This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML mode...
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Brain asymmetry,involving structural and functional differencesbetween the two hemispheres,is a major organizational principle ofthe human *** structural and functional connectivity withineach hemisphere defines the h...
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Brain asymmetry,involving structural and functional differencesbetween the two hemispheres,is a major organizational principle ofthe human *** structural and functional connectivity withineach hemisphere defines the hemispheric network or *** left-right differences of the hemispheric network providesopportunities for brain asymmetry *** review examinesthe asymmetry in the hemispheric white matter and functionalnetwork to assess health and brain *** this article,the brain asymmetry in structural and functional connectivity includingnetwork topologies of healthy individuals,involving brain cognitivesystems and the development trend,is ***,theabnormal asymmetry of the hemispheric network related to cognition changes in brain disorders,such as Alzheimer’s disease,schizophrenia,autism spectrum disorder,attention deficit hyperactivity disorder,and bipolar disorder,is *** review suggests that thehemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitivesystem and brain disorders.
Deepfake technology has rapidly evolved, posing a serious threat to the authenticity of digital media and contributing to the spread of misinformation. The manipulation of media content raises significant concerns acr...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
Deepfake technology has rapidly evolved, posing a serious threat to the authenticity of digital media and contributing to the spread of misinformation. The manipulation of media content raises significant concerns across sectors like politics, entertainment, and social media, undermining public trust in digital information. In response, this research proposes an advanced deepfake detection model that integrates Explainable Artificial Intelligence (XAI) techniques within a hybrid deep learning architecture. The proposed model combines ResNet50 and Vision Transformer (ViT) to capture spatial and contextual features effectively. While exploring additional methods such as facial landmark detection and frequency domain analysis, the final model focuses on the ResNet50-ViT combination for its superior performance to fusion model. To ensure transparency, the model employs XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), offering interpretability by revealing the key factors that influence its predictions. This dual focus on high detection accuracy and interpretability ensures that the model not only detects deepfakes effectively but also builds trust in automated decision-making processes. By leveraging cutting-edge deep learning and XAI methods, this framework offers a more reliable, transparent, and adaptable solution for preserving digital media authenticity across various applications, ultimately contributing to combating misinformation and fostering trust in digital content.
Multimodal physiological signals, such as EEG, EOG and EMG, provide rich and reliable physiological information for automated sleep staging (ASS). However, in the real world, the completeness of various modalities is ...
ISBN:
(纸本)9798331314385
Multimodal physiological signals, such as EEG, EOG and EMG, provide rich and reliable physiological information for automated sleep staging (ASS). However, in the real world, the completeness of various modalities is difficult to guarantee, which seriously affects the performance of ASS based on multimodal learning. Furthermore, the exploration of temporal context information within PSs is also a serious challenge. To this end, we propose a robust multimodal sleep staging framework named contrastive imagination modality sleep network (CIMSleepNet). Specifically, CIMSleepNet handles the issue of arbitrary modal missing through the combination of modal awareness imagination module (MAIM) and semantic & modal calibration contrastive learning (SMCCL). Among them, MAIM can capture the interaction among modalities by learning the shared representation distribution of all modalities. Meanwhile, SMCCL introduces prior information of semantics and modalities to check semantic consistency while maintaining the uniqueness of each modality. Utilizing the calibration of SMCCL, the data distribution recovered by MAIM is aligned with the real data distribution. We further design a multi-level cross-branch temporal attention mechanism, which can facilitate the mining of cross-scale temporal context representations at both the intra-epoch and inter-epoch levels. Extensive experiments on five multimodal sleep datasets demonstrate that CIMSleepNet remarkably outperforms other competitive methods under various missing modality patterns. The source code is available at: https://***/SQAIYY/CIMSleepNet.
This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storag...
This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storage, a super-capacitor bank, hydrogen storage, and fuel cell electric vehicles (FCEVs). More specifically, the paper introduces a stochastic planning framework that incorporates uncertainties in renewable energy generation, energy demand, and wholesale electricity price dynamics. An advanced metaheuristic optimization algorithm is applied to optimize the MG planning, considering the stochastic nature of renewable energy sources and dynamic component interactions. The proposed approach aims to improve the reliability and robustness of MGs under uncertain conditions. The performance of the proposed approach is evaluated through simulation studies and compared with standard deterministic methods to demonstrate its effectiveness in addressing uncertainties whilst optimizing multi-dimensional MGs. Importantly, the results indicate percentage changes of −22%, +4%, and +23% in the whole-life system cost in the best-case, most likely case, and worst-case stochastic scenarios compared to the deterministic methods.
Getting an accurate prediction of a digital currency, also known as a cryptocurrency price index, becomes a significant factor in helping investors make the right decision. Failure to predict the movement of the crypt...
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The literature on incentive-driven, market-oriented demand-side management in microgrids has focused almost entirely on minimizing the operating cost, but failed to characterize the competitive relationships in decent...
The literature on incentive-driven, market-oriented demand-side management in microgrids has focused almost entirely on minimizing the operating cost, but failed to characterize the competitive relationships in decentralized energy markets. Accordingly, this has led to producing clear evidence of their underperformance when applied in real-world settings. In response, using ideas from non-cooperative game theory to address behavioral risk factors, this paper introduces an aggregator-mediated, demand response scheduling framework in a two-layer arrangement, and integrates it into an optimal day-ahead energy management model of grid-connected multi-microgrids. The results obtained by applying the proposed model to a conceptual multi-microgrid system, have demonstrated its effectiveness in yielding the best compromise solution between demand response utilization and electricity imports. More specifically, the results indicate that the suggested model can reduce the test-case system’s daily operating cost by up to ~41% by finding a well-balanced solution with respect to the optimal decisions made by competing players in a strategic setting.
The Unified Extensible Firmware Interface (UEFI) is a linchpin of modern computing systems, governing secure system initialization and booting. This paper is urgently needed because of the surge in UEFI-related attack...
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The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-time operating system tailored f...
The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem.
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