A Novel Categorical Data Attribute Split Technique in Decision Tree Learning

A new technique is proposed for splitting categorical data during the process of decision tree learning. This technique is based on the class probability representations and manipulations of the class labels corresponding to the distinct values of categorical attributes. For each categorical attribute aggregate similarity in terms of class probabilities is computed and then based on the highest aggregated similarity measure the best attribute is selected and then the data in the current node of the decision tree is divided into the number of sub sets equal to the number of distinct values of the best categorical split attribute. Many experiments are conducted using this proposed method and the results have shown that the proposed technique is better than many other competitive methods in terms of efficiency, ease of use, understanding, and output results and it will be useful in many modern applications.

A new method for mining information of co-expression network based on multi-cancers integrated data

Background Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. Methods In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. Results We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. Conclusions The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

A Contention-Based Hop-By-Hop Bidirectional Congestion Control Algorithm for Ad-Hoc Networks

Existing hop-by-hop congestion control algorithms are mainly divided into two categories: those improving the sending rate and those suppressing the receiving rate. However, these congestion control algorithms have problems with validity and limitations. It is likely that the network will be paralyzed due to the unreasonable method of mitigating congestion. In this paper, we present a contention-based hop-by-hop bidirectional congestion control algorithm (HBCC). This algorithm uses the congestion detection method with queue length as a parameter. By detecting the queue length of the current node and the next hop node, the congestion conditions can be divided into the following four categories: 0–0, 0–1, 1–0, 1–1 (0 means no congestion, 1 means congestion). When at least one of the two nodes is congested, the HBCC algorithm adaptively adjusts the contention window of the current node, which can change the priority of the current node to access the channel. In this way, the buffer queue length of the congested node is reduced. When the congestion condition is 1–1, the hop-by-hop priority congestion control (HPCC) method proposed in this paper is used. This algorithm adaptively changes the adjustment degree of the current node competition window and improves the priority of congestion processing of the next hop node. The NS2 simulation shows that by using the HBCC algorithm, when compared with distributed coordination function (DCF) without congestion control, the proposed unidirectional congestion control algorithms hop-by-hop receiving-based congestion control (HRCC) and hop-by-hop sending-based congestion control (HSCC), and the existing congestion control algorithm congestion alleviation—MAC (CA-MAC), the average saturation throughput increased by approximately 90%, 62%, 12%, and 62%, respectively, and the buffer overflow loss ratio reduced by approximately 80%, 79%, 44%, and 79%.

Path Planning with CPD Heuristics

Compressed Path Databases (CPDs) are a leading technique for optimal pathfinding in graphs with static edge costs. In this work we investigate CPDs as admissible heuristic functions and we apply them in two distinct settings: problems where the graph is subject to dynamically changing costs, and anytime settings where deliberation time is limited. Conventional heuristics derive cost-to-go estimates by reasoning about a tentative and usually infeasible path, from the current node to the target. CPD-based heuristics derive cost-to-go estimates by computing a concrete and usually feasible path. We exploit such paths to bound the optimal solution, not just from below but also from above. We demonstrate the benefit of this approach in a range of experiments on standard gridmaps and in comparison to Landmarks, a popular alternative also developed for searching in explicit state-spaces.

Incorporating Structural Information for Better Coreference Resolution

Coreference resolution plays an important role in text understanding. In the literature, various neural approaches have been proposed and achieved considerable success. However, structural information, which has been proven useful in coreference resolution, has been largely ignored in previous neural approaches. In this paper, we focus on effectively incorporating structural information to neural coreference resolution from three aspects. Firstly, nodes in the parse trees are employed as a constraint to filter out impossible text spans (i.e., mention candidates) in reducing the computational complexity. Secondly, contextual information is encoded in the traversal node sequence instead of the word sequence to better capture hierarchical information for text span representation. Lastly, additional structural features (e.g., the path, siblings, degrees, category of the current node) are encoded to enhance the mention representation. Experimentation on the data-set of the CoNLL 2012 Shared Task shows the effectiveness of our proposed approach in incorporating structural information into neural coreference resolution.

The average weighted receiving time with weight-dependent walk on a family of double-weighted polymer networks

In this paper, a family of the double-weighted polymer networks is introduced depending on the number of copies [Formula: see text] and two weight factors [Formula: see text]. The double-weights represent the selected weights and the consumed weights, respectively. Denote by [Formula: see text] the selected weight connecting the nodes [Formula: see text] and [Formula: see text], and denote by [Formula: see text] the consumed weight connecting the nodes [Formula: see text] and [Formula: see text]. Let [Formula: see text] be related to the weight factor [Formula: see text], and let [Formula: see text] be related to the weight factors [Formula: see text]. Assuming that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the selected weight of edge linking them. The weighted time for two adjacency nodes is the consumed weight connecting the two nodes. The average weighted receiving time (AWRT) is defined on the double-weighted polymer networks. Our results show that in large network, the leading behaviors of AWRT for the double-weighted polymer networks follow distinct scalings, with the trapping efficiency associated with the network size [Formula: see text], the number of copies [Formula: see text], and two weight factors [Formula: see text]. We also found that the scalings of the AWRT with weight-dependent walk in double-weighted polymer networks is due to the use of the weight-dependent walk and the weighted time. The dominant reason is the range of each weight factor. To investigate the reason of the scalings, the AWRT for four cases are discussed.

Improved Provoking Trustworthy Routing using Warning Packet Arising Algorithm in Wireless Ad Hoc Network

In WAHN nodes are ready to broadcast data packets frequently with maximum usage of energy, unsecure and nodes resource utilization is important they are not controlled bycertificate revocation scheme. Key mixing is difficult to take much time, so sometime the routing nodes, work as well and they are changed to fake mode depending on its behavior. It affects the security of packet transmission.Nodes transmission is broken by wrong link established by intruder nodes. Proposed an Improvedprovokingtrustworthy Routing (IPTR) schemetomeasure the nodes behavior, attacker works good else bad alternatively so they are easily identified based on historical information for particular node which are present in routing path.The warning packet arising algorithm is implemented to provide warning message to next neighbor node in routing path. Current node changes its operating mode to bad state gives a warning signal, so time delay is reduced and improve network lifetime

A node influence ranking algorithm based on probability walking model

For the limitation that current node influence ranking algorithms can only be applied in a single type of network and the results are inaccurate, an algorithm based on similarity is proposed. When a node is similar to many nodes in the network, it is representative and can be treated as an influential node. Firstly, probability walking model is used to simulate the initiative visit between nodes in different types of networks. Secondly, superposed probabilistic transfer similarity is defined based on the model considering nodes’ inbound and outbound information. Finally, node ranking algorithm is set up using the new similarity measuring method. Experiments show that the algorithm can evaluate different kinds of networks with high accuracy, whether the network is directed or undirected, weighted or unweighted.

Reliable Energy-Aware Routing Protocol in Delay-Tolerant Mobile Sensor Networks

In the data transmission process of delay-tolerant mobile sensor networks, data is easily lost, and the network lifetime decreases due to energy depletion by the nodes. We propose a reliable energy-aware routing protocol, called RER. To ensure the reliability of message transmission, a hop-by-hop retransmission acknowledgement mechanism is introduced in the RER. Second, we design a metric called Reliable Energy Cost Based on Distance (RECBD) to aid RER, which is determined by analysing the distance between the current node and the relay node, the distance between the relay node and the sink node, the current residual energy of the current node, and the link quality. Finally, the message is routed based on the RECBD to improve reliability and reduce energy consumption. The simulation results show that the routing protocol can improve the energy utilization of the sensor nodes and prolong the network lifetime while guaranteeing the delivery ratio and reliability.

An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid obstacles is an urgent need. Moreover, the sensor nodes are typically powered by batteries, which are difficult to replace, restricting the network lifetime of UASNs. In this paper, an Energy-efficient and Obstacle-Avoiding Routing protocol (EOAR) is proposed not only to address the issue of marine animals acting as obstacles that interfere with communications, but also to balance the network energy according to the residual energy. In the EOAR protocol, when the current node perceives the existence of marine animals, the interference area of the animal-nodes is first calculated using the underwater acoustic channel model, and then the candidate forwarding relay set of the current node is obtained according to the constraint conditions. The optimal candidate forwarding relay is determined by a fuzzy logic-based forwarding relay selection scheme based on considering the three parameters of the candidate forwarding relay, which includes the propagation delay, the included angle between two neighbor nodes, and the residual energy. Furthermore, in order to solve the problem of energy waste caused by packet collision, we use a priority-based forwarding method to schedule the packet transmission from the candidate forwarding relay to the destination node. The proposed EOAR protocol is simulated on the Aqua-sim platform and the simulation results show that proposed protocol can increase the packet delivery ratio by 28.4% and 11.8% and can reduce the energy consumption by 53.4% and 32.7% and, respectively, comparing with the hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR).