With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of data mining is very important, this paper proposes four faster classification algorithms in comparison with each other. In this paper, A Multi-Layer perceptron (MLP) Network is trained with Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Invasive Weed Optimization (IWO) separately. The classifications are done on Wisconsin Breast Cancer (WBC) data base. At the end, to illustrate the speed and accuracy of these classifiers, they are compared with each other and two other types of Genetic algorithm classifiers (GA).

The exponential rise in wireless communication demands and allied applications have revitalized academia-industries to develop more efficient routing protocols. Wireless Sensor Network (WSN) being battery operated network, it often undergoes node death-causing pre-mature link outage, data drop and retransmission causing delay and energy exhaustion. Furthermore, the presence of a malicious node to impacts network performance adversely. In this paper, a highly robust and efficient evolutionary computing assisted WSN routing protocol is developed for QoS and energy-efficiency. Our proposed routing protocol encompasses two key functions Network Condition Aware Node Profiling and Malicious Node Detection (NCAMND) exploits or mines the dynamic node/network parameters to identify malicious node, and evolutionary computing assisted Dual-Disjoint Forwarding Path (EC-DDFP) model learns over node/network connectivity and availability information to obtain a dual-disjoint path with no-shared components to ensure QoS centric and energy-efficient routing. Simulation results affirm that the proposed routing protocol achieves higher throughput, low energy consumption, and low delay that confirm its suitability for real-time WSN systems.

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

Fog computing emerged to meet to the needs of modern IoT applications, such as low latency, high security, etc. To this end, it brings the network resources closer to the end user. The properties of fog computing, such as heterogeneity, distribution, and resource limitations, have challenged application deployment in this environment. Smart service placement means deploying services of the IoT applications on fog nodes in a way that their service quality requirements are met and fog resources are used effectively. This paper proposes an efficient application deployment method in fog computing using communities. In contrast to previous research, the proposed method uses more factors than topological features to distribute network capacity more evenly between communities. This results in efficient use of network resources and better fulfillment of application requirements. In addition, according to our argument, using multiple criteria to prioritize applications will lead to better deployment and more effective use of resources. For this purpose, we use the number of application requests besides the deadline factor for application prioritization. Extensive simulation results showed that the proposed method significantly outperforms the state-of-the-art methods in terms of meeting deadlines, decreasing delays, increasing resource utilization, and availability by about 17, 33, 7, and 11 percent, respectively.

Soft computing models based on intelligent fuzzy systems have the capability of managing uncertainty in the image based practices of disease. Analysis of the breast tumors and their classification is critical for early diagnosis of breast cancer as a common cancer with a high mortality rate between women all around the world. Soft computing models based on fuzzy and evolutionary algorithms play an important role in advances obtained in computer aided detection (CAD) systems. Combination of the evolutionary nature of swarm intelligence algorithms in optimization along with the potential of fuzzy models to cope with uncertainty and complex environments. In this research, a fuzzy inference model has been proposed for managing uncertainty in input data. The main uncertainty issues for classification of the breast tumors were modeled through the linguistic terms, fuzzy variables and fuzzy reasoning processes in the fuzzy inference model. Fuzzy linguist terms and rule sets are valuable to have an intelligent model with the ability to interact with the clinicians. Furthermore, hybrid fuzzy-evolutionary models have been proposed for tuning fuzzy membership functions for diagnosis of malignant and benign breast tumors. The hybrid proposed evolutionary methods are: 1) Fuzzy-Genetic, 2) Fuzzy-Particle swarm intelligence, and 3) Fuzzy-biogeography models. evolutionary nature inspired combination with the fuzzy inference model (FIM) improves the proficiency of the FIM by adaption to the environment through the tuning process using training and testing datasets. To achieve this, the Genetic Algorithm was applied as a base evolutionary method. Then, the potential of the Particle Swart intelligence algorithm in using local and global experiences of the solutions in the search space. Also, bio-geographical aspects of species in finding an optimum solution lands with the high suitability habitat index has been concentrated in optimization process of the FIM. evolutionary algorithms perform tuning of the fuzzy membership functions to improve the accuracy of the fuzzy inference model while simplicity and interpretability of the FIM was kept. For performance evaluation, an ROC curve analysis was conducted which is a robust and reliable technique that represents the trades of between classification model benefits and costs. Also, for validation purpose, a 10-fold cross-validation technique was performed for partitioning the dataset into training and testing sets in the evolutionary optimization algorithms. The performance of the proposed methods were evaluated using a dataset including 295 images and extracted features from mammographic image analysis society (MIAS) dataset. The results reveal that the hybrid Fuzzy-biogeography model outperforms the other evolutionary models with an accuracy and area under the ROC curve (AUC) of 95. 25%, and 91. 43%, respectively. Performance comparison of the hybrid evolutionary models in this study with the related methods for classification of the breast tumors on the MIAS dataset reveals that the fuzzy-biogeography model outperforms the other methods in terms of trades-off between accuracy and interpretability with an area under the ROC curve of 95. 25% with four extracted features. The Fuzzy-GA and Fuzzy-Swarm Intelligence models are competitive with the best results of counterpart methods with an accuracy of 93. 9% and 94. 58% in terms of the AUC, respectively. The proposed fuzzy-evolutionary models in this study are promising for diagnosis of the breast tumors in early stages of the disease and providing suitable treatment.