers subsequently being separated by physical filtration. This approach offers a potential pathway toward the sustainable development of thermal management materials.

RUL Prediction for Lithium Battery Systems in Fuel Cell Ships Based on Adaptive Modal Enhancement Networks

With the widespread application of fuel cell technology in the fields of transportation and energy, Battery Management Systems (BMSs) have become one of the key technologies for ensuring system stability and extending battery lifespan. As an auxiliary power source in fuel cell systems, the prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for enhancing the reliability and efficiency of fuel cell ships. However, due to the complex degradation mechanisms of lithium batteries and the actual noisy operating conditions, particularly capacity regeneration noise, accurate RUL prediction remains a challenge. To address this issue, this paper proposes a lithium battery RUL prediction method based on an Adaptive Modal Enhancement Network (RIME-VMD-SEInformer). By incorporating an improved Variational Mode Decomposition (VMD) technique, the RIME algorithm is used to optimize decomposition parameters for the adaptive extraction of key modes from the signal. The Squeeze-and-Excitation Networks (SEAttention) module is employed to enhance the accuracy of feature extraction, and the sparse attention mechanism of Informer is utilized to efficiently model long-term dependencies in time series. This results in a comprehensive prediction framework that spans signal decomposition, feature enhancement, and time-series modeling. The method is validated on several public datasets, and the results demonstrate that each component of the RIME-VMD-SEInformer framework is both necessary and justifiable, leading to improved performance. The model outperforms the state-of-the-art models, with a MAPE of only 0.00837 on the B0005 dataset, representing a 59.96% reduction compared to other algorithms, showcasing outstanding prediction performance.

Bidirectional synapse based on anti-resonance modulation of a photonic microring

A synaptic device is fundamental for memory and learning functions of neural networks. In this work, we demonstrated a GST (Ge2Sb2Te5)-based microring synapse with nonvolatile reconfigurable characteristics. The device shows bidirectional weight modulation during unidirectional crystallization or amorphization of GST, simulating long-term depression (LTD) and long-term potentiation (LTP). In addition, we adopted an anti-resonance pump scheme to reduce the fluctuations in pump energy coupled into the device to less than 12%. This scheme significantly enhances the programming precision of the microring synapse, achieving 24 resolvable states over 15 cycles. This work holds promise for laying the foundation for novel, to the best of our knowledge, photonic computing architectures and provides possibilities for the implementation of vision and adaptive optical neural networks that rely on bidirectional plasticity.

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the interactions between nodes. To address this issue, a schizophrenia classification model based on a three-dimensional adaptive graph convolutional neural network (3D-AGCN) is proposed. Each subject’s EEG data is divided into various segment lengths and frequency bands for the experiment. The attention mechanism is then used to integrate the node features in the spatial, feature, and frequency band dimensions. The resulting adaptive brain functional network features are then constructed and fed into the GAT + GCN model. This adaptive approach eliminates the human-specified criteria for feature selection and brain network construction. The trial results demonstrated that, when using a 6-second segment length and time-domain and frequency-domain features, patients with first-episode schizophrenia achieved the highest classification accuracy of 87.64% This method outperforms other feature selection and brain network modeling approaches, providing new insights and directions for the early diagnosis and recognition of schizophrenia.

Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet)

This paper introduces a new probabilistic composite model for the detection of zero-day exploits targeting the capabilities of existing anomaly detection systems in terms of accuracy, computational time, and adaptability. To address the issues mentioned above, the proposed framework consisted of three novel elements. The first key innovations are the introduction of “Adaptive WavePCA-Autoencoder (AWPA)” for pre-processing stage which address the denoising and dimensionality reduction, and contributes to the general dependability and accuracy of zero-day exploit detection. Additionally, a novel “Meta-Attention Transformer Autoencoder (MATA)” for enhancing feature extraction which address the subtlety issue, and improves the model’s ability and flexibility to detect new security threats, and a novel “Genetic Mongoose-Chameleon Optimization (GMCO)” was introduced for effective feature selection in the case of addressing the efficiency challenges. Furthermore, a novel “Adaptive Hybrid Exploit Detection Network (AHEDNet)” was introduced which address the dynamic ensemble adaptation issue where the accuracy of anomaly detection is very high with low false positives. The experimental results show the proposed model outperforms the other models of dataset 1 in accuracy of 0.988086 and 0.990469, precision of 0.987976 and 0.990628, recall of 0.988298 and 0.990435, with the lowest Hamming Loss of 0.011914 and 0.009531, also, the proposed model outperforms the other models of dataset 2 in accuracy of 0.9819 and 0.9919, precision of 0.9868 and 0.9968, recall of 0.9813 and 0.9923, with the lowest Hamming Loss of 0.0209 and 0.0109, thus the proposed model outperformed the other models in detecting zero-day exploits.

Deep learning powered single-cell clustering framework with enhanced accuracy and stability

Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this exploration, allows for the identification of distinct cell types within a population. Graph-based deep clustering methods have shown promise in preserving the structural relationships between cells (nodes) within the data. However, these methods often neglect the inherent distribution of nodes in the graph, leading to incomplete representations of cell populations. Additionally, conventional graph convolutional networks (GCNs) can suffer from oversmoothing, a phenomenon where the network loses the ability to differentiate between samples with similar expression profiles. To address these limitations, we proposed scG-cluster, an innovative deep structural clustering method. This method incorporates two key innovations: (1) Dual-topology adjacency graph: scG-cluster integrates information about node distribution into the traditional adjacency graph used by GCNs. This enriches the graph representation by capturing the spatial relationships between cells in addition to their pairwise similarities. (2) Dual-topology adaptive graph convolutional network (TAGCN): The framework employs a TAGCN architecture with residual concatenation. This network utilizes an attention mechanism to dynamically weight features within the graph, focusing on the most informative aspects for clustering. Additionally, residual connections are implemented to combat oversmoothing, ensuring the network retains the ability to distinguish between subtle differences in cell expression profiles. Furthermore, scG-cluster iteratively refines the clustering centers, leading to enhanced stability and accuracy in the final cluster assignments. Extensive evaluations on six diverse scRNA-seq datasets demonstrate that scG-cluster consistently outperforms existing state-of-the-art methods in terms of both clustering accuracy and scalability. Ablation studies are also conducted to validate the significant contributions of both the residual connections and the attention mechanism to the overall performance of the model. The source code for scG-cluster is publicly available at https://github.com/xixi-wq/scG-cluster.

Leveraging Community-based Approaches for Enhancing Resource Allocation in Fog Computing Environment

Efficient resource allocation in fog computing environments is essential to address the increasing demand for high-performance and adaptable network services. Traditional methods lack granular differentiation based on traffic characteristics often resulting in suboptimal bandwidth utilization and elevated latency. To enhance network efficiency, this study applies a community-based resource allocation approach leveraging the Louvain algorithm to dynamically cluster network nodes with similar traffic demands. By forming communities based on bandwidth and latency needs, this approach enables a targeted resource distribution, aligning each community with optimized pathways that address specific requirements. The results indicate notable performance gains, including a 14% increase in bandwidth utilization affecting the download and a reduction in latency by an average of 23% for time-sensitive applications. These improvements highlight the effectiveness of the proposed approach in managing diverse network demands, improving data flow stability, and enhancing the overall performance of fog computing infrastructures. These findings underscore the potential for community-based resource allocation to support scalable, adaptable, and secure resource management, positioning it as a viable solution to meet the complex needs of IoT and other distributed network systems.

Cancer drug resistance as learning of signaling networks.

Drug resistance is a major cause of tumor mortality. Signaling networks became useful tools for driving pharmacological interventions against cancer drug resistance. Signaling datasets now cover the entire human cell. Recently, network adaptation became understood as a learning process. We review rapidly increasing evidence showing that the development of cancer drug resistance can be described as learning of signaling networks. During drug adaptation, the network forgets drug-affected pathways by desensitization and relearns by strengthening alternative pathways. Thus, resistant cancer cells develop a drug resistance memory. We show that all key players of cellular learning (i.e., IDPs, protein translocation, microRNAs/lncRNAs, scaffolding proteins and epigenetic/chromatin memory) have important roles in the development of cancer drug resistance. Moreover, all of them are central components of the epithelial-mesenchymal transition leading to metastases and resistance. Phenotypic plasticity was recently listed as a hallmark of cancer. We review how network plasticity induces rare, pre-existent drug-resistant cells in the absence of drug treatment. Key network methods assessing the development of drug resistance and network pharmacological interventions against drug resistance are summarized. Finally, we highlight the class of cellular memory drugs affecting cellular learning and forgetting, and we summarize current challenges to prevent or break drug resistance using network models.

Spatiotemporal interactive learning dynamic adaptive graph convolutional network for traffic forecasting

Spatiotemporal interactive learning dynamic adaptive graph convolutional network for traffic forecasting

MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images

MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images

Adaptive reconstruct feature difference network for open set domain generalization fault diagnosis

Adaptive reconstruct feature difference network for open set domain generalization fault diagnosis

A deep adaptive bidirectional generative adversarial neural network (Bi-GAN) for groundwater contamination source estimation

A deep adaptive bidirectional generative adversarial neural network (Bi-GAN) for groundwater contamination source estimation

Integrated adaptive communication in multi-agent systems: Dynamic topology, frequency, and content optimization for efficient collaboration

In multi-agent systems (MAS), effective communication is essential for coordination and achieving common goals, especially in complex environments. However, existing communication methods face significant challenges, such as high resource consumption and limited adaptability to dynamic environments. To address these, we propose the integrated adaptive communication network (IACN) built on multi-agent proximal policy optimization (MAPPO), which enhances communication efficiency and adaptability in MAS. IACN dynamically adjusts communication topology using a learnable graph and optimizes content based on task relevance. Additionally, it incorporates an adaptive frequency adjustment mechanism to balance communication demands based on task urgency and environmental changes. Experiments in multi-agent particle environments demonstrate that IACN significantly outperforms existing methods in terms of overall performance, coordination effectiveness, and adaptability.

Prognostic and power scheduling technique for EV using optimized adaptive deep belief network

Prognostic and power scheduling technique for EV using optimized adaptive deep belief network

Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network

Text detection of container numbers is essential in logistics for the efficient tracking and management of containers. However, the wear and damage that container numbers often endure in harsh environments make detection particularly challenging. We propose a Dual-Branch Adaptive Multi-Scale Network (DAMNet) to address these issues. The DAMNet integrates Efficient Multi-Scale Attention (EMA) into the Differentiable Binarization (DBNet) model to address the challenges of improving accuracy and efficiency in the current text detection networks. We also incorporate an auxiliary information branch that processes images using the Otsu thresholding method to extract local features. This additional branch enhances the model’s ability to learn the features of container number text. Furthermore, we improve the feature pyramid structure by introducing Adaptive Spatial Feature Fusion (ASFF). This method retains rich detailed information while capturing global context, improving the model’s robustness. We also propose a dual-loss strategy. It employs BCE loss for classification tasks and Dice loss to tackle imbalanced positive and negative samples. Both losses are combined via weighting. The experimental results demonstrate that the improved DBNet model achieves a precision of 89.7%, a recall of 80.1%, and an F-score of 83.5% on the ICDAR2015 dataset. The recall increased by 3.9% compared to the original model.

Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction

Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction

Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R2 = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions.

MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network for KRAS mutation status prediction

MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network for KRAS mutation status prediction