Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, and hidden patterns. Convolutional neural networks use sparse connections at high-level sensitivity with layered connection complying indiscriminative disciplines with local spatial mapping footprints. This fact varies with architectural dependencies, insight inputs, number and types of layers and its fusion with derived signatures. This research focuses this gap by incorporating GoogLeNet, VGG-19, and ResNet-50 architectures with maximum response based Eigenvalues textured and convolutional Laplacian scaled object features with mapped colored channels to obtain the highest image retrieval rates over millions of images from versatile semantic groups and benchmarks. Time and computation efficient formulation of the presented model is a step forward in deep learning fusion and smart signature capsulation for innovative descriptor creation. Remarkable results on challenging benchmarks are presented with a thorough contextualization to provide insight CNN effects with anchor bindings. The presented method is tested on well-known datasets including ALOT (250), Corel-1000, Cifar-10, Corel-10000, Cifar-100, Oxford Buildings, FTVL Tropical Fruits, 17-Flowers, Fashion (15), Caltech-256, and reported outstanding performance. The presented work is compared with state-of-the-art methods and experimented over tiny, large, complex, overlay, texture, color, object, shape, mimicked, plain and occupied background, multiple objected foreground images, and marked significant accuracies.

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models.

High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides the decision maker with greater flexibility to exploit item utilities, such as quantity and profits, to extract remarkable and efficient database patterns. However, most prevailing empirical articles have focused on HUIs. Nevertheless, in many practical situations, low-utility itemsets (LUIs) maintain a high level of significance and usage (e.g., in security systems and the low-utility itemsets represent the security system vulnerabilities that need monitoring). Hence, this paper proposes a new association rule mining (ARM) framework named low-utility itemset mining (LUIM) that extracts LUIs. Enhancing the performance of LUIM, the researchers here propose innovative HUI generators, determining the generators based on the itemset transaction weight utility (TWU) rather than the support values used in HUG-Miner and GHUI-Miner. Moreover, this paper offers two efficient algorithms called LUG-Miner and LUIMA. The LUG-Miner extracts high and low-utility generators while LUIMA extracts low-utility itemsets using low-utility generators (LUGs). The experimental results on both dense and sparse datasets illuminated the recommended framework, and the algorithms are efficiently operational.INDEX TERMS Association rule mining, utility mining, high utility itemset mining, low-utility itemset mining.

No abstract

Allocating bandwidth guarantees to applications in the cloud has become increasingly demanding and essential as applications compete to share cloud network resources. However, cloud-computing providers offer no network bandwidth guarantees in a cloud environment, predictably preventing tenants from running their applications. Existing schemes offer tenants practical cluster abstraction solutions emulating underlying physical network resources, proving impractical; however, providing virtual network abstractions has remained an essential step in the right direction. In this paper, we consider the requirements for enabling the application-aware network with bandwidth guarantees in a Virtual Data Center (VDC). We design GANA-VDC, a network virtualization framework supporting VDC application-aware networking with bandwidth guarantees in a cloud datacenter. GANA-VDC achieves scalability using an interceptor to translate OpenFlow features to prompt fine-grained Quality of Service (QoS). Facilitating the expression of diverse network resource demands, we also propose a new Virtual Network (VN) to Physical Network (PN) mapping approach, Graph Abstraction Network Architecture (GANA), which we innovatively introduce in this paper, allowing tenants to provide applications with cloud networking environment, thereby increasing the preservation performance. Our results show GANA-VDC can provide bandwidth guarantee and achieve low time complexity, yielding higher network utility.