Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Li, Guang; Liu, Fangfang; Sharma, Ashutosh; Khalaf, Osamah Ibrahim; Alotaibi, Youseef; Alsufyani, Abdulmajeed; Alghamdi, Saleh

doi:10.1155/2021/9982305

Cited by 118 publications

(114 citation statements)

References 46 publications

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“…The questions asked were as follows: Blockchain can also be integrated with many applications to make them secure which are based on IoT (Internet of Things) for the WSN (Wireless Sensor Networks) [14,[33][34][35][36]. Also there is a possibility of using the blockchain in already develpoed system and applications for the healthcare [36][37][38] and in computational based techniques [39,40] in the future.…”

Section: Results Analysis and Testingmentioning

confidence: 99%

Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers

et al. 2021

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Conventional crop insurance systems are complex and often not economically feasible. Farmers are often reluctant to be covered for their crops due to lack of trust in insurance firms and the fear of delayed or non-payment of claims. In this paper, a blockchain based crop insurance solution is suggested. The solution suggested in this paper is an affordable, efficient, low cost crop insurance solution which will ensure many farmers are insured and benefiting from timely crop insurance. Currently the cost of administering insurance is an essential barrier to accessing this facility. With the proper use of blockchain based on ethereum this expense can be reduced dramatically. We have conducted various tests on platforms such as Google Cloud and found that the least throughput is 165 transactions. Upon analysis we have found that the time taken by the block formation is directly proportional to the timing of processing. The end-to-end average latency of the system was achieved as 31.2 s, which was quite effective for the infrastructure what we are using. Upon conducting acceptance testing, we found that the system suggested in the paper is effective and we are planning to release the application on open source platforms for future improvements.

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Section: Results Analysis and Testingmentioning

confidence: 99%

Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers

et al. 2021

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“…Firstly, the pretrained CNN models are compared with the proposed model in order to validate its flexibility and performance. The various pretrained CNN models used are AlexNet [29], GoogleNet [30], ResNet [31] and VggNet [32,33]. The comparison is drawn in terms of time intricacy and accuracy which are shown in Figure 8.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.Povzetek: Opisana je metoda ugotavljanja sladkorne bolezni iz slik očesnega ozadje na osnovi strojnega in globokega učenja.

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“…The steady-stage phase begins after the setup stage ends, identically as done in the LEACH protocol. However, in our proposed protocol, we had added a feature to preclude surplus data [23,24] from being sent to the base station. The CH combines collected data from its various parts, and a data compression algorithm is executed to commingle the data.…”

Section: Steady-state Phasementioning

confidence: 99%

“…Hence, as a result, the entire network will use less energy. The energy consumed for data transmission is determined through the cluster head involvement and estimated by equation [24][25][26][27][28] below.…”

Section: Steady-state Phasementioning

confidence: 99%

Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol

et al. 2021

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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.

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Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Cited by 118 publications

References 46 publications

Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers

Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol

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