Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number o...
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Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging *** paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency ***,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief *** datasets for light field SOD are also ***,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD *** to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and *** supplemental data make a universal benchmark ***,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection *** provide nine observations on challenges and future directions,and outline several open *** the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://***/kerenfu/LFSOD-Survey.
Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online indust...
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In the future, electric vehicles, including automobiles, motorcycles, and public transport, are expected to play a significant role in daily commutes. However, India's current infrastructure lacks a wellestablishe...
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First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimiza...
The cloud is a vast network of interconnected servers and other devices that provide computing, communication, and storage services. The cloud market is dynamic, with ever-changing user needs and resource demands. Res...
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The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital in...
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ISBN:
(纸本)9798331518097
The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the
Chronic kidney disease (CKD) is an intensifying and immutable condition in which the kidneys slowly reduces is actual function. CKD can be effected and induced because of various aspects like high cholesterol, blood p...
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Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure lear...
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The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affec...
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The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning capabilities by combining symbolic reasoning with connectionist learning. We su...
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