Ensemble of Deep Capsule Neural Networks: An Application to Pneumonia Prediction

Pneumonia is the primary cause of death in children under the age of 5 years. Faster and more accurate laboratory testing aids in the prescription of appropriate treatment for children suspected of having pneumonia, lowering mortality. In this work, we implement a deep neural network model to efficiently evaluate pediatric pneumonia from chest radio graph images. Our network uses a combination of convolutional and capsule layers to capture abstract details as well as low level hidden features from the the radio graphic images, allowing the model to generate more generic predictions. Furthermore, we combine several capsule networks by stacking them together and connected them with dense layers. The joint model is trained as a single model using joint loss and the weights of the capsule layers are updated using the dynamic routing algorithm. The proposed model is evaluated using benchmark pneumonia dataset\cite{kermany2018identifying}, and the outcomes of our experimental studies indicate that the capsules employed in the network enhance the learning of disease level features that are essential in diagnosing pneumonia. According to our comparison studies, the proposed model with Convolution base from InceptionV3 attached with Capsule layers at the end surpasses several existing models by achieving an accuracy of 94.84\%. The proposed model is superior in terms of various performance measures such as accuracy and recall, and is well suited to real-time pediatric pneumonia diagnosis, substituting manual chest radiography examination.

Clustered Wireless Sensor Network Assisted the Design of Intelligent Art System

Based on the principle of cluster wireless sensor network, this article introduces typical routing protocols in wireless sensors, and wireless sensor network protocol in detail analyzes their advantages and disadvantages and addresses their shortcomings. First, in the clustering network, a uniform clustering protocol with multiple hops in the circular network is proposed. The circular network is divided into rings of equal width, and clusters of equal size are set on different rings. Secondly, the ordinary nodes on each layer of the ring send the collected data to the auxiliary intelligent nodes in the cluster in a single-hop manner, and the auxiliary intelligent nodes located on the outer ring transfer the data to the auxiliary intelligent nodes located on the adjacent inner ring. Finally, on the basis of studying the clustering network protocol, this paper proposes a new clustering routing algorithm, a multihop adaptive clustering routing algorithm. The simulation results show that the algorithm can effectively extend the life of the network, save network energy consumption, and achieve network load balance. At the same time, the initial energy of the auxiliary intelligent node is set according to the energy consumption of the ordinary node and the relative distance between the auxiliary intelligent node and the base station on each layer of the ring. The theoretical and simulation results prove that, compared with the clustered network and auxiliary intelligent nodes, the clustered network can extend the life of the network.

Advancement in Physical Education Teaching Using Improved Energy Efficient Scalable Routing Algorithm-Based Wireless Network

Physical education (PE) is a crucial topic in higher coaching that individually points motor abilities in health-enhancing activities. Conventional PE in institutions struggles to pique graduates’ attentiveness in sports, proceeding in low task involvements rates, and incapacity to exercise the body. Innovative teaching concepts and methodologies, coaching techniques and procedures, and coaching assessment techniques in physical education are all accompanied to developing the physical education study hall climate and successfully boosting physical education efficacy. Each element of regular living, especially education, is being influenced by wireless internet innovations. We will provide extra help to students by predicting academic endurance or dropout. We can improve the wireless platform’s potential utility in sports applications and change the character of PE, including visualization and repetition by incorporating it into PE teaching. Based on the concept of wireless network technology, this paper proposes an Improved Energy Efficient Scalable Routing Algorithm (IEESRA) for physical education advancement. Initially, the physical education dataset is preprocessed using normalization. The aspects are removed using the scale-invariant feature transform (SIFT) method. The data is transferred using a wireless network using Improved Energy Efficient Scalable Routing Algorithm (IEESRA). The classification is done using random forest (RF) classifier. The results of the analysis reveal that wireless network-based PE may increase graduates’ strength, speed, and qualities providing a more important reference and reference for enhancing the success of PE. The proposed strategy has the potential to enhance actual attention to PE teaching to 90% with raising students’ engagement to 70%.

Plane-separated routing in ad-hoc networks

Control and user (data) plane separation (CUPS) is a concept applied in various networking areas to scale network resources independently, increase the quality of service, and facilitate the autonomy of networks. In this study, we leverage this concept to design a plane-separated routing algorithm, CUPS-based hierarchical routing algorithm (CHRA), as an energy-efficient and low-latency end-to-end communication scheme for clustered ad-hoc networks. In CHRA, while cluster heads constitute the control plane to conduct network discovery and routing, ordinary nodes residing in the user plane forward packets according to the routing decisions taken by the control plane. Exploiting the CUPS, we avoid exhausting cluster heads by offloading packet-forwarding to ordinary nodes and improve the quality of service by utilizing alternative paths other than the backbone of cluster heads. Our simulation results show that CHRA offers a better quality of service in terms of end-to-end latency and data-to-all ratio, and promotes fairness in energy-consumption in both stationary and mobile scenarios.

An Efficient Probe-Based Routing for Content-Centric Networking

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

Research on dynamic routing algorithm based on gaussian mixture model

In this paper, based on the method of environmental sound detection, a neural network model based on capsule network and Gaussian mixture model is proposed. The model proposed in this paper mainly aims at the disadvantages of dynamic routing algorithm in the capsule network, and proposes a dynamic routing algorithm based on Gaussian mixture model. The improved dynamic routing algorithm assumes that the characteristics of the data conform to the multi-dimensional Gaussian distribution, so the model can learn the distribution of data features by building distribution functions of different classes. The information entropy is used as the activation value of the salient degree of the feature. Through experiments, the accuracy of the proposed algorithm on Urbansound8K data set is more than 92%, which is 4.8% higher than the original algorithm.