News summarization is a process of distilling the most important information from a news document in a precise way. For the advancement of Internet nowadays almost all of the Bangla newspapers have their online versions, and people of this era like to read newspaper from website using Internet. But large amount of electronic news content is a burden for human to come out with valuable information. For mitigating this pain point, this paper proposes an automatic method to summarize Bangla news document. In this proposed approach, graph based sentence scoring feature is introduced for the first time for Bangla news document summarization. After analyzing vast amount of Bangla news document 12 sentence scoring features have been introduced for calculating score of a sentence. An improved summary generation method has also been proposed which remove the redundant information from summary. The result is evaluated using a standard summary evaluation tool called ROUGE, and found proposed method outperforms all existing methods used in Bangla news summarization.
In mobile ad hoc network communication is performed usually by using only send and receive messages and every node is powered by limited energy from low capacity battery. Every send or receive message takes particular amount of energy from the node. So node's total energy level gradually decreases each time while it is sending or receiving something. In this way node will die out and packets coming from the source will be dropped since one of the routing node on the current route is no longer functioning. These packet loss events are observed and minimized in this paper. In the proposed approach, when source receives Warning Message from any routing node on the ongoing route then it will stop sending packets on the ongoing route. Critical energy level of routing node has been defined to generate a Warning Message when routing node's energy level reduces to critical energy level.
<span>Machine learning approaches are progressively successful in image based analysis such as different diseases prediction as well as level of risk assessment etc. In this paper, image based data analysis with machine learning technique were used for fertilizer treatment of maize. We address this issue as our country depend on agricultural field rather than others. Maize has a bright future. To predict fertilizer treatment of maize dataset were comprised of ground coverage region which highlights the green pixels of a maize image. For calculating green pixels from an image we used “Can Eye” tool. The achievement of machine learning approaches is highly dependent on quality and quantity of the dataset which is used for training the machine for better classification result. For this perseverance, we collected images from the maize field directly. Then processed those images and classified the data into four classes (Less Nitrogen=-N, Less Phosphorus=-P, Less Potassium=-K and NPK) to train our machine using decision tree algorithm to predict fertilizer treatment. We got 93% classification accuracy for decision tree. Finally, the outcome of this paper is the fertilizer treatment of a maize field based on the ground cover percentage, and we implemented this whole work using an android platform because of the availability of android mobile phone throughout the world.</span>
Mobile ad hoc network is a temporary wireless network consisting of mobile nodes. Frequent route breakage is a common event i Every node is equipped with battery energy. This form of energy is one of the scar promiscuous mode to observe packets coming from the nodes within a direct transmission range. At the same time critical energ routing node is used to find the low energy node on an ongoing route. QLR-APM to find alternate node of low energy node which avoids route breakage in MANET. This approach prevents route breakage and the same time packet loss and retransmission of pack proposed approach outperforms than QLR-APM.
Vehicular Ad Hoc Networks (VANETs) enable road users and public infrastructure to share information that improves the operation of roads and driver experience. However, these are vulnerable to poorly behaved authorized users. Trust management is used to address attacks from authorized users in accordance with their trust score. By removing the dissemination of trust metrics in the validation process, communication overhead and response time are lowered. In this paper, we propose a new Tamper-Proof Device (TPD) based trust management framework for controlling trust at the sender side vehicle that regulates driver behaviour. Moreover, the dissemination of feedback is only required when there is conflicting information in the VANET. If a conflict arises, the Road-Side Unit (RSU) decides, using the weighted voting system, whether the originator is to be believed, or not. The framework is evaluated against a centralized reputation approach and the results demonstrate that it outperforms the latter.
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