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The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method.

Software-defined networking is a new network model proposed to solve the complexity of traditional network problems and facilitate dynamic network operation and management. The separation of the control plane from the data plane is the main idea of software-defined networks. Controllers are the operating system of software-defined networks and are responsible for managing the entire network. It is essential to locate controllers appropriately to have a balanced topology while guaranteeing low latency. In this work, a metaheuristic algorithm is used for controller placement. First, the problem is formulated, and the network is partitioned by a clustering algorithm. Then, the seagull optimization algorithm is used to determine a suitable place for the controller in each network partition dynamically. Simulations are performed on the standard network topology from the internet topology zoo dataset to evaluate the proposed method. Simulation results reveal that the proposed method performs well in case of delay and load balancing compared with the state-of-the-art optimization algorithms.

In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts.

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Energy efficiency in data centers has become a hot topic in recent years as more and larger data centers have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in server consolidation. In the past few years, many approaches to virtual machine placement have been proposed, but existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines. In this paper, we proposed a new approach for placement based on Discrete Chaotic whale optimization Algorithm. First goal of our presented algorithm is reducing the energy consumption in datacenters by decreasing the number of active physical machines. Second goal is decreasing waste of resources and management of them using optimal placement of virtual machines on physical machines in cloud data centers. By using the method, the increase in migration of virtual machines to physical machines is prevented. Finally, our proposed algorithm is compared to some algorithms in this area like FF, ACO, MGGA, GSA, and FCFS.

Security attacks are a nightmare to many computing environments such as fog computing, and these attacks should be addressed. Fog computing environments are vulnerable to various kinds of DDoS attacks, which can keep fog resources busy. Typically in such attacks, fog environments often have less available resources, which can negatively impact the scheduling of Internet of Things (IoT) submitted workflows. However, most of the existing scheduling schemes do not consider DDoS attacks' effect in the scheduling process, increasing the deadline missed workflows and offloaded tasks on the cloud. For dealing with these issues, a hybrid optimization algorithm is proposed, comprising both Particle Swarm Optimization (PSO) and Salp Swarm algorithm (SSA), to solve the workflow scheduling problem in multiple fog computing environments. Two discrete-time Markov chain models are proposed for each fog computing environment to address DDoS attacks' effects on them. Our first Markov model computes the average available network bandwidth for each fog. The second Markov model finds the average number of available virtual machines (VMs) for each fog; the models address different levels of DDoS attacks. Extensive simulations show that by predicting the effects of DDoS attacks on fog environments, the proposed approach can effectively mitigate the number of offloaded tasks on cloud data centers and can reduce the number of the deadline missed workflows.

The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.