<p class="Abstract"><span lang="EN-US">The internet of things (IoT) environment prerequisite seamless connectivity for meeting real-time application requirements; thus, required efficient resource management techniques. Heterogeneous wireless networks (HWNs) have been emphasized for providing seamless connectivity with high quality of service (QoS) performance to provision IoT applications. However, the existing resource allocation scheme suffers from interference and fails to provide a quality experience for low-priority users. As a result, induce bandwidth wastage and increase handover failure. In addressing the research issues this paper presented the resource-optimized network selection (RONS) method for HWNs. The RONS method employs better load balancing to reduce handover failure and maximizes resource utilization through dynamic slot optimization. The RONS method assures tradeoffs between high performance to high priority users and quality of experience (QoE) for low priority users. The experiment outcome shows the RONS achieves very good performance in terms of throughput, packet loss, and handover failures in comparison with existing resource selection methods.</span></p>
<p>Content-based image retrieval (CBIR) uses the content features for retrieving and searching the images in a given large database. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as shape, colour, and texture used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deep convolutional neural network (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e. learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets the oxford dataset considering mean average precision (mAP) metrics and comparative analysis shows IDD-CNN outperforms the other existing model.</p>
The image retrieval focuses on finding images that are similar from a dataset that is of a large scale against an image of a query. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as their shape, colour, and texture. used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deepconvolutional neural networks (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e., learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets of Paris and the oxford dataset considering metrics; also, image retrieval and re-ranking is carried out against the given query. Comparative analysis of various difficulty levels against the different CNN models suggests that IDD-CNN simply outperforms the existing model.
There have been several researches done in the field of image saliency but not as much as in video saliency. In order to increase precision and accuracy during compression, reduce coding complexity and time consumption along with memory allocation problems with our proposed solution. It is a modified high-definition video compression (HEVC) pixel based consistent spatiotemporal diffusion with temporal uniformity. It involves taking apart the video into groups of frames, computing colour saliency, integrate temporal fusion, pixel saliency fusion is conducted and then colour information guides the diffusion process for the spatiotemporal mapping with the help of permutation matrix. The proposed solution is tested on a publicly available extensive dataset with five global saliency valuation metrics and is compared with several other state-of-the-art saliency detection methods. The results display and overall best performance amongst all other candidates.
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