PCO-IB: Churn Analysis P2P Networks Using A Peer Co-Operative Intensive Based Schema

The Peer-to-Peer networks used technology of distributed computing.  The P2P network is essential for network communication. P2P networks are utilized in many applications due to these benefits. For example, record sharing, broadcast communications, and media streaming. There are a lot of nodes connected to the P2P network. Peers of network frequently join and leave the network at the same time. In the P2P network, this kind of paradigm is called churn. Numerous new examination works uncovered that stir is the primary issue looked by the present P2P organization. Content availability, data accuracy, and overhead were significantly reduced by the churn process. An Incentive-Based (IB) schema was proposed in this paper in order to circumvent the limitations of the P2P network for multimedia transmission. The IB schema that has been proposed encourages fair communication and cooperation among the nodes. Multimedia transmission efficiency in real-time P2P networks is maximized by the IB schema. In this paper, IB outline for the most part centered around the upgrade of the P2P organizations. The proposed construction is carried out utilizing Organization Test system. In P2P networks, the proposed IB schema improved multimedia transmission performance

Study of the Topology Mismatch Problem in Peer-to-Peer Networks

The advantages of peer-to-peer (P2P) technology are innumerable when compared to other systems like Distributed Messaging System, Client-Server model, Cloud based systems. The vital advantages are not limited to high scalability and low cost. On the other hand the p2p system suffers from a bottle-neck problem caused by topology mismatch. Topology mismatch occurs in an unstructured peer-to-peer (P2P) network when the peers participating in the communication choose their neighbors in random fashion, such that the resultant P2P network mismatches its underlying physical network, resulting in a lengthy communication between the peers and redundant network traffics generated in the underlying network[1] However, most P2P system performance suffers from the mismatch between the overlays topology and the underlying physical network topology, causing a large volume of redundant traffic in the Internet slowing the performance. This paper surveys the P2P topology mismatch problems and the solutions adapted for different applications

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree's classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method

Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification

Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet)