This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial ev...
详细信息
The development of multi-agent technology for assessing the availability of information at the software development initial stages is an actual task, the solution of which is the purpose of this study. The paper prese...
详细信息
Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular puls...
详细信息
Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he
The study of gaze tracking is a significant research area in computer vision. It focuses on real-world applications and the interface between humans and computers. Recently, new eye-tracking applications have boosted ...
详细信息
Local Binary Patterns (LBP) is one of many image features suitable for enhancing textures within an image. Despite its popularity, this feature as well as its variants actually still has some drawbacks: requiring the ...
详细信息
The Internet of Vehicles (IoV) enhances road safety through real-time vehicle-to-vehicle (V2V) communication of traffic messages. However, V2V wireless connectivity poses security and privacy threats, as malicious adv...
详细信息
Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in Io...
详细信息
Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.
I had the privilege and the pleasure to work closely with Stephen J. Pennycook for about twenty years, having a group of post-docs and Vanderbilt-University graduate students embedded in his electron microscopy group ...
详细信息
I had the privilege and the pleasure to work closely with Stephen J. Pennycook for about twenty years, having a group of post-docs and Vanderbilt-University graduate students embedded in his electron microscopy group at Oak Ridge National Laboratory, spending on average a day per week there. We combined atomic-resolution imaging of materials,electron-energy-loss spectroscopy, and density-functional-theory calculations to explore and elucidate diverse materials phenomena, often resolving long-standing issues. This paper is a personal perspective of that journey, highlighting a few examples to illustrate the power of combining theory and microscopy and closing with an assessment of future prospects.
Deep learning has been successfully used for tasks in the 2D image *** on 3D computer vision and deep geometry learning has also attracted *** achievements have been made regarding feature extraction and discriminatio...
详细信息
Deep learning has been successfully used for tasks in the 2D image *** on 3D computer vision and deep geometry learning has also attracted *** achievements have been made regarding feature extraction and discrimination of 3D *** recent advances in deep generative models such as generative adversarial networks,effective generation of 3D shapes has become an active research *** 2D images with a regular grid structure,3D shapes have various representations,such as voxels,point clouds,meshes,and implicit *** deep learning of 3D shapes,shape representation has to be taken into account as there is no unified representation that can cover all tasks *** such as the representativeness of geometry and topology often largely affect the quality of the generated 3D *** this survey,we comprehensively review works on deep-learning-based 3D shape generation by classifying and discussing them in terms of the underlying shape representation and the architecture of the shape *** advantages and disadvantages of each class are further *** also consider the 3D shape datasets commonly used for shape ***,we present several potential research directions that hopefully can inspire future works on this topic.
In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attribu...
详细信息
In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.
暂无评论