Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.
The study and exploration of massive multiple-input multiple-output (MMIMO) and millimeter-wave wireless access technology has been spurred by a shortage of bandwidth in the wireless communication sector. Massive MIMO, which combines antennas at the transmitter and receiver, is a key enabler technology for next-generation networks to enable exceptional spectrum and energy efficiency with simple processing techniques. For massive MIMOs, the lower band microwave or millimeter-wave band and the antenna are impeccably combined with RF transceivers. As a result, the 5G wireless communication antenna differs from traditional antennas in many ways. A new concept of the MIMO tri-band hexagonal antenna array is being introduced for next-generation cellular networks. With a total scaling dimension of 150 × 75 mm2, the structure consists of multiple hexagonal fractal antenna components at different corners of the patch. The radiating patch resonates at 2.55–2.75, 3.45–3.7, and 5.65–6.05 GHz (FR1 band) for better return loss (S11) of more than 15 dB in all three operating bands. The coplanar waveguide (CPW) feeding technique and defective ground structure in the ground plane have been employed for effective impedance matching. The deviation of the main lobe of the radiation pattern is achieved using a two-element microstrip Taylor antenna array with series feeding, which also boosts the antenna array’s bandwidth and minimizes sidelobe. The proposed antenna is designed, simulated, and tested in far-field radiating conditions and generates tri-band S-parameters with sufficient separation and high-quality double-polarized radiation. The fabrication and testing of MIMO antennas were completed, where the measurement results matched the simulation results. In addition, the 5G smartphone antenna system requires a new, lightweight phased microwave antenna (μ-wave) with wide bandwidth and a fire extender. Because of its decent performance and compact architectures, the proposed smartphone antenna array architecture is a better entrant for upcoming 5G cellular implementations.
In recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.
A Flying Ad-hoc network constitutes many sensor nodes with limited processing speed and storage capacity as they institute a minor battery-driven device with a limited quantity of energy. One of the primary roles of the sensor node is to store and transmit the collected information to the base station (BS). Thus, the life span of the network is the main criterion for the efficient design of the FANETS Network, as sensor nodes always have limited resources. In this paper, we present a methodology of an energy-efficient clustering algorithm for collecting and transmitting data based on the Optimized Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. The selection of CH is grounded on the new optimized threshold function. In contrast, LEACH is a hierarchical routing protocol that randomly selects cluster head nodes in a loop and results in an increased cluster headcount, but also causes more rapid power consumption. Thus, we have to circumvent these limitations by improving the LEACH Protocol. Our proposed algorithm diminishes the energy usage for data transmission in the routing protocol, and the network’s lifetime is enhanced as it also maximizes the residual energy of nodes. The experimental results performed on MATLAB yield better performance than the existing LEACH and Centralized Low-Energy Adaptive Clustering Hierarchy Protocol in terms of energy efficiency per unit node and the packet delivery ratio with less energy utilization. In addition, the First Node Death (FND) is also meliorated when compared to the LEACH and LEACH-C protocols.
Mobile ad hoc networks (MANETs) are self-organizing nodes in a mobile network that collaborate to form dynamic network architecture to establish connections. In MANET, data must traverse several intermediary nodes before reaching its destination. There must be security in place to prevent hostile nodes from accessing this data. Multiple methods were suggested in literature for securing routing; these techniques tackle different aspects of security. In order to enhance fault tolerance, wireless network multipath routing is typically used instead of the original single path routing. The routing protocol Genetic Algorithm with Hill climbing (GAHC) described in this article shows a hybrid GA-Hill Climbing algorithm that picks the optimal route in multipath. Prior to this in the beginning, the Improved fuzzy C-means algorithm method was built on density peak, and cluster heads (CHs) were chosen in a predicted manner, based on recent, indirect, and direct trust. The computation is based worth nodes are at the trust threshold found in addition. Even CHs take part in the alternate paths, the blend of all the many paths from these Cluster Heads that chooses the optimal route, which is based on the predicted hybrid protocol, as well as the optimum route's aggregate features such as throughput, latency, and connection. This suggested technique requires a minimum amount of energy of 0.10 m joules and a small amount of delay time of 0.004 msec, which also yields a maximum throughput of 0.85 bits per second, a maximum detection rate of 91 percent and maximum packet delivery ratio of 89percent . The suggested approach was put through the paces with the selective packet dropping attack INDEX TERMS MANET, Genetic Algorithm (GA), Cluster heads (CHs), Hill climbing (HC), Selective packet dropping attack.
In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.
Blockchain-based electronic health system growth is hindered by privacy, confidentiality, and security. By protecting against them, this research aims to develop cybersecurity measurement approaches to ensure the security and privacy of patient information using blockchain technology in healthcare. Blockchains need huge resources to store big data. This paper presents an innovative solution, namely patient-centric healthcare data management (PCHDM). It comprises the following: (i) in an on-chain health record database, hashes of health records are stored as health record chains in Hyperledger fabric, and (ii) off-chain solutions that encrypt actual health data and store it securely over the interplanetary file system (IPFS) which is the decentralized cloud storage system that ensures scalability, confidentiality, and resolves the problem of blockchain data storage. A security smart contract hosted through container technology with Byzantine Fault Tolerance consensus ensures patient privacy by verifying patient preferences before sharing health records. The Distributed Ledger technology performance is tested under hyper ledger caliper benchmarks in terms of transaction latency, resource utilization, and transaction per second. The model provides stakeholders with increased confidence in collaborating and sharing their health records.
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