Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.

Data is today's most powerful tool; valuable facts and information can be determined by analyzing them using appropriate techniques and algorithms. Also, the rapid increase in access to Internet technology to a large mass of people worldwide has increased the importance of analyzing data generated on the web much more than before. The preceding discussion of this research is sales forecasting in marketing, which is very important in this topic. Marketing is a tool through which people's standard of living is developed, which is done before and after the sale. This research presents a model based on a dynamic analysis system for forecasting marketing sales based on the AGA-LSTM neural network model. It is challenging to recognize emotions in natural language, even for humans, and automatic recognition makes it more complicated. This research presents a hybrid deep-learning model for accurate sentiment prediction in real-time multimodal data. In the proposed method, the work process is such that after extracting emotional data from social networks, they are pre-processed and prepared for pattern discovery. The data is evaluated in the adaptive genetic algorithm, and the pattern is discovered in the designed neural network, and this pattern is discovered after discovery. The cornerstone of sales policies is improved. The adaptive genetic algorithm was used to optimize the parameters of the LSTM model, and the model can predict the types of goods and the total volume of online retail sales. In the simulation of the proposed method, in 3000 rounds of training, an accuracy of 76 has been achieved, which is an improvement of 11% compared to the primary method.

The blockchain is a revolutionary technology transforming how assets are managed digitally and securely on a distributed network. Blockchain decentralized technology can solve distrust problems of the traditional centralized network and enhance the privacy and security of data. It provides a distinct way of storing and sharing data through blocks chained together. The blockchain is highly appraised and endorsed for its decentralized infrastructure and peer-to-peer nature. However, much research about the blockchain is shielded by Bitcoin. But blockchain could be applied to a variety of fields far beyond Bitcoin. Blockchain has shown its potential for transforming the traditional industry with its essential characteristics: decentralization, persistency, anonymity, and audibility. Undoubtedly, blockchain technology can significantly change the global business environment and lead to a paradigm shift in the functioning of the business world. However, to unlock the tremendous potential, various challenges in the adoption and viability of blockchain technology must be addressed before we can see the legal, economic, and technical viability of this technology in the operation of various business applications. In this study, the fundamental concepts of blockchain are discussed at the beginning, and the way it works and its architecture is mentioned, and since all technologies face challenges, this technology is no exception and has challenges based on the works related to the challenges It is mentioned.

Since the increase in internet attacks brings much damage, it is essential to take care of the security of network activities. networks must use different security systems, such as intrusion detection systems, to deal with attacks. This research proposes a reliable approach for intrusion detection systems based on anomaly networks. The network traffic data sets are large and unbalanced, affecting intrusion detection systems' performance. The imbalance has caused the minority class to be incorrectly identified by conventional data mining algorithms. By ignoring the example of this class, we tried to increase the overall accuracy, while the correct example of the minority class protocols is also essential. In the proposed method, network penetration detection based on the combination of multi-dimensional features and homogeneous cumulative set learning was proposed, which has three stages: the first stage, based on the characteristics of the data, several original datasets of raw data or datasets criteria are extracted. Then, the original feature datasets are combined to form multiple comprehensive feature datasets. Finally, the same basic algorithm is used to train different comprehensive feature datasets for the multi-dimensional subspace of features. An initial classifier is trained, and the predicted probabilities of all the basic classifiers are entered into a meta-module. In this research, an AdaBoost meta-algorithm has been used for unbalanced data according to a suitable design. Also, various single CNN models and multi-CNN fusion models have been proposed, implemented, and trained. This evaluation is done with the NSL-KDD dataset to solve some of the inherent problems of the KDD'99 dataset. Simulations were performed to evaluate the performance of the proposed model on the mentioned data sets. This proposed method's accuracy and detection rate obtained better results than other methods.

The establishment of fire stations is considered an essential part of the security of any city. At the time of an accident, the location of fire stations is essential for timely and quick relief. The delay in providing aid causes irreparable damage to the life and property of the city's people, and the correct location of fire stations can prevent such incidents from happening, which is necessary to achieve this goal. It is systematic and integrated based on a suitable model. Therefore, in this research, a suitable model for locating the position of firefighting forces based on fuzzy logic and mutated genetic algorithm is proposed, which has two objective functions: one for optimizing the urban coverage and the other for optimizing Building the number of fires stations. The goal is to deploy stations in such a way as to create maximum urban coverage, and on the other hand, considering the cost of deploying each station, the method seeks to reduce the number of stations. The criteria needed for the stations' location have been examined, including the distance from the existing fire station. S the distance from the areas at risk of earthquakes, the high population density, the density of wooden buildings, the proximity to the roads—the main and density of hazardous materials facilities., the data set of fire stations in Istanbul city was used, to check the results and simulation in this research. This data set contains two parts, one of which contains information about the location of the stations, which has 124 data, and the other contains related information to the areas where the fire occurred and has 107 data. In this research, five scenarios were set, the first scenario of two parameters, the second scenario of three parameters, the third, fourth, and fifth scenarios of four parameters and their influence on the choice of the parent were investigated, and the results showed that the best solution is It is obtained that both goals have the same weight in the scenarios. It happens when the number of stations reaches the desired level. In fact, by increasing the number of stations to the appropriate size, the urban coverage amount reached the desired results.