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.

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations and dimension reductions. However, when it applied to real-world datasets, it shows shortcomings for the shortest path between all pairs of data points, which are based on the nearest neighborhood G graph via the Dijkstra algorithm, which makes it a very time-consuming step. The other critical problem is the classical Isomap has a lack of topological stability on the nearest neighborhood G graph. In this paper, we propose a novel technique called the FastIsomapVis for the above problems of the classical Isomap. The FastIsomapVis uses hierarchal divide, conquer, and combine approach through two algorithms, which are randomized division tree (KD-tree) and Dijkstra Buckets Double (DKD). The primary aim of the FastIsomapVis is to increase the efficiency and accuracy of the graph. This research paper focuses on transforming the high dimensional datasets into a low dimensional Isomap visualization. The FastIsomapVis makes it easy to construct an accurate K nearest neighborhood G graph and scale high dimensional data points into low dimensional space. Our proposed method is compared to the classical Isomap to verify its effectiveness and provide highly authentic results of the high dimensional datasets. The finding of the current study shows that our proposed method is much fastened than classical Isomap.

In recent years, millimeter-wave radar technology has been widely used for non-invasive recognition and tracking of sleep postures due to its advantages of high accuracy, contactless operation, and ability to penetrate clothing. In order to promote the development of this field and to address the lack of large-scale, high-quality sleep posture transition datasets, this paper proposes a publicly available millimeter-wave sleep posture transition dataset. The dataset contains 20 volunteers (15 males and 5 females) aged between 19 and 25 years, with heights ranging from 1.55 m to 1.80 m and weights between 45 kg and 90 kg. Each participant performed seven different body position transitionmaneuvers in a preset order, yielding a total of 1400 samples. During the experiment, participants' postural changes were captured by a millimeter-wave radar system mounted on the side of the bed. This dataset provides valuable support for the optimization of sleep posture recognition algorithms, analysis of nocturnal behavioral patterns, and health monitoring.

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.