The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.

Sentiment analysis is also known as opinion mining which shows the people’s opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent.

As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multi-label image classification problem. An attention-based neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long short-term memory (ConvLSTM) is proposed (CNN-CAtt-ConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pre-trained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other state-of-the-art (SOTA) methods is achieved, which proves the superiority and validity of our method.

Network anomaly detection using graph-structured data is a critical task in data mining and cybersecurity, involving the identification of unusual patterns within a network by analyzing its structure as a graph. However, network data often exhibit high dimensionality and sparsity, complicating the detection of meaningful rarity anomalous patterns. Accurate modeling the distance between the nodes in spatial structure is particularly challenging. Additionally, the scarcity of labelled anomaly data for training supervised models can hinder the accuracy and effectiveness of anomaly detection methods. To address these challenges, we propose a novel method that enhances distance feature extraction by embedding graph data into hyperbolic space, which naturally captures hierarchical and relational structures in graphs. This method has been validated both mathematically and experimentally. Specifically, a gain factor derived from commonality metrics is introduced, adhering to conformal properties and preserving relative distances in space. The gain factor enhances the precision of edge weight features used for graph construction, ensuring that relative distances between points are accurately preserved in the embedding space. Data augmentation techniques are employed to address the issue of the scarcity of labelled data. Results demonstrate that optimizing edge weights yields greater improvements in anomaly detection performance compared to optimizing node attributes, achieving twice the contribution. Optimizing edge weights is more effective in capturing interaction anomalies between nodes, whereas focusing solely on node attributes may overlook subtle irregularities at the relational level. Furthermore, findings suggest that the proposed approach is suitable for complex anomaly detection environments due to its robustness and scalability.