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Mobile ad hoc network (MANET) is one of the most widely used networks, which has attracted attentions, having features such as limited energy resources, limited bandwidth, and security weaknesses due to lack of a central infrastructure. Safe and suitable routing is one of the research aspects of MANET. In this paper, a proposed method, called M-AODV, which is a type of overhearing backup protocol, based on AODV, is presented. The simulation results of this protocol, applied by NS2 simulator, showed the improvement of packet delivery rate and reduction of overhead and delay. Moreover, to assess the security of the proposed protocol, we simulated M-AODV and AODV protocols under black hole and wormhole attacks, using no security solution. The results showed that M-AODV had been improved in terms of packet delivery ratio, and the delay had been reduced as well, but the amount of overhead had been increased.

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<span>Many software companies and teams use Agile methods as their main development approach. These methods promise higher team productivity, faster product delivery, a more flexible development process, and greater customer satisfaction. Nevertheless, a review of the literature shows that adapting to these methods, known as Agile transition, is not as easy as expected. However, several frameworks and models have been proposed to facilitate the Agile transition process. The challenging issue after the transition to agility is the behavior of companies and teams after the Agile transition and how to maintain agility in the long run. Very little research has been done on this issue, which has largely expressed concern. The present study tries to explore the hidden aspects of the transition to agility and provide a solution for Agile consolidation in newly Agile software teams. In this regard, using the grounded theory approach, the basic theory of Agile consolidation in these teams has been presented. Preliminary findings of the study indicate important factors that play an important role in Agile consolidation. Identification of challenges, facilitators, organizational culture structure, and human roles in Agile consolidation is the most important initial findings of this study.</span>

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. IoT has introduced various services and daily human life depends on its reliable and accessible operation. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routs with separate links. One of such protocols is AOMDV routing protocol. AOMDV protocol is a multi-path protocol which uses multiple different paths for sending information in order to maintain the network traffic balance, manage and control node energy, decrease latency, etc. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named GSTMPR-IoT is introduced which chooses the routs with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed GSTMPR-IoT method is compared to the EECRP and AOMDV approaches with regard to throughput, packet delivery rate, end to end delay, average residual energy, and network lifetime. The results demonstrate the superior performance of the proposed GSTMPR-IoT compared to the EECRP and AOMDV approaches.

Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

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